Detailed Office Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Arguments
Applicant’s arguments with respect to claim 1, 3-11, and 13-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
35 U.S.C. 112(a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
Claims 1 and 3-10
Independent claim 1 and claims 3-10, as dependent on claim 1, are rejected under 35 U.S.C. 112(a) because the specification, while being enabling for “a method comprising: obtaining a quantum neural network having an ordered sequence of network layers, each network layer having a set of trainable parameters,” does not reasonably provide enablement for “a distinct set of quantum nodes physically arranged along two intertwined helices.” The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the invention commensurate in scope with these claims. As delineated by this Office actions Wands factor analyses, the specification’s block diagrams, and the text describing the block diagrams, do not reasonably provide enablement for methods directed at:
“assigning each network layer of the ordered sequence of network layers to a distinct set of quantum nodes physically arranged along two intertwined helices extending along a longitudinal axis of a quantum computing architecture such that adjacent network layers that are consecutive in the ordered sequence are assigned to sets of quantum nodes on different ones of the two intertwined helices and are quantum-mechanically coupled via inter-helix quantum couplers.”
“mapping the trainable parameters of each network layer to quantum connections implemented by the inter-helix quantum couplers between the set of quantum nodes assigned to the network layer and the set of quantum nodes assigned to an adjacent network layer;” and
“performing one or more quantum computations including executing distributed quantum gate operations across the sets of quantum nodes using the quantum computing architecture, the distributed quantum gate operations including local quantum operations applied at the assigned sets of quantum nodes and inter-layer coupling operations applied through the inter-helix quantum couplers programmed according to the mapped trainable parameters, wherein adjacency and inter- layer communication between quantum nodes are determined based on angular proximity of quantum nodes along the two intertwined helices and by quantum-mechanical coupling between quantum nodes positioned at corresponding or near-corresponding angular positions on different ones of the two intertwined helices.”
as recited in claim 1. See MPEP 2165.06(a) (citing In re Ghiron, 442 F.2d 985, 169 USPQ 723 (CCPA 1971), “The specification did not particularly identify each of the elements represented by the blocks or the relationship therebetween, nor did it specify particular apparatus intended to carry out each function. The Board further questioned whether the selection and assembly of the required components could be carried out routinely by persons of ordinary skill in the art.”). See MPEP 2165.06(a) (citing In re Scarbrough, 500 F.2d 560, 182 USPQ 298 (CCPA 1974), “The court concluded that there was not an enabling disclosure because the specification did not describe how ‘complex elements known to perform broadly recited functions in different systems would be adaptable for use in Appellant’s particular system with only a reasonable amount of experimentation’ and that ‘an unreasonable amount of work would be required to arrive at the detailed relationships appellant says that he has solved.’”).
Instant Application, Fig. 1A – Block Representation of “an example quantum computing system” comprising “116 Quantum Processing Unit(s)” and “126 Entanglement, Cross-Talk Management Module”
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Instant Application, Fig. 1A (cropped) – Block Representation of “an example” of a “dual-helix quantum architecture” comprising “102 [helix 102A intertwined with helix 120B],” “126 Entanglement, Cross-Talk Management Module,” “124 Waveguides and Dynamic Couplers,” “112 Photon System,” and “104 Multidimensional Modulation Controllers”
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Instant Application, Fig. 1B – Block Representation of “an example implementation” …of “116 Quantum Processing Unit” comprising “142 Single-Qubit Operations,” “144 Multi-Qubit Operations,” and“146 Parallel Processing Dual-Helix”
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Instant Application, Fig. 2. – Block and Stick Representation of “an example” of a “dual-helix quantum architecture” comprising “A-Chain,” “B-Chain,” “A-helix chain,” “B-helix chain,” “A# quantum nodes,” and “B# quantum nodes”
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Instant Application, Fig. 3.– Block and Stick Flattened Representation of “an example” of a “dual-helix quantum architecture” comprising “A-helix chain,” “B-helix chain,” “A# quantum nodes,” “B# quantum nodes,” “301 links between quantum nodes,” and “302 links between quantum nodes”
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Instant Application, Fig. 4.– Block Representation of “an example process flow for quantum computing” comprising “402 initialization/design phase” (“state preparation of quantum nodes”) See Instant Application’s Specification, paragraph [0081]
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Instant Application, Figs. 5-9 – Block Representations of “example methods of quantum computing” comprising “quantum nodes”
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Wands factor analyses of Specification’s Block Diagrams and Related Text
(A) Wands Factor - The breadth of the claims.
Reading the claims in light of the specification’s block diagrams and related text, the examiner determined that claims are directed at methods of performing quantum computations by executing distributed quantum gate operations across the sets of quantum nodes using the quantum computing architecture. See Instant Application, Fig. 1A and related text (Block Representation of “an example quantum computing system” comprising “116 Quantum Processing Unit(s)” and “126 Entanglement, Cross-Talk Management Module”). All embodiments would necessarily comprise at least “a distinct set of quantum nodes physically arranged along two intertwined helices extending along a longitudinal axis of a quantum computing architecture.” Instant Application, Fig. 1A (cropped)and related text ( Block Representation of “an example” of a “dual-helix quantum architecture” comprising “102 [helix 102A intertwined with helix 120B],” “126 Entanglement, Cross-Talk Management Module,” “124 Waveguides and Dynamic Couplers,” “112 Photon System,” and “104 Multidimensional Modulation Controllers”).
(B) Wands Factor - The nature of the invention.
The claims are directed at multi-disciplinary photonic processes at the nano- and meta-scales. See Instant Application, Fig. 1A (cropped), (Block Representation of “an example” of a “dual-helix quantum architecture” comprising “102 [helix 102A intertwined with helix 120B],” “126 Entanglement, Cross-Talk Management Module,” “124 Waveguides and Dynamic Couplers,” “112 Photon System,” and “104 Multidimensional Modulation Controllers), Instant Application, Fig. 2. (Block and Stick Representation of “an example” of a “dual-helix quantum architecture” comprising “A-Chain” and “B-Chain”), and Instant Application, Fig. 3. (Block and Stick Flattened Representation of “an example” of a “dual-helix quantum architecture” comprising “A-helix chain,” “B-helix chain,” “A# quantum nodes,” “B# quantum nodes,” “301 links between quantum nodes,” and “302 links between quantum nodes”).
The claims are directed at abstract theoretical, mathematical, and computational processes. Instant Application, Figs. 5-9 (Block Representations of “example methods of quantum computing” comprising “quantum nodes”).
The claims are directed at hardware configurations and systems implementing computational processes enabled by photonic processes. Instant Application, Fig. 1B (Block Representation of “an example implementation” …of “116 Quantum Processing Unit” comprising “142 Single-Qubit Operations,” “144 Multi-Qubit Operations,” and“146 Parallel Processing Dual-Helix”).
(C) Wands Factor – The state of the prior art.
The examiner’s prior art search identified significant bodies of literature pertinent to key aspects of making and using the claimed “distinct set of quantum nodes physically arranged along two intertwined helices.”
Regarding neural networks, for example, activation energies and quantum photonics established high, the prior art disclosed theoretical and experimental investigations spanning multiple disciplines. See Gupta et al., A Multiple Controlled Toffoli Driven Adaptive Quantum Neural Network Model for Dynamic Workload Prediction in Cloud Environments," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 12, pp. 7574-7588, Dec. 2024, doi: 10.1109/TPAMI.2024.3402061, abstract (“The key challenges in cloud computing encompass dynamic resource scaling, load balancing, and power consumption. Accurate workload prediction is identified as a crucial strategy to address these challenges. Despite numerous methods proposed to tackle this issue, existing approaches fall short of capturing the high-variance nature of volatile and dynamic cloud workloads. Consequently, this paper introduces a novel model aimed at addressing this limitation. This paper presents a novel Multiple Controlled Toffoli-driven Adaptive Quantum Neural Network (MCT-AQNN) model to establish an empirical solution to complex, elastic as well as challenging workload prediction problems by optimizing the exploration, adaption, and exploitation proficiencies through quantum learning. The computational adaptability of quantum computing is ingrained with machine learning algorithms to derive more precise correlations from dynamic and complex workloads. The furnished input data point and hatched neural weights are refitted in the form of qubits while the controlling effects of Multiple Controlled Toffoli (MCT) gates are operated at the hidden and output layers of Quantum Neural Network (QNN) for enhancing learning capabilities. Complimentarily, a Uniformly Adaptive Quantum Machine Learning (UAQL) algorithm has evolved to functionally and effectually train the QNN. The extensive experiments are conducted and the comparisons are performed with state-of-the-art methods using four real-world benchmark datasets. Experimental results evince that MCT-AQNN has up to 32%–96% higher accuracy than the existing approaches.”). See also Zuo et al., (2019). All-optical neural network with nonlinear activation functions. Optica.Vol. 6, No. 9 September 2019; Zuo et al., Optical neural network quantum state tomography, Advanced Photonics 4(2), 026004 (24 Mar 2022); Williamson et al., Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks, arXiv:1903.04579v2 [eess.SP] 23 Jul 2019; Maronese et al., Quantum activation functions for quantum neural networks. Quantum Inf Process 21, 128 (2022). https://doi.org/10.1007/s11128-022-03466-0 (Year: 2022); Mangini et al., Quantum computing models for artificial neural networks, 2021 EPL 134 10002DOI 10.1209/0295-5075/134/10002; Gupta et al., A Multiple Controlled Toffoli Driven Adaptive Quantum Neural Network Model for Dynamic Workload Prediction in Cloud Environments," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 12, pp. 7574-7588, Dec. 2024, doi: 10.1109/TPAMI.2024.3402061; Fu et al., Optical neural networks: progress and challenges. Light Sci Appl 13, 263 (2024). https://doi.org/10.1038/s41377-024-01590-3; Fang et al. Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding. Light Sci Appl 13, 49 (2024). https://doi.org/10.1038/s41377-024-01386-5; Ewaniuk et al., (2022). Imperfect Quantum Photonic Neural Networks. Advanced Quantum Technologies, 6.
Regarding quantum computing and photonic gates, the prior art disclosed theoretical and experimental investigations spanning multiple disciplines. See Zhou et al., (2021). Chiral single-photon switch-assisted quantum logic gate with a nitrogen-vacancy center in a hybrid system. Photonics Research. 9. 405-415. 10.1364/PRJ.405246, abstract. (“We propose what we believe is a novel proposal for realizing a quantum C-NOT logic gate, through fabricating an interesting hybrid device with a chiral photon-pulse switch, a single nitrogen-vacancy (NV) center, and an optical microcavity. Three major different practical routes on realizing a chiral photon emitter are discussed, which can implement a chiral control unit via the nonreciprocal emitter–photon interactions, so-called “propagation-direction- dependent” emission. With the assistance of dichromatic microwave driving fields, we carry out the relevant C-NOT operations by engineering the interactions on a single NV spin in a cavity. We note that this logic gate is robust against practical noise and experimental imperfection, and this attempt may evoke wide and fruitful applications in quantum information processing.). See also, Aroche et al., DNA as a perfect quantum computer based on the quantum physics principles. Sci Rep 14, 11636 (2024). https://doi.org/10.1038/s41598-024-62539-5; Luo et al. Hybrid Toffoli gate on photons and quantum spins. Sci Rep. 2015 Nov 16;5:16716. doi: 10.1038/srep16716. PMID: 26568078; PMCID: PMC4644947.; Marengo et al., Double helix quantum computer, in Optical Fiber Communication Conference and International Conference on Quantum Information, 2001 OSA Technical Digest Series (Optica Publishing Group, 2001), paper PB2.; Peng et al., Universal Linear-Optical Logic Gate with Maximal Intensity Contrast Ratios, ACS Photonics 2018, 5, 1137−1143; Pooley et al., Controlled-NOT gate operating with single photons. Appl. Phys. Lett. 21 May 2012; 100 (21): 211103. https://doi.org/10.1063/1.4719077; Qian et al., A simple DNA gate motif for synthesizing large-scale circuits. J R Soc Interface 7 September 2011; 8 (62): 1281–1297. https://doi.org/10.1098/rsif.2010.0729 (Year: 2011); Zhang et al., Implementing Logic Gates by DNA Crystal Engineering, Adv. Mater. 2023, 2302345.
Regarding nano-scale active configurations, including dots and centers disposed near edges and helices, the prior art disclosed theoretical and experimental investigations spanning multiple disciplines. See Peter et al., Chirality dependent photon transport and helical superradiance, Phys. Rev. Research 6, 023200 – Published 22 May, 2024, abstract (“Chirality, or handedness, is a geometrical property denoting a lack of mirror symmetry. Chirality is ubiquitous in nature and is associated with the nonreciprocal interactions observed in complex systems ranging from biomolecules to topological materials. Here, we demonstrate that chiral arrangements of dipole-coupled atoms or molecules can facilitate the helicity-dependent superradiant emission of light. We show that the collective modes of these systems experience an emergent spin-orbit coupling that leads to chirality-dependent photon transport and nontrivial topological properties. These phenomena are fully described within the electric dipole approximation, resulting in very strong optical responses. Our results demonstrate an intimate connection between chirality, superradiance, and photon helicity and provide a comprehensive framework for studying electron transport dynamics in chiral molecules using cold atom quantum simulators.”). See also Aroche et al., DNA as a perfect quantum computer based on the quantum physics principles. Sci Rep 14, 11636 (2024). https://doi.org/10.1038/s41598-024-62539-5; Guo et al., Polarization entanglement enabled by orthogonally stacked van der Waals NbOCl2 crystals. Nat Commun 15, 10461 (2024). https://doi.org/10.1038/s41467-024-54876-w; Kim et al., Chiral 3D structures through multi-dimensional transfer printing of multilayer quantum dot patterns. Nat Commun 15, 6996 (2024). https://doi.org/10.1038/s41467-024-51179-y; Likhov et al., OAM-mode coupling by segmented helical-ring-core waveguides inscribed with a femtosecond laser beam, Opt. Lett. 49, 1217-1220 (2024); Marengo et al., Double helix quantum computer, in Optical Fiber Communication Conference and International Conference on Quantum Information, 2001 OSA Technical Digest Series (Optica Publishing Group, 2001), paper PB2.; Narayan et al., Discovery of Double Helix and Impact on Nanoscale to Mesoscale; Crystalline Structures, ACS Omega 2022, 7, 25853−25859; Parappurath et al., Direct observation of topological edge states in silicon photonic crystals: Spin, dispersion, and chiral routing. Sci. Adv.6,eaaw4137(2020). DOI:10.1126/sciadv.aaw4137; Peter et al., Chirality-induced emergent spin-orbit coupling in topological atomic lattices, arXiv:2311.09303. Sahoo et al., A Third Angular Momentum of Photons. Symmetry 2023, 15, 158. https://doi.org/10.3390/ sym1501015; Shi et al., Double-helix singularity and vortex–antivortex annihilation in space-time helical pulses, Nanophotonics 2025; 14(6): 741–747; Suárez-Forero et al., Chiral quantum optics: recent developments, and future directions, arXiv:2411.06495,10 Nov 2024; Vavulin et al., Quantum walks of photon pairs in twisted waveguide arrays, 2015 J. Phys.: Conf. Ser. 643 012050DOI 10.1088/1742-6596/643/1/012050; Yan et al, Chirality-dependent electromagnetically induced transparency based on a double semi-periodic helix metastructure, Opt. Lett. 43, 3722-3725 (2018); Zannotti et al., Chiral Light in Helically Twisted Photonic Lattices, Advanced Optical Materials 2017, 5, 1600629).
Regarding light-matter coupling, including structural influences, the prior art disclosed theoretical and experimental investigations spanning multiple disciplines. See Peter et al., Chirality-induced emergent spin-orbit coupling in topological atomic lattices, arXiv:2311.09303, abstract (“Spin-orbit coupling is of fundamental interest in both quantum optical and condensed matter systems alike. In this work, we show that optically induced electronic excitations in lattices of V-type atoms exhibit an emergent spin-orbit coupling when the geometry is chiral. This spin-orbit coupling arises naturally from the electric dipole interaction between the atomic sites and leads to a nontrivial topology for the lattice band structure. Using a general quantum optical model, we determine analytically the conditions that give rise to spin-orbit coupling and characterize the behavior under various symmetry transformations. We demonstrate that chirality-induced spin-orbit coupling can result from either the chirality of the underlying lattice geometry or the combination of an achiral lattice with a suitably chosen external quantization axis. We then discuss how these results are influenced by dissipation, which breaks time-reversal symmetry and illuminates the distinction between true and false chirality. Our results demonstrate that chiral atom arrays are a robust platform for realizing spin-orbit-coupled topological states of matter.”). See also, Sahoo et al., A Third Angular Momentum of Photons. Symmetry 2023, 15, 158. https://doi.org/10.3390/ sym1501015; Shi et al., Asymmetric chiral coupling in a topological resonator. Appl. Phys. Lett. 8 May 2023; 122 (19): 191104. https://doi.org/10.1063/5.0149671; Suárez-Forero et al., Chiral quantum optics: recent developments, and future directions, arXiv:2411.06495,10 Nov 2024; Tang et al., Highly sensitive and actively tunable chiral metasurface enabled by bound states in the continuum. Optics express 32 22 (2024): 40007-40019.; Xu et al., Topological Landau–Zener nanophotonic circuits, Advanced Photonics 5(3), 036005 (1 Jun 2023) https://doi.org/10.1117/1.AP.5.3.036005; Yan et al, Chirality-dependent electromagnetically induced transparency based on a double semi-periodic helix metastructure, Opt. Lett. 43, 3722-3725 (2018); Yang et al., (2024). Inverse-designed integrated all-optical nonlinear activators for optical computing. Optics Express. 32. 34001-34014. 10.1364/OE.531679. ; Qian et al., A simple DNA gate motif for synthesizing large-scale circuits. J R Soc Interface 7 September 2011; 8 (62): 1281–1297. https://doi.org/10.1098/rsif.2010.0729 (Year: 2011); Zhang et al., Implementing Logic Gates by DNA Crystal Engineering, Adv. Mater. 2023, 2302345. ‘
(D) Wands Factor - The level of one of ordinary skill.
A granular assessment of the above-cited prior art evinces the high of individual persons practicing one or two related disciplines covered by claimed invention’s multiple disciplines (for example, mathematics, physics, physical/organic/inorganic chemistry, nano- and room-scale materials science, electrical engineering, and computer science). However, by comparing the above-cited prior art to how the block diagrams integrate hardware, software, and sets of quantum nodes, it is readily apparent it is highly unlikely that a person would demonstrate high levels skill in anything more than a small subset of the large set of diverse skills required to make and use the invention commensurate in scope with these claims.
(E) Wands Factor - The level of predictability in the art.
While the above cited prior art may advance the predictability of some of the individual technologies that define the claimed invention’s large and diverse assemblage of multiple technologies, the prior art does not disclose any multidisciplinary efforts sufficient to assess the predictability of making and using a distinct set of quantum nodes physically arranged along two intertwined helices.
(F) Wands Factor - The amount of direction provided by the inventor.
Regarding the helices along which indistinct sets of quantum nodes are arranged, the inventor discloses the following.
The inventor discloses that helices’ material composition(s) may include “high-transparency quartz, fused silica, silicon nitride (SiN), lithium niobate (LiNbO.sub.3), or other suitable material(s).” Written Specification, paragraph [0026].
The inventor does not describe the helices’ structural feature beyond speculating that , “modulating the frequency, phase, and/or amplitude of each helix chain …might involve modifying structural properties of the dual-helix structure.” Written Specification, paragraph [0029].
The inventor discloses that the dual-helix structure may process quantum information. Specification, paragraph [0031].
The inventor discloses that the helix chains may operate in parallel while simultaneously processing different quantum tasks. Specification, paragraph [0027]
Regarding the quantum nodes that are arranged along the helices, the inventor discloses the following.
The inventor does not describe the quantum nodes’ material composition(s).
The inventor does not describe the quantum nodes’ structure(s).
The inventor discloses that sets of quantum nodes are, “physically arranged along two intertwined helices extending along a longitudinal axis of a quantum computing architecture such that adjacent network layers of the plurality of network layers are assigned to sets of quantum nodes on different ones of the two intertwined helices.” Specification, abstract.
The inventor discloses that quantum nodes may be “on one helix, along the helix, and along a helix and between helices. Specification, paragraph [0023].
The inventor discloses that positions of the members of a set of quantum nodes are characterized as angles of rotation about the longitudinal axis of the intertwined helices. Specification, paragraphs [0022] and [0050].
The inventor discloses that a rung is an inter-helix connection between a first node physically arranged along a first helix of a pair of intertwined helices and a second node physically arranged along a second helix of the pair of intertwined helices. Specification, paragraph [0023].
The inventor discloses that: nodes within the dual-helix structure may act as quantum memory units, storing quantum states encoded via frequency, phase, and amplitude modulation; and that nodes within the dual-helix structure may that store and manipulate quantum states for computational operations in quantum computing applications.. Specification, paragraph [0034].
(G) Wands Factor - The existence of working examples.
The inventor appears to provide prophetic examples delivering prophetic results:
The inventor appears to provide prophetic examples: “The alternating assignment may also support bidirectional data flow, wherein gradient information during backpropagation may travel downward through the same rung connections used for forward propagation. For example, during the execution phase 408, gradient information may travel downward through the same rung connections used for forward propagation. The rung couplers between nodes on the first helix chain 102A and the second helix chain 102B may operate bidirectionally, enabling both forward data flow and backward gradient flow within the same physical interconnect structure.’ Specification, paragraph [0108].
The inventor appears to provide prophetic results: “For example, during forward propagation, a quantum state may be transmitted from a node on the first helix chain 102A to a corresponding node on the second helix chain 102B via a rung coupler. During backpropagation, gradient information computed at the node on the second helix chain 102B may be transmitted back to the node on the first helix chain 102A through the same rung coupler. The bidirectional operation may be achieved through time-multiplexed control signals that alternate the direction of quantum state transfer, or through simultaneous bidirectional channels implemented using distinct optical wavelengths or polarization states in photonic implementations.” Specification, paragraph [0108].
(H) Wands Factor - The quantity of experimentation needed to make or use the invention based on the content of the disclosure.
Designing and conducting sets of complementary investigations that address complementary technology subsets might demonstrate that the specification enables any person skilled in the art to make and use the invention commensurate in scope with these claims.
For example, OAM-mediated machine learning is demonstrated by prior art Ewaniuk et al., (2022). Imperfect Quantum Photonic Neural Networks. Advanced Quantum Technologies, 6 (“Machine learning with optical neural networks has featured unique advantages of the information processing including high speed, ultrawide bandwidths and low energy consumption because the optical dimensions (time, space, wavelength, and polarization) could be utilized to increase the degree of freedom. However, due to the lack of the capability to extract the information features in the orbital angular momentum (OAM) domain, the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network model. Here, we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network (CNN) based on Laguerre-Gaussian (LG) beam modes with diverse diffraction losses”).
For example, light-twisting multilayer stacks are demonstrated by prior art Guo et al., Polarization entanglement enabled by orthogonally stacked van der Waals NbOCl2 crystals. Nat Commun 15, 10461 (2024). https://doi.org/10.1038/s41467-024-54876-w(“While we present a proof-of-concept demonstration, the fidelity of the polarization-entangled Bell states can be further enhanced by more precise control of sample thickness, stacking angle, and pump polarization angle. Exciting opportunities are also anticipated to emerge in creating and manipulating quantum light states through interfacing this vdW platform with cavity photonics and metaoptics, in light of the facile vdW integration with various photonic structures and twist-stacking degree of freedom demonstrated in this work. [Paragraph Break] As a novel platform for engineering photon-pair states at the nanoscale, greater designability and tunability with this twistable vdW platform is expected. For example, while we have demonstrated bilayer orthogonal stacks, additional possibilities could be accessible by employing arbitrary twist angles and by resorting to tri-layer stacks, as shown and discussed in Supplementary Section 11. In addition, arbitrary qutrit states could also be generated with tri-layer stacked structures (see Supplementary Fig. S13 for details), underpinning an avenue towards the preparation of complex quantum states at the nanoscale that are challenging for a natural χ(2) crystal with a fixed nonlinear tensor.”).
For example, nanostructures manipulating the amplitude, phase, and polarization states of light are demonstrated buy prior art Kim et al., Chiral 3D structures through multi-dimensional transfer printing of multilayer quantum dot patterns. Nat Commun 15, 6996 (2024) (“Three-dimensional optical nanostructures have garnered significant interest in photonics due to their extraordinary capabilities to manipulate the amplitude, phase, and polarization states of light. However, achieving complex three dimensional optical nanostructures with bottom-up fabrication has remained challenging, despite its nanoscale precision and cost-effectiveness, mainly due to inherent limitations in structural controllability. Here, we report the optical characteristics of intricate two- and three-dimensional nanoarchitectures made of colloidal quantum dots fabricated with multi-dimensional transfer printing. Our customizable fabrication platform, directed by tailored interface polarity, enables flexible geometric control over a variety of one-, two-, and three-dimensional quantum dot architectures, achieving tunable and advanced optical features. For example, we demonstrate a two-dimensional quantum dot nanomesh with tuned subwavelength square perforations designed by finite-difference time-domain calculations, achieving an 8-fold enhanced photoluminescence due to the maximized optical resonance. Furthermore, a three-dimensional quantum dot chiral structure is also created via asymmetric stacking of one-dimensional quantum dot layers, realizing a pronounced circular dichroism intensity exceeding 20°. [Paragraph Break] To this end, we designed simple chiral nanostructures composed of cross-stacked QD grating patterns, as illustrated in the inset of Fig. 5a. As already shown in Fig. 3c, mTP is capable of fabricating 3D twisted nanoarchitectures made solely of PbS QDs, with layer thicknesses of up to 450 nm, thereby enabling a high CPL absorption capacity. The flexible angle controllability between crossed QDgrating patterns offered bymTP can create various asymmetric geometries for high chirality. Additionally, the extensive control over structural parameters, such as symmetry, dimension, and thickness, allows for tunable CD spectra as will be demonstrated below.
For example, theoretical descriptions of arrays of closely spaced optical waveguides that are twisted around a central axis along the propagation direction are provided by prior art Vavulin et al., Quantum walks of photon pairs in twisted waveguide arrays, 2015 J. Phys.: Conf. Ser. 643 012050DOI 10.1088/1742-6596/643/1/012050. abstract. (“We consider an array of closely spaced optical waveguides, which are twisted around a central axis along the propagation direction. We derive Schrodinger-type equation of the biphoton wavefunction, taking into account the waveguide bending through the appearance of additional phase in the coupling coefficients. We present an example of the evolution of quantum photon-pair state.”).
For example, edge-to-edge topological transport in silicon waveguide lattices is experimentally investigated by prior art Yan et al., Silicon photonic quantum computing with spin qubits. APL Photonics 1 July 2021; 6 (7): 070901. Abstract (“Topological edge states (TESs), arising from topologically nontrivial phases, provide a powerful toolkit for the architecture design of photonic integrated circuits, since they are highly robust and strongly localized at the boundaries of topological insulators. It is highly desirable to be able to control TES transport in photonic implementations. Enhancing the coupling between the TESs in a finite-size optical lattice is capable of exchanging light energy between the boundaries of a topological lattice, hence facilitating the flexible control of TES transport. … we establish a bridge linking the interaction between the TESs in a finite-size optical lattice … to provide an alternative way to modulate/control the transport of topological modes. We experimentally demonstrate an edge-to-edge topological transport with high efficiency at telecommunication wavelengths in silicon waveguide lattices. Our results may power up various potential applications for integrated topological photonics.”).
Conclusion: Wands factor analyses indicate that the specification, while being enabling for “a method comprising: obtaining a quantum neural network having an ordered sequence of network layers, each network layer having a set of trainable parameters,” does not reasonably provide enablement for “a distinct set of quantum nodes physically arranged along two intertwined helices.”
Consequently, claims 1 and 3-10 are rejected under 35 U.S.C. 112(a) because the specification, while being enabling for “a method comprising: obtaining a quantum neural network having an ordered sequence of network layers, each network layer having a set of trainable parameters,” does not reasonably provide enablement for “a distinct set of quantum nodes physically arranged along two intertwined helices.” The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the invention commensurate in scope with these claims.
Claims 11 and 13-20
Independent claim 11 and claims 13-20, as dependent on claim 11, are rejected under 35 U.S.C. 112(a) because the specification, while being enabling for a quantum computing configuration configured to “assign each network layer of an ordered sequence of network layers of a quantum neural network, each network layer having a set of trainable parameters,” does not reasonably provide enablement for a “distinct set of quantum nodes positioned along the intertwined helices.” The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the invention commensurate in scope with these claims. As delineated by this Office actions Wands factor analyses, the specification’s block diagrams, and the text describing the block diagrams, do not reasonably provide enablement for quantum computing configured to:
“assign each network layer of an ordered sequence of network layers of a quantum neural network, each network layer having a set of trainable parameters, to a distinct set of quantum nodes positioned along the intertwined helices such that adjacent network layers that are consecutive in the ordered sequence are assigned to sets of quantum nodes on different ones of the intertwined helices and are operatively coupled via the inter-helix quantum couplers;”
“map the trainable parameters of each network layer to the inter-helix quantum couplers coupling the set of quantum nodes assigned to the network layer with the set of quantum nodes assigned to an adjacent network layer;”
“perform one or more quantum computations including executing quantum gate operations distributed across the sets of quantum nodes using the quantum computing architecture, the quantum gate operations including local quantum operations applied at the assigned sets of quantum nodes and inter-layer coupling operations applied through the inter-helix quantum couplers programmed according to the mapped trainable parameters, wherein adjacency and inter-layer communication between quantum nodes are determined by angular proximity along the two intertwined helices and by quantum coupling between quantum nodes positioned at corresponding or near-corresponding angular positions on different ones of the two intertwined helices;”
as recited in claim 11. See MPEP 2165.06(a) (citing In re Ghiron, 442 F.2d 985, 169 USPQ 723 (CCPA 1971), “The specification did not particularly identify each of the elements represented by the blocks or the relationship therebetween, nor did it specify particular apparatus intended to carry out each function. The Board further questioned whether the selection and assembly of the required components could be carried out routinely by persons of ordinary skill in the art.”). See MPEP 2165.06(a) (citing In re Scarbrough, 500 F.2d 560, 182 USPQ 298 (CCPA 1974), “The court concluded that there was not an enabling disclosure because the specification did not describe how ‘complex elements known to perform broadly recited functions in different systems would be adaptable for use in Appellant’s particular system with only a reasonable amount of experimentation’ and that ‘an unreasonable amount of work would be required to arrive at the detailed relationships appellant says that he has solved.’”).
Wands factor analyses of Specification’s Block Diagrams and Related Text
(A) Wands Factor - The breadth of the claims.
Reading the claims in light of the specification’s block diagrams and related text, the examiner determined that claims are directed at a quantum computing system configured to perform one or more quantum computations including executing quantum gate operations distributed across the sets of quantum nodes using the quantum computing architecture. . See Instant Application, Fig. 1A and related text (Block Representation of “an example quantum computing system” comprising “116 Quantum Processing Unit(s)” and “126 Entanglement, Cross-Talk Management Module”). All embodiments would necessarily comprise at least “a distinct set of quantum nodes physically arranged along two intertwined helices extending along a longitudinal axis of a quantum computing architecture.” Instant Application, Fig. 1A (cropped)and related text ( Block Representation of “an example” of a “dual-helix quantum architecture” comprising “102 [helix 102A intertwined with helix 120B],” “126 Entanglement, Cross-Talk Management Module,” “124 Waveguides and Dynamic Couplers,” “112 Photon System,” and “104 Multidimensional Modulation Controllers”).
(B) Wands Factor - The nature of the invention.
The claims are directed at multi-disciplinary photonic processes at the nano- and meta-scales. See Instant Application, Fig. 1A (cropped), (Block Representation of “an example” of a “dual-helix quantum architecture” comprising “102 [helix 102A intertwined with helix 120B],” “126 Entanglement, Cross-Talk Management Module,” “124 Waveguides and Dynamic Couplers,” “112 Photon System,” and “104 Multidimensional Modulation Controllers), Instant Application, Fig. 2. (Block and Stick Representation of “an example” of a “dual-helix quantum architecture” comprising “A-Chain” and “B-Chain”), and Instant Application, Fig. 3. (Block and Stick Flattened Representation of “an example” of a “dual-helix quantum architecture” comprising “A-helix chain,” “B-helix chain,” “A# quantum nodes,” “B# quantum nodes,” “301 links between quantum nodes,” and “302 links between quantum nodes”).
The claims are directed at abstract theoretical, mathematical, and computational processes. Instant Application, Figs. 5-9 (Block Representations of “example methods of quantum computing” comprising “quantum nodes”).
The claims are directed at hardware configurations and systems implementing computational processes enabled by photonic processes. Instant Application, Fig. 1B (Block Representation of “an example implementation” …of “116 Quantum Processing Unit” comprising “142 Single-Qubit Operations,” “144 Multi-Qubit Operations,” and“146 Parallel Processing Dual-Helix”).
(C) Wands Factor – The state of the prior art.
The examiner’s prior art search identified significant bodies of literature pertinent to key aspects of making and using the claimed “distinct set of quantum nodes physically arranged along two intertwined helices.”
Regarding neural networks, for example, activation energies and quantum photonics established high, the prior art disclosed theoretical and experimental investigations spanning multiple disciplines. See Gupta et al., A Multiple Controlled Toffoli Driven Adaptive Quantum Neural Network Model for Dynamic Workload Prediction in Cloud Environments," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 12, pp. 7574-7588, Dec. 2024, doi: 10.1109/TPAMI.2024.3402061, abstract (“The key challenges in cloud computing encompass dynamic resource scaling, load balancing, and power consumption. Accurate workload prediction is identified as a crucial strategy to address these challenges. Despite numerous methods proposed to tackle this issue, existing approaches fall short of capturing the high-variance nature of volatile and dynamic cloud workloads. Consequently, this paper introduces a novel model aimed at addressing this limitation. This paper presents a novel Multiple Controlled Toffoli-driven Adaptive Quantum Neural Network (MCT-AQNN) model to establish an empirical solution to complex, elastic as well as challenging workload prediction problems by optimizing the exploration, adaption, and exploitation proficiencies through quantum learning. The computational adaptability of quantum computing is ingrained with machine learning algorithms to derive more precise correlations from dynamic and complex workloads. The furnished input data point and hatched neural weights are refitted in the form of qubits while the controlling effects of Multiple Controlled Toffoli (MCT) gates are operated at the hidden and output layers of Quantum Neural Network (QNN) for enhancing learning capabilities. Complimentarily, a Uniformly Adaptive Quantum Machine Learning (UAQL) algorithm has evolved to functionally and effectually train the QNN. The extensive experiments are conducted and the comparisons are performed with state-of-the-art methods using four real-world benchmark datasets. Experimental results evince that MCT-AQNN has up to 32%–96% higher accuracy than the existing approaches.”). See also Zuo et al., (2019). All-optical neural network with nonlinear activation functions. Optica.Vol. 6, No. 9 September 2019; Zuo et al., Optical neural network quantum state tomography, Advanced Photonics 4(2), 026004 (24 Mar 2022); Williamson et al., Reprogrammable Electro-Optic Nonlinear Activation Functions for Optical Neural Networks, arXiv:1903.04579v2 [eess.SP] 23 Jul 2019; Maronese et al., Quantum activation functions for quantum neural networks. Quantum Inf Process 21, 128 (2022). https://doi.org/10.1007/s11128-022-03466-0 (Year: 2022); Mangini et al., Quantum computing models for artificial neural networks, 2021 EPL 134 10002DOI 10.1209/0295-5075/134/10002; Gupta et al., A Multiple Controlled Toffoli Driven Adaptive Quantum Neural Network Model for Dynamic Workload Prediction in Cloud Environments," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 46, no. 12, pp. 7574-7588, Dec. 2024, doi: 10.1109/TPAMI.2024.3402061; Fu et al., Optical neural networks: progress and challenges. Light Sci Appl 13, 263 (2024). https://doi.org/10.1038/s41377-024-01590-3; Fang et al. Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding. Light Sci Appl 13, 49 (2024). https://doi.org/10.1038/s41377-024-01386-5; Ewaniuk et al., (2022). Imperfect Quantum Photonic Neural Networks. Advanced Quantum Technologies, 6.
Regarding quantum computing and photonic gates, the prior art disclosed theoretical and experimental investigations spanning multiple disciplines. See Zhou et al., (2021). Chiral single-photon switch-assisted quantum logic gate with a nitrogen-vacancy center in a hybrid system. Photonics Research. 9. 405-415. 10.1364/PRJ.405246, abstract. (“We propose what we believe is a novel proposal for realizing a quantum C-NOT logic gate, through fabricating an interesting hybrid device with a chiral photon-pulse switch, a single nitrogen-vacancy (NV) center, and an optical microcavity. Three major different practical routes on realizing a chiral photon emitter are discussed, which can implement a chiral control unit via the nonreciprocal emitter–photon interactions, so-called “propagation-direction- dependent” emission. With the assistance of dichromatic microwave driving fields, we carry out the relevant C-NOT operations by engineering the interactions on a single NV spin in a cavity. We note that this logic gate is robust against practical noise and experimental imperfection, and this attempt may evoke wide and fruitful applications in quantum information processing.). See also, Aroche et al., DNA as a perfect quantum computer based on the quantum physics principles. Sci Rep 14, 11636 (2024). https://doi.org/10.1038/s41598-024-62539-5; Luo et al. Hybrid Toffoli gate on photons and quantum spins. Sci Rep. 2015 Nov 16;5:16716. doi: 10.1038/srep16716. PMID: 26568078; PMCID: PMC4644947.; Marengo et al., Double helix quantum computer, in Optical Fiber Communication Conference and International Conference on Quantum Information, 2001 OSA Technical Digest Series (Optica Publishing Group, 2001), paper PB2.; Peng et al., Universal Linear-Optical Logic Gate with Maximal Intensity Contrast Ratios, ACS Photonics 2018, 5, 1137−1143; Pooley et al., Controlled-NOT gate operating with single photons. Appl. Phys. Lett. 21 May 2012; 100 (21): 211103. https://doi.org/10.1063/1.4719077; Qian et al., A simple DNA gate motif for synthesizing large-scale circuits. J R Soc Interface 7 September 2011; 8 (62): 1281–1297. https://doi.org/10.1098/rsif.2010.0729 (Year: 2011); Zhang et al., Implementing Logic Gates by DNA Crystal Engineering, Adv. Mater. 2023, 2302345.
Regarding nano-scale active configurations, including dots and centers disposed near edges and helices, the prior art disclosed theoretical and experimental investigations spanning multiple disciplines. See Peter et al., Chirality dependent photon transport and helical superradiance, Phys. Rev. Research 6, 023200 – Published 22 May, 2024, abstract (“Chirality, or handedness, is a geometrical property denoting a lack of mirror symmetry. Chirality is ubiquitous in nature and is associated with the nonreciprocal interactions observed in complex systems ranging from biomolecules to topological materials. Here, we demonstrate that chiral arrangements of dipole-coupled atoms or molecules can facilitate the helicity-dependent superradiant emission of light. We show that the collective modes of these systems experience an emergent spin-orbit coupling that leads to chirality-dependent photon transport and nontrivial topological properties. These phenomena are fully described within the electric dipole approximation, resulting in very strong optical responses. Our results demonstrate an intimate connection between chirality, superradiance, and photon helicity and provide a comprehensive framework for studying electron transport dynamics in chiral molecules using cold atom quantum simulators.”). See also Aroche et al., DNA as a perfect quantum computer based on the quantum physics principles. Sci Rep 14, 11636 (2024). https://doi.org/10.1038/s41598-024-62539-5; Guo et al., Polarization entanglement enabled by orthogonally stacked van der Waals NbOCl2 crystals. Nat Commun 15, 10461 (2024). https://doi.org/10.1038/s41467-024-54876-w; Kim et al., Chiral 3D structures through multi-dimensional transfer printing of multilayer quantum dot patterns. Nat Commun 15, 6996 (2024). https://doi.org/10.1038/s41467-024-51179-y; Likhov et al., OAM-mode coupling by segmented helical-ring-core waveguides inscribed with a femtosecond laser beam, Opt. Lett. 49, 1217-1220 (2024); Marengo et al., Double helix quantum computer, in Optical Fiber Communication Conference and International Conference on Quantum Information, 2001 OSA Technical Digest Series (Optica Publishing Group, 2001), paper PB2.; Narayan et al., Discovery of Double Helix and Impact on Nanoscale to Mesoscale; Crystalline Structures, ACS Omega 2022, 7, 25853−25859; Parappurath et al., Direct observation of topological edge states in silicon photonic crystals: Spin, dispersion, and chiral routing. Sci. Adv.6,eaaw4137(2020). DOI:10.1126/sciadv.aaw4137; Peter et al., Chirality-induced emergent spin-orbit coupling in topological atomic lattices, arXiv:2311.09303. Sahoo et al., A Third Angular Momentum of Photons. Symmetry 2023, 15, 158. https://doi.org/10.3390/ sym1501015; Shi et al., Double-helix singularity and vortex–antivortex annihilation in space-time helical pulses, Nanophotonics 2025; 14(6): 741–747; Suárez-Forero et al., Chiral quantum optics: recent developments, and future directions, arXiv:2411.06495,10 Nov 2024; Vavulin et al., Quantum walks of photon pairs in twisted waveguide arrays, 2015 J. Phys.: Conf. Ser. 643 012050DOI 10.1088/1742-6596/643/1/012050; Yan et al, Chirality-dependent electromagnetically induced transparency based on a double semi-periodic helix metastructure, Opt. Lett. 43, 3722-3725 (2018); Zannotti et al., Chiral Light in Helically Twisted Photonic Lattices, Advanced Optical Materials 2017, 5, 1600629).
Regarding light-matter coupling, including structural influences, the prior art disclosed theoretical and experimental investigations spanning multiple disciplines. See Peter et al., Chirality-induced emergent spin-orbit coupling in topological atomic lattices, arXiv:2311.09303, abstract (“Spin-orbit coupling is of fundamental interest in both quantum optical and condensed matter systems alike. In this work, we show that optically induced electronic excitations in lattices of V-type atoms exhibit an emergent spin-orbit coupling when the geometry is chiral. This spin-orbit coupling arises naturally from the electric dipole interaction between the atomic sites and leads to a nontrivial topology for the lattice band structure. Using a general quantum optical model, we determine analytically the conditions that give rise to spin-orbit coupling and characterize the behavior under various symmetry transformations. We demonstrate that chirality-induced spin-orbit coupling can result from either the chirality of the underlying lattice geometry or the combination of an achiral lattice with a suitably chosen external quantization axis. We then discuss how these results are influenced by dissipation, which breaks time-reversal symmetry and illuminates the distinction between true and false chirality. Our results demonstrate that chiral atom arrays are a robust platform for realizing spin-orbit-coupled topological states of matter.”). See also, Sahoo et al., A Third Angular Momentum of Photons. Symmetry 2023, 15, 158. https://doi.org/10.3390/ sym1501015; Shi et al., Asymmetric chiral coupling in a topological resonator. Appl. Phys. Lett. 8 May 2023; 122 (19): 191104. https://doi.org/10.1063/5.0149671; Suárez-Forero et al., Chiral quantum optics: recent developments, and future directions, arXiv:2411.06495,10 Nov 2024; Tang et al., Highly sensitive and actively tunable chiral metasurface enabled by bound states in the continuum. Optics express 32 22 (2024): 40007-40019.; Xu et al., Topological Landau–Zener nanophotonic circuits, Advanced Photonics 5(3), 036005 (1 Jun 2023) https://doi.org/10.1117/1.AP.5.3.036005; Yan et al, Chirality-dependent electromagnetically induced transparency based on a double semi-periodic helix metastructure, Opt. Lett. 43, 3722-3725 (2018); Yang et al., (2024). Inverse-designed integrated all-optical nonlinear activators for optical computing. Optics Express. 32. 34001-34014. 10.1364/OE.531679. ; Qian et al., A simple DNA gate motif for synthesizing large-scale circuits. J R Soc Interface 7 September 2011; 8 (62): 1281–1297. https://doi.org/10.1098/rsif.2010.0729 (Year: 2011); Zhang et al., Implementing Logic Gates by DNA Crystal Engineering, Adv. Mater. 2023, 2302345. ‘
(D) Wands Factor - The level of one of ordinary skill.
A granular assessment of the above-cited prior art evinces the high of individual persons practicing one or two related disciplines covered by claimed invention’s multiple disciplines (for example, mathematics, physics, physical/organic/inorganic chemistry, nano- and room-scale materials science, electrical engineering, and computer science). However, by comparing the above-cited prior art to how the block diagrams integrate hardware, software, and sets of quantum nodes, it is readily apparent it is highly unlikely that a person would demonstrate high levels skill in anything more than a small subset of the large set of diverse skills required to make and use the invention commensurate in scope with these claims.
(E) Wands Factor - The level of predictability in the art.
While the above cited prior art may advance the predictability of some of the individual technologies that define the claimed invention’s large and diverse assemblage of multiple technologies, the prior art does not disclose any multidisciplinary efforts sufficient to assess the predictability of making and using a distinct set of quantum nodes physically arranged along two intertwined helices.
(F) Wands Factor - The amount of direction provided by the inventor.
Regarding the helices along which indistinct sets of quantum nodes are arranged, the inventor discloses the following.
The inventor discloses that helices’ material composition(s) may include “high-transparency quartz, fused silica, silicon nitride (SiN), lithium niobate (LiNbO.sub.3), or other suitable material(s).” Written Specification, paragraph [0026].
The inventor does not describe the helices’ structural feature beyond speculating that , “modulating the frequency, phase, and/or amplitude of each helix chain …might involve modifying structural properties of the dual-helix structure.” Written Specification, paragraph [0029].
The inventor discloses that the dual-helix structure may process quantum information. Specification, paragraph [0031].
The inventor discloses that the helix chains may operate in parallel while simultaneously processing different quantum tasks. Specification, paragraph [0027]
Regarding the quantum nodes that are arranged along the helices, the inventor discloses the following.
The inventor does not describe the quantum nodes’ material composition(s).
The inventor does not describe the quantum nodes’ structure(s).
The inventor discloses that sets of quantum nodes are, “physically arranged along two intertwined helices extending along a longitudinal axis of a quantum computing architecture such that adjacent network layers of the plurality of network layers are assigned to sets of quantum nodes on different ones of the two intertwined helices.” Specification, abstract.
The inventor discloses that quantum nodes may be “on one helix, along the helix, and along a helix and between helices. Specification, paragraph [0023].
The inventor discloses that positions of the members of a set of quantum nodes are characterized as angles of rotation about the longitudinal axis of the intertwined helices. Specification, paragraphs [0022] and [0050].
The inventor discloses that a rung is an inter-helix connection between a first node physically arranged along a first helix of a pair of intertwined helices and a second node physically arranged along a second helix of the pair of intertwined helices. Specification, paragraph [0023].
The inventor discloses that: nodes within the dual-helix structure may act as quantum memory units, storing quantum states encoded via frequency, phase, and amplitude modulation; and that nodes within the dual-helix structure may that store and manipulate quantum states for computational operations in quantum computing applications.. Specification, paragraph [0034].
(G) Wands Factor - The existence of working examples.
The inventor appears to provide prophetic examples delivering prophetic results:
The inventor appears to provide prophetic examples: “The alternating assignment may also support bidirectional data flow, wherein gradient information during backpropagation may travel downward through the same rung connections used for forward propagation. For example, during the execution phase 408, gradient information may travel downward through the same rung connections used for forward propagation. The rung couplers between nodes on the first helix chain 102A and the second helix chain 102B may operate bidirectionally, enabling both forward data flow and backward gradient flow within the same physical interconnect structure.’ Specification, paragraph [0108].
The inventor appears to provide prophetic results: “For example, during forward propagation, a quantum state may be transmitted from a node on the first helix chain 102A to a corresponding node on the second helix chain 102B via a rung coupler. During backpropagation, gradient information computed at the node on the second helix chain 102B may be transmitted back to the node on the first helix chain 102A through the same rung coupler. The bidirectional operation may be achieved through time-multiplexed control signals that alternate the direction of quantum state transfer, or through simultaneous bidirectional channels implemented using distinct optical wavelengths or polarization states in photonic implementations.” Specification, paragraph [0108].
(H) Wands Factor - The quantity of experimentation needed to make or use the invention based on the content of the disclosure.
Designing and conducting sets of complementary investigations that address complementary technology subsets might demonstrate that the specification enables any person skilled in the art to make and use the invention commensurate in scope with these claims.
For example, OAM-mediated machine learning is demonstrated by prior art Ewaniuk et al., (2022). Imperfect Quantum Photonic Neural Networks. Advanced Quantum Technologies, 6 (“Machine learning with optical neural networks has featured unique advantages of the information processing including high speed, ultrawide bandwidths and low energy consumption because the optical dimensions (time, space, wavelength, and polarization) could be utilized to increase the degree of freedom. However, due to the lack of the capability to extract the information features in the orbital angular momentum (OAM) domain, the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network model. Here, we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network (CNN) based on Laguerre-Gaussian (LG) beam modes with diverse diffraction losses”).
For example, light-twisting multilayer stacks are demonstrated by prior art Guo et al., Polarization entanglement enabled by orthogonally stacked van der Waals NbOCl2 crystals. Nat Commun 15, 10461 (2024). https://doi.org/10.1038/s41467-024-54876-w(“While we present a proof-of-concept demonstration, the fidelity of the polarization-entangled Bell states can be further enhanced by more precise control of sample thickness, stacking angle, and pump polarization angle. Exciting opportunities are also anticipated to emerge in creating and manipulating quantum light states through interfacing this vdW platform with cavity photonics and metaoptics, in light of the facile vdW integration with various photonic structures and twist-stacking degree of freedom demonstrated in this work. [Paragraph Break] As a novel platform for engineering photon-pair states at the nanoscale, greater designability and tunability with this twistable vdW platform is expected. For example, while we have demonstrated bilayer orthogonal stacks, additional possibilities could be accessible by employing arbitrary twist angles and by resorting to tri-layer stacks, as shown and discussed in Supplementary Section 11. In addition, arbitrary qutrit states could also be generated with tri-layer stacked structures (see Supplementary Fig. S13 for details), underpinning an avenue towards the preparation of complex quantum states at the nanoscale that are challenging for a natural χ(2) crystal with a fixed nonlinear tensor.”).
For example, nanostructures manipulating the amplitude, phase, and polarization states of light are demonstrated buy prior art Kim et al., Chiral 3D structures through multi-dimensional transfer printing of multilayer quantum dot patterns. Nat Commun 15, 6996 (2024) (“Three-dimensional optical nanostructures have garnered significant interest in photonics due to their extraordinary capabilities to manipulate the amplitude, phase, and polarization states of light. However, achieving complex three dimensional optical nanostructures with bottom-up fabrication has remained challenging, despite its nanoscale precision and cost-effectiveness, mainly due to inherent limitations in structural controllability. Here, we report the optical characteristics of intricate two- and three-dimensional nanoarchitectures made of colloidal quantum dots fabricated with multi-dimensional transfer printing. Our customizable fabrication platform, directed by tailored interface polarity, enables flexible geometric control over a variety of one-, two-, and three-dimensional quantum dot architectures, achieving tunable and advanced optical features. For example, we demonstrate a two-dimensional quantum dot nanomesh with tuned subwavelength square perforations designed by finite-difference time-domain calculations, achieving an 8-fold enhanced photoluminescence due to the maximized optical resonance. Furthermore, a three-dimensional quantum dot chiral structure is also created via asymmetric stacking of one-dimensional quantum dot layers, realizing a pronounced circular dichroism intensity exceeding 20°. [Paragraph Break] To this end, we designed simple chiral nanostructures composed of cross-stacked QD grating patterns, as illustrated in the inset of Fig. 5a. As already shown in Fig. 3c, mTP is capable of fabricating 3D twisted nanoarchitectures made solely of PbS QDs, with layer thicknesses of up to 450 nm, thereby enabling a high CPL absorption capacity. The flexible angle controllability between crossed QDgrating patterns offered bymTP can create various asymmetric geometries for high chirality. Additionally, the extensive control over structural parameters, such as symmetry, dimension, and thickness, allows for tunable CD spectra as will be demonstrated below.
For example, theoretical descriptions of arrays of closely spaced optical waveguides that are twisted around a central axis along the propagation direction are provided by prior art Vavulin et al., Quantum walks of photon pairs in twisted waveguide arrays, 2015 J. Phys.: Conf. Ser. 643 012050DOI 10.1088/1742-6596/643/1/012050. abstract. (“We consider an array of closely spaced optical waveguides, which are twisted around a central axis along the propagation direction. We derive Schrodinger-type equation of the biphoton wavefunction, taking into account the waveguide bending through the appearance of additional phase in the coupling coefficients. We present an example of the evolution of quantum photon-pair state.”).
For example, edge-to-edge topological transport in silicon waveguide lattices is experimentally investigated by prior art Yan et al., Silicon photonic quantum computing with spin qubits. APL Photonics 1 July 2021; 6 (7): 070901. Abstract (“Topological edge states (TESs), arising from topologically nontrivial phases, provide a powerful toolkit for the architecture design of photonic integrated circuits, since they are highly robust and strongly localized at the boundaries of topological insulators. It is highly desirable to be able to control TES transport in photonic implementations. Enhancing the coupling between the TESs in a finite-size optical lattice is capable of exchanging light energy between the boundaries of a topological lattice, hence facilitating the flexible control of TES transport. … we establish a bridge linking the interaction between the TESs in a finite-size optical lattice … to provide an alternative way to modulate/control the transport of topological modes. We experimentally demonstrate an edge-to-edge topological transport with high efficiency at telecommunication wavelengths in silicon waveguide lattices. Our results may power up various potential applications for integrated topological photonics.”).
Conclusion: Wands factor analyses indicate that the specification, while being enabling for a quantum computing configuration configured to “assign each network layer of an ordered sequence of network layers of a quantum neural network, each network layer having a set of trainable parameters,” does not reasonably provide enablement for a “distinct set of quantum nodes positioned along the intertwined helices.”
Consequently, claims 11 and 13-10 are rejected under 35 U.S.C. 112(a) because the specification, while being enabling for a quantum computing configuration configured to “assign each network layer of an ordered sequence of network layers of a quantum neural network, each network layer having a set of trainable parameters,” does not reasonably provide enablement for a “distinct set of quantum nodes positioned along the intertwined helices.” The specification does not enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the invention commensurate in scope with these claims.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-11, and 12-20
Claims 1, 3-11, and 12-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fu et al. (Optical neural networks: progress and challenges. Light Sci Appl 13, 263 (2024); “Fu”) in view of Mangini et al. (Quantum computing models for artificial neural networks, 2021 EPL 134 10002DOI 10.1209/0295-5075/134/10002; “Mangini”), further in view of Peter et al. (Chirality dependent photon transport and helical superradiance, Phys. Rev. Research 6, 023200 – Published 22 May, 2024; “Peter”), and further in view of Zhou et al. (Chiral single-photon switch-assisted quantum logic gate with a nitrogen-vacancy center in a hybrid system. 2021. Photonics Research. 9. 405-415. 10.1364/PRJ.40524; “Zhou”).
Regarding claim 1, Fu discloses in figures 4e and 7a-d, and related figures and text, for example, Selected Text, embodiments of optical neural networks, for example, that can perform matrix multiplication. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text.
Fu, Figures 4e and 7a-d, and Selected Text
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Fig. 4e Different optical elements and systems for implementing optical matrix multiplication. … OMM implementation based on integrated metalines (consists of subwavelength diffractive units).
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Abstract.
… In recent years, optical neural networks (ONNs) have made a range of research progress in optical computing due to advantages such as subnanosecond latency, low heat dissipation, and high parallelism. ONNs are in prospect to provide support regarding computing speed and energy consumption for the further development of artificial intelligence with a novel computing paradigm. Herein, we first introduce the design method and principle of ONNs based on various optical elements.
Principal of ONNs.
Implementation of optical matrix operations. Extensive matrix operations in the training or inferring stage of neural networks can be equivalent to the propagation process of light. Due to the advantages of low latency, low power consumption, large bandwidth, and parallel signal processing of light, the matrix operations process can be executed by modulating the optical feature quantities (amplitude, phase, polarization, angular momentum, etc.) during the light propagation process. Thus, further designing optical systems to achieve the inference function of ONNs is more advantageous than its electronic counterparts.
ONNs based on discrete optical diffractive elements.
The trainable parameters (modulation units) of the ONNs built based on the 4f system are used to be placed in the Fourier plane37–40, which limits the expansion of trainable parameters and the number of hidden layers. However, free-space DONNs constructed by diffractive elements42–59 can overcome these limitations well. In 2018, Lin et al.42 proposed an all-optical deep learning framework, in which neural networks are physically formed by multiple layers of diffractive surfaces (Fig. 7a). The diffractive surfaces are composed of diffractive units, each of which is termed as a hidden layer in DONNs, and each diffractive unit on the diffractive surface is defined as a neuron. Meanwhile, the function of the diffractive unit is to change the phase difference after the light passes, and the specific phase difference values can be pre-trained on the computer through intelligent algorithms such as forward propagation, gradient descent, error back-propagation, etc. Among them, the forward propagation process (wave analysis) is demonstrated by the Rayleigh- Sommerfeld diffraction equation42. After obtaining all phase difference values, the diffractive surfaces can be fabricated using 3D printing technology to obtain the physical implementation device of DONNs (Fig. 7c). The relationship between the thickness of diffractive units and phase difference values is linear, and the specific value of the thickness of each diffractive unit can be calculated by the formula: h ¼ λϕ=ð2πΔnÞ, where Δn is the refractive index difference between the 3D printing material and air, ϕ is the phase difference value (trainable parameter), λ is the wavelength of light propagation in free-space. Finally, Lin et al.42 validated the classification tasks of the MNIST and Fashion MNIST datasets by encoding the input signal into the amplitude and phase of light, respectively. This work proposed a novel scheme for the study of ONNs. In 2 years, Qian et al.44 designed a DONN (Fig. 7d) for logic gate operations (AND, OR, NOT, etc.) based on Rayleigh- Sommerfeld diffraction. The hidden layers function of the DONN is implemented by the Huygens metasurfaces fabricated through mechanical processing.
ONNs based on on-chip MZI mesh.
Theoretically, any matrix can be decomposed into one diagonal matrix and two unitary matrices by using the singular value decomposition method. The optical attenuators can implement any diagonal matrix function, and the beam splitters and phase shifters can achieve any unitary matrix function. Thus, the training weight matrices of ONNs can be physically implemented one-to-one via integrated optical elements. In this work, the parameters of on-chip ONN are obtained through pre-training on a computer, and the optical transmission characteristic curve of a saturated absorber is adopted as the nonlinear function during the ONN training process. …. This work leads to a new method for studying on-chip ONNs. In 2018, Hughes et al.84 proposed an in-situ training method for on-chip ONNs designed by MZI mesh. Firstly, the optical power of the output port of the on-chip ONNs is accurately measured. Then, the adjoint variable method is employed to realize the gradient derivation and error backpropagation during the on-chip ONNs in-situ training process (Fig. 10b). This method allows the structural parameters of on-chip ONNs to be directly trained by optical hardware, thus overcoming the error problem caused by the chip fabricating process, which is a crucial advancement in the training method of on-chip ONNs based on MZI mesh, from offline training to in-situ online training.
ONN based on on-chip diffractive metasurfaces.
The clever design of metasurfaces can theoretically achieve arbitrary control of the wavefront of reflected/ refracted beams106. Based on this, research on integrated DONNs107–120 has also been carried out.
To reduce the size of computing units and further improve the integration and system stability of the freespace DONNs. In 2021, Goi et al.109 selected a nearinfrared wavelength (785 nm) and fabricated an integrated DONN with high neuron density by two-photon nanolithography using the complementary metal-oxide semiconductor (COMS) chip as the substrate (Fig. 14a). The research team fabricated a multilayer diffractive metasurface on a CMOS chip consisting of an array of subwavelength cylindrical structures in steps of 10 nm in the height direction of the cylinder. This DONN is highly integrated and can fabricate about 500 million neurons per square centimeter. In 2022, Luo et al.112 selected the visible wavelength (532 nm) to fabricate an integrated DONN on a CMOS chip substrate that can support performing multi-channel sensing and multitasks in a visible light environment (Fig. 14b). The metasurface (hidden layer) in the integrated DONN consists of a subwavelength nanopillar array with a fixed height of 600 nm and a central spacing between adjacent nanopillars (the nanopillar array period) of 400 nm, which modulates the phase of propagated light by varying the length and width of each nanopillar. In this work, a multi-channel classifier framework was constructed by implementing a polarization multiplexing scheme using subwavelength nanostructures, which further demonstrates that the comprehensive design of optical feature quantities can endow ONNs architectures with the ability to handle multiple tasks (the various classification tasks of MNIST and Fashion- MNIST were demonstrated). In addition, the number of neurons on this CMOS chip that could be integrated per square millimeter was about 6.25 × 1016.
Additionally, the on-chip one-dimensional metasurfaces (metalines) composed of subwavelength diffractive units can also achieve the wavefront shaping of light in slab waveguides121,122. The on-chip DONNs constructed from diffractive metalines on slab waveguides have better stability, portability, and scalability. In 2020, Zarei et al.107 designed an on-chip DONN composed of diffractive metalines by using subwavelength rectangular slots on the SOI platform and validated the performance of the DONNs (Fig. 15a) through simulation calculations. The computing speed of such an on-chip DONN is about 1.2 × 1016 multiply accumulate operations per second. In 2021, Fu et al.108 developed an on-chip integrated two-dimensional spatial electromagnetic propagation model and a weight mapping model, which can complete parameter training via computers for on-chip DONN and ensure the pretrained parameters accurately map onto the physical devices (Fig. 15b). Two years later, Fu et al.115 fabricated on-chip DONNs (Fig. 15d) based on the previous theoretical exploration and considered the practical issues that were difficult to care for during the simulation part. Meanwhile, to effectively reduce the system errors caused by chip fabricating and packaging processes, an in-situ training scheme based on the particle swarm algorithm for error compensation was proposed as well. The effectiveness of this scheme remarkably ensured the performance of the DONN chips and improved the robustness of the experimental testing system. In 2022, Wang et al.110 further advanced their previous work122 by designing and fabricating an on-chip DONN (Fig. 15c) based on subwavelength diffractive units. In this work, to reduce the mutual interference between adjacent subwavelength diffractive units, Wang et al.110 combined two identical subwavelength diffractive units as a new computing cell in the hidden layer of the onchip DONNs. In addition, the input signals are loaded onto the DONN chip through a DMD and lens system, which indicates that the input dimension of the on-chip DONN can be unrestricted by the number of waveguides, providing a feasible solution for the problem of limited input dimension of on-chip ONNs.
ONN based on other on-chip optical components
In addition to the preceding introduced integrated optical devices83,90,96,103,109,115, there are a variety of onchip optical components that can construct ONNs123–140, such as single-mode waveguides, multi-mode interferometers, phase shifters, attenuators, detectors, three dimensional integrated waveguides, etc. … Moughames et al.128 utilized two-photon polymer printing technology to fabricate on-chip three-dimensional integrated low-loss photonic waveguide arrays. The waveguides interconnect structure corresponds to large-scale vector matrix products (Fig. 16b) and is the core of neural network computing.
Further regarding claim 1, Mangini, figure 1, and related figures and text, for example, Selected Text; embodiments of classical and quantum learning models: (a) classical feedforward NNs process information through consecutive layers of nodes, or neurons; (b) a quantum neural network in the form of a variational quantum circuit; (c) a model for a quantum perceptron; (d) quantum kernel methods map input data to quantum states; (e) a Quantum convolutional NN [44] consists of a repeated application of Convolutional Layers (CL) and Pooling Layers (PL), and a Fully Connected Layer (FCL); (f) a dissipative QNN.Mangini, figure 1, and related figures and text, for example, Selected Text.
Mangini, Figure 1 and Selected Text
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Quantum neural networks. – In gate-based quantum computers, such as those realized with superconducting qubits, ion traps, spins in semiconductors, or photonic circuits [9], a quantum computation is fully described by its quantum circuit representation, i.e., the arrangement of subsequent operations (gates) applied to the qubits in the quantum processing unit. These gates can be parametrized by continuous variables, like a Pauli-X rotation, Rx(θ) = eiθX/2. A quantum circuit that makes use of parametrized gates is known as a Parametrized Quantum Circuit (PQC) [10]. Variational Quantum Algorithms (VQAs) [11–13] are a large class of PQC-based protocols whose goal is to properly tune the parameters in order to solve a target task. For example, the task might be the minimization of the expectation value of an observable _O_ = _0|U(θ)†OU(θ)|0_, where U(θ) is the unitary operator representing the quantum circuit parametrized by angles θ = (θ1, θ2, . . .) and acting on a quantum register initialized in a reference state |0_ = |0_⊗n. VQAs are made by three main constituents: data encoding, variational ansatz, and final measurements with a classical update of the parameters. The first stage accounts for loading input data (classical or quantum) into the quantum circuit using quantum operations that depend on the given input; the second stage consists of a variational circuit with a specific structure, called ansatz, which often consists of repeated applications of similar operations (layers). Finally, a measurement on the qubits is performed to infer some relevant information about the system. Given the outcome, the parameters in the circuits are updated through a classical optimizer to minimize the cost function defining the problem. Such algorithms generally require limited resources to be executed on real quantum devices, thus they are expected to take full advantage of current noisy quantum hardware [14,15].
The definitions above highlight many similarities between PQCs and classical NNs. In both models, information is processed through a sequence of parametrized layers, which are iteratively updated using a classical optimizer. The key difference lies in the way information is processed. Quantum computers promise to possibly achieve some form of computational advantage, i.e., speedups or better performances [7], due to inherently quantum effects not available in classical learning scenarios. While many different definitions of Quantum Neural Networks (QNN) are possible, the most general and currently accepted one is that of a parametrized quantum circuit whose structure is directly inspired by classical neural networks….
Optimization routines. The most common training routine in classical NNs is the backpropagation algorithm [1], which combines the chain rule for derivatives and stored values from intermediate layers to efficiently compute gradients. However, such a strategy cannot be straightforwardly generalized to the quantum case, because intermediate values from a quantum computation can only be accessed via measurements, which disturb the relevant quantum states [60]. For this reason, the typical strategy is to use classical optimizers leveraging gradient based or gradient-free numerical methods. However, there exist scenarios where the quantum nature of the optimization can be taken into account, leading to strategies tailored to the quantum domain like the parameter shift rule [19,60,61], the quantum natural gradient [62], and closed update formulas [20]. Finally, while a fully coherent quantum update of the parameters is certainly appealing [63], this remains out of reach for near-term hardware, which is limited in the number of available qubits and circuit depth [9].
Outlooks. – Quantum machine learning techniques, and particularly quantum neural network models, hold promise of significantly increasing our computational capabilities, unlocking whole new areas of investigation. The main theoretical and practical challenges currently encompass trainability, the identification and suitable treatment of appropriate data sets, and a systematic comparison with classical counterparts. Moreover, with steady progress on the hardware side, significant attention will be devoted in the near future towards experimental demonstrations of practical relevance. In fact, small to medium scale implementations of quantum machine learning techniques have already been tested on actual quantum hardware, such as currently available digital quantum circuits based on superconducting qubits [29,30,32,33,81], nanophotonic architectures [82,83], and devices based on trapped ion qubits [84].
Consequently, it would have been obvious to one of ordinary skill in the art to modify Fu to disclose a method comprising obtaining a quantum neural network having an ordered sequence of network layers, each network layer having a set of trainable parameters; Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; because the resulting configuration and method would facilitate designing, fabricating and deploying implementations of quantum machine learning techniques, for example, comprising quantum hardware and digital quantum circuits based on superconducting qubits, nanophotonic architectures, and devices based on trapped ion qubits. Mangini, Outlooks.
Further regarding claim 1, Peter discloses in figure 1, and related figures and text, for example, Selected Text; embodiments of‘chiral arrangements of dipole-coupled atoms or molecules and the helicity-dependent superradiant emission of light as applicable to quantum information science. Peter, figure 1, and related figures and text, for example, Selected Text.
Peter, Figure 1 and Selected Text
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Abstract. …Chirality, or handedness, is a geometrical property denoting a lack of mirror symmetry. Chirality is ubiquitous in nature and is associated with the nonreciprocal interactions observed in complex systems ranging from biomolecules to topological materials. Here, we demonstrate that chiral arrangements of dipole-coupled atoms or molecules can facilitate the helicity-dependent superradiant emission of light. We show that the collective modes of these systems experience an emergent spin-orbit coupling that leads to chirality-dependent photon transport and nontrivial topological properties. These phenomena are fully described within the electric dipole approximation, resulting in very strong optical responses. Our results demonstrate an intimate connection between chirality, superradiance, and photon helicity and provide a comprehensive framework for studying electron transport dynamics in chiral molecules using cold atom quantum simulators.
I. Introduction. … Of course, in addition to electrons, photons can also carry spin angular momentum, which is encoded in their two orthogonal polarizations [18–21]. The coupling of photons to atoms or molecules [22,23] can result in efficient energy transfer [24–27] and cooperative dissipation leading to the superradiant and subradiant emission of light [28–31]. Demonstration of chirality-induced photon transport could facilitate a similar explosion in the development of polarization-controllable photonics devices that are analogous to CISS-based spintronics. …
II. HELICITY DEPENDENT DYNAMICS …Excitation transport between atoms or molecules can be modeled as a collection of quantum emitters interacting with a radiation field [46]. As a minimal model, we focus on a helix as the archetypal chiral geometry—though our results are generalizable to arbitrary chiral setups. The individual dipoles along the helix are modeled as V-type quantum emitters, each with two hyperfine transitions excited by left (σ +) and right (σ −) circularly polarized light, respectively, and resonance frequency ω0 = 2πc/λ0, where λ0 is the wavelength of the optical transition and c is the speed of light in vacuum [Fig. 1(b)]. Long-range dipole-dipole interactions between emitters at positions ri and rj are mediated through real and virtual photon exchanges that couple orbitals |σi_ and |σ _ j _ with either the same or opposite polarization. When the photon polarization is changed, conservation of angular momentum requires that the interaction picks up an additional position-dependent phase [Fig. 1(c)].
VI. OUTLOOK …Here we have demonstrated the helicity-dependent transport and superradiant emission of circularly polarized photons from chiral arrangements of atoms or molecules. This phenomenon has a complete description within the electric dipole approximation, placing it within the class of nonmagnetic chiral interactions exhibiting very strong optical responses [70,71]. Precise control over the transport and emission of photons is a fundamental goal of quantum information science and could contribute to the development of new quantum technologies. Our findings also represent an exciting opportunity for cold atom quantum simulators towards studying chirality dependent transport and chiral light-matter interactions at the single-atom scale.
Consequently, in light of Peter’s disclosure of quantum emitters interacting with radiation fields, it would have been obvious to one of ordinary skill in the art to modify the method of Fu in view of Mangini to disclose obtaining a quantum neural network having an ordered sequence of network layers, each network layer having a set of trainable parameters; assigning each network layer of the ordered sequence of network layers to a distinct set of quantum nodes physically arranged along two intertwined helices extending along a longitudinal axis of a quantum computing architecture such that adjacent network layers that are consecutive in the ordered sequence are assigned to sets of quantum nodes on different ones of the two intertwined helices and are quantum-mechanically coupled via inter-helix quantum couplers; mapping the trainable parameters each network layer to quantum connections implemented by the inter-helix quantum couplers between the set of quantum nodes assigned to the network layer and the set of quantum nodes assigned to an adjacent network layer; and performing one or more quantum computations including executing distributed quantum gate operations across the sets of quantum nodes using the quantum computing architecture, the distributed quantum gate operations including local quantum operations applied at the assigned sets of quantum nodes and inter-layer coupling operations applied through the inter-helix quantum couplers programmed according to the mapped trainable parameters, wherein adjacency and inter- layer communication between quantum nodes are determined based on angular proximity of quantum nodes along the two intertwined helices and by quantum-mechanical coupling between quantum nodes positioned at corresponding or near-corresponding angular positions on different ones of the two intertwined helices. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; because the resulting configuration and method would facilitate designing, fabricating and deploying quantum hardware and circuits; Mangini; Outlooks, that can perform C-NOT operations. Zhou, figure 1, and related figures and text, for example, Selected Text.
Zhou, Figure 1 and Selected Text
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Abstract. We propose what we believe is a novel proposal for realizing a quantum C-NOT logic gate, through fabricating an interesting hybrid device with a chiral photon-pulse switch, a single nitrogen-vacancy (NV) center, and an optical microcavity. Three major different practical routes on realizing a chiral photon emitter are discussed, which can implement a chiral control unit via the nonreciprocal emitter–photon interactions, so-called “propagation-direction- dependent” emission. With the assistance of dichromatic microwave driving fields, we carry out the relevant C-NOT operations by engineering the interactions on a single NV spin in a cavity. We note that this logic gate is robust against practical noise and experimental imperfection, and this attempt may evoke wide and fruitful applications in quantum information processing.
Regarding method claims 3-10 and system claims 11 and 13-20, it would have been obvious to one of ordinary skill in the art to modify Fu in view of Mangini, further in view of Peter, and further in view of Zhou, as applied in the rejection of claim 1, to disclose:
3. The method of claim 1, wherein assigning each network layer of the ordered sequence of network layers to a distinct set of quantum nodes includes: determining a number of quantum nodes for a first network layer; and selecting a first set of quantum nodes on a first helix of the intertwined helices with a number of quantum nodes equal to the determined number of quantum nodes for the first network layer. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
4. The method of claim 3, wherein the first set of quantum nodes are contiguous quantum nodes on the first helix. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
5. The method of claim 3, wherein assigning each network layer of the ordered sequence of network layers to a distinct set of quantum nodes further includes: determining a number of quantum nodes for a second network layer adjacent to the first network layer; and selecting a second set of quantum nodes on a second helix of the intertwined helices with a number of quantum nodes equal to the determined number of quantum nodes for the second network layer. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
6. The method of claim 1, further comprising forming a quantum connection between different sets of quantum nodes on different ones of the intertwined helices. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
7. The method of claim 1, further comprising mapping operation parameters associated with a network layer of the ordered sequence of network layers to quantum connections between quantum nodes of the set of quantum nodes to which the network layer is assigned. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
8. The method of claim 1, wherein performing one or more quantum computations using the quantum computing architecture includes applying activation functions locally at one or more of the quantum nodes. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
9. The method of claim 1, wherein performing one or more quantum computations using the quantum computing architecture includes passing gradient information along bi-directional channels between the sets of quantum nodes along the intertwined helices. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
10. The method of claim 1, wherein performing one or more quantum computations using the quantum computing architecture includes two or more sets of quantum nodes on a helix of the intertwined helices forming a spatial data pipeline. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
11. A quantum computing system comprising: a plurality of quantum nodes physically arranged along two intertwined helices extending along a longitudinal axis of a quantum computing architecture; inter-helix quantum couplers coupling quantum nodes on different ones of the two intertwined helices; a quantum processing system configured to: assign each network layer of an ordered sequence of network layers of a quantum neural network, each network layer having a set of trainable parameters , to a distinct set of quantum nodes positioned along the intertwined helices such that adjacent network layers that are consecutive in the ordered sequence are assigned to sets of quantum nodes on different ones of the intertwined helices and are operatively coupled via the inter-helix quantum couplers; map the trainable parameters of each network layer to the inter-helix quantum couplers coupling the set of quantum nodes assigned to the network layer with the set of quantum nodes assigned to an adjacent network layer; and perform one or more quantum computations including executing quantum gate operations distributed across the sets of quantum nodes using the quantum computing architecture, the quantum gate operations including local quantum operations applied at the assigned sets of quantum nodes and inter-layer coupling operations applied through the inter-helix quantum couplers programmed according to the mapped trainable parameters , wherein adjacency and inter-layer communication between quantum nodes are determined by angular proximity along the two intertwined helices and by quantum coupling between quantum nodes positioned at corresponding or near-corresponding angular positions on different ones of the two intertwined helices. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
13. The quantum computing system of claim 11, wherein assigning each network layer of the ordered sequence of network layers to a distinct set of quantum nodes includes: determining a number of quantum nodes for a first network layer; and selecting a first set of quantum nodes on a first helix of the intertwined helices with a number of quantum nodes equal to the determined number of quantum nodes for the first network layer. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
14. The quantum computing system of claim 13, wherein the first set of quantum nodes are contiguous quantum nodes on the first helix. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
15. The quantum computing system of claim 13, wherein assigning each network layer of the ordered sequence of network layers to a distinct set of quantum nodes further includes: determining a number of quantum nodes for a second network layer adjacent to the first network layer; and selecting a second set of quantum nodes on a second helix of the intertwined helices with a number of quantum nodes equal to the determined number of quantum nodes for the second network layer. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
16. The quantum computing system of claim 11, further comprising a quantum connection between different sets of quantum nodes on different ones of the intertwined helices. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
17. The quantum computing system of claim 11, wherein the quantum processing system is further configured to map trainable parameters associated with a network layer of the ordered sequence of network layers to quantum connections between quantum nodes of the set of quantum nodes to which the network layer is assigned. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
18. The quantum computing system of claim 11, wherein performing one or more quantum computations using the quantum computing architecture includes applying activation functions locally at one or more of the quantum nodes. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
19. The quantum computing system of claim 11, wherein performing one or more quantum computations using the quantum computing architecture includes passing gradient information along bi-directional channels between the sets of quantum nodes along the intertwined helices. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
20. The quantum computing system of claim 11, wherein performing one or more quantum computations using the quantum computing architecture includes two or more sets of quantum nodes on a helix of the intertwined helices forming a spatial data pipeline. Fu, figures 4e and 7a-d, and related figures and text, for example, Selected Text; Mangini, figure 1, and related figures and text, for example, Selected Text; Peter, figure 1, and related figures and text, for example, Selected Text; Zhou, figure 1, and related figures and text, for example, Selected Text.
because the resulting configurations, methods and systems would facilitate designing, fabricating and deploying quantum hardware and circuits; Mangini; Outlooks, that can perform C-NOT operations. Zhou, figure 1, and related figures and text, for example, Selected Text.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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/PETER RADKOWSKI/Primary Examiner, Art Unit 2874
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