DETAILED 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 .
Priority
Applicant claim the benefit of a prior-filed Japanese Patent Application No.2022-149292, filed September 20, 2022, which is acknowledged.
Drawings
The drawings were received on 02/28/2023. These drawings are acceptable.
Information Disclosure Statement
The information disclosure statements (IDSs) submitted on 04/13/2023 and 02/28/2023 been considered by the examiner.
Claim Interpretation
Per the guidance provided in MPEP 2111, the examiner makes record of the broadest reasonable interpretation (BRI) used for claim limitation terms in light of applicant specification.
Noted portions of MPEP 2111:
During patent examination, the pending claims must be "given their broadest reasonable interpretation consistent with the specification." The Federal Circuit’s en banc decision in Phillips v. AWH Corp., 415 F.3d 1303, 1316, 75 USPQ2d 1321, 1329 (Fed. Cir. 2005) expressly recognized that the USPTO employs the "broadest reasonable interpretation" standard:
The Patent and Trademark Office ("PTO") determines the scope of claims in patent applications not solely on the basis of the claim language, but upon giving claims their broadest reasonable construction "in light of the specification as it would be interpreted by one of ordinary skill in the art." In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1364[, 70 USPQ2d 1827, 1830] (Fed. Cir. 2004). Indeed, the rules of the PTO require that application claims must "conform to the invention as set forth in the remainder of the specification and the terms and phrases used in the claims must find clear support or antecedent basis in the description so that the meaning of the terms in the claims may be ascertainable by reference to the description." 37 CFR 1.75(d)(1).
See also In re Suitco Surface, Inc., 603 F.3d 1255, 1259, 94 USPQ2d 1640, 1643 (Fed. Cir. 2010); In re Hyatt, 211 F.3d 1367, 1372, 54 USPQ2d 1664, 1667 (Fed. Cir. 2000).
….
Under a broadest reasonable interpretation (BRI), words of the claim must be given their plain meaning, unless such meaning is inconsistent with the specification. The plain meaning of a term means the ordinary and customary meaning given to the term by those of ordinary skill in the art at the relevant time. The ordinary and customary meaning of a term may be evidenced by a variety of sources, including the words of the claims themselves, the specification, drawings, and prior art. However, the best source for determining the meaning of a claim term is the specification - the greatest clarity is obtained when the specification serves as a glossary for the claim terms. Phillips v. AWH Corp., 415 F.3d 1303, 1315, 75 USPQ2d 1321, 1327 (Fed. Cir. 2005) (en banc) ("[T]he specification ‘is always highly relevant to the claim construction analysis. Usually, it is dispositive; it is the single best guide to the meaning of a disputed term.’" (quoting Vitronics Corp. v. Conceptronic Inc., 90 F.3d 1576, 1582 (Fed. Cir. 1996)). The words of the claim must be given their plain meaning unless the plain meaning is inconsistent with the specification. In re Zletz, 893 F.2d 319, 321, 13 USPQ2d 1320, 1322 (Fed. Cir. 1989) (discussed below); Chef America, Inc. v. Lamb-Weston, Inc., 358 F.3d 1371, 1372, 69 USPQ2d 1857 (Fed. Cir. 2004) (Ordinary, simple English words whose meaning is clear and unquestionable, absent any indication that their use in a particular context changes their meaning, are construed to mean exactly what they say…
The presumption that a term is given its ordinary and customary meaning may be rebutted by the applicant by clearly setting forth a different definition of the term in the specification. In re Morris, 127 F.3d 1048, 1054, 44 USPQ2d 1023, 1028 (Fed. Cir. 1997) (the USPTO looks to the ordinary use of the claim terms taking into account definitions or other "enlightenment" contained in the written description); But c.f. In re Am. Acad. of Sci. Tech. Ctr., 367 F.3d 1359, 1369, 70 USPQ2d 1827, 1834 (Fed. Cir. 2004) ("We have cautioned against reading limitations into a claim from the preferred embodiment described in the specification, even if it is the only embodiment described, absent clear disclaimer in the specification."). When the specification sets a clear path to the claim language, the scope of the claims is more easily determined and the public notice function of the claims is best served…
The claims in this application are given their broadest reasonable interpretation (BRI) using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art, see MPEP 2111. Examiner notes the following BRI of the noted claim terms:
The noted claim terms:
Quantum Circuit:
a framework in computer science that is used to evaluate the computational resources used in a quantum algorithm for modeling the causal relationship in quantum physics as connected qubit registers and quantum gate operators; wherein the qubit registers are represented by wires to model the state of qubit evolution according to a sequence of gates that define the quantum circuit; Examples of these resources for modeling quantum information are depicted in Figs. 2-5 each demonstrate the BRI of a quantum circuits comprised of concatenated operators/sub circuit blocks
Also See Coreas et al. (NPL: Everything You Always Wanted to Know About Quantum Circuits)
Concatenated: connected/chaining quantum information for processing circuit operators/elements (e.g. q-bits, quantum gates …etc.)
Parameterized circuit: any operational element/object that is controlled/modeled with a corresponding parameter. Or a quantum circuit that incorporates a tunable parameter
Quantum bits (i.e. qbit or qubit):
are considered element that can hold the value 0 and 1 at the same time described using equation
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where the value of the bit when observed (or measured) collapsed to a state of 0 or 1;
can also be interpreted as an element for carrying information associated with a quantum process/system/algorithm that can be observed or measured.
Quantum gate:
not a physical gate line in a classical computer but refers model representation used to describe matrix operations in quantum computed as noted in the following examples:
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A qubit/qbit is considered a wire carrying information in a quantum circuit/system
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, 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.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Romero et al. (US 11636370, hereinafter ‘Rom’) in view of Sim et al. (NPL: “Adaptive pruning-based optimization of parameterized quantum circuits”, hereinafter ‘Sim’) in further view of Hollenberg et al. (US 20230259673, hereinafter ‘Holle’).
Regarding independent claim 14, Rom teaches a quantum-classical hybrid neural network comprising a plurality of block circuits, each of the plurality of block circuits comprising a quantum circuit in which quantum information undergoes processing in an order of a (in As depicted in Figs.. 5A-D; And in 4:46-50: FIG. 5 is a diagram of a hybrid quantum-classical computer for implementing Generative Adversarial Network (GAN) [a quantum-classical hybrid neural network] according to one embodiment of the present invention; And in 7:4-24: …FIG. 4 illustrates an example of such the quantum generator circuit 400. In the embodiment of FIG. 4, the circuit architecture of the quantum generator circuit 400 includes a circuit that generates states from a latent space (z) using the variational circuit G(Θ.sub.g) 402. The classical vector z is encoded into a quantum state using a fixed encoding circuit, R 404, which maps by measuring a fixed set of operators on the generated state, thereby producing the fake sample x.sub.Fake 508. And in 7:24-30: The variational quantum generator 400 includes two variational circuits [a plurality of block circuits, each of the plurality of block circuits comprising a quantum circuit… ]: (1) a first quantum encoding circuit R(z) 404 [quantum-classical hybrid neural network comprising a repetitive structure of block circuits each comprising a quantum circuit in which quantum information undergoes processing in an order of a Hartree-Fock state construction], which acts on r qubits, and (2) a generator circuit G(Θ.sub.g) 402 acting on a register of n qubits, with n>r. For example, to generate a black and white image of size 256×256 pixels encoded as a quantum state, the quantum generator circuit 400 would require 16 qubits (n=16)…; And in 8:31-42: A second quantum encoding circuit 502 (shown in more detail in FIG. 6) [quantum-classical hybrid neural network comprising a repetitive structure of block circuits each comprising a quantum circuit in which quantum information undergoes processing in an order of a Hartree-Fock state construction], which may correspond to a variational circuit of choice, may receive a random variable x∈custom character.sup.d 514 (which may be, for example, a scalar) as a circuit parameter and generates a random selection of the state from the manifold of states prepared as R(x)|0.sup..Math.rcustom character=|ϕ(x)custom character. This is the quantum equivalent to the random source employed in classical Generative Adversarial Networks (GANs), where the space of the variable x would correspond to the latent space in the language of generative models and the corresponding manifold of states {|ϕ(x)custom character} would correspond to the quantum latent space…)
a parameterized quantum circuit processing, (in 11:65-12:3: A given variational quantum circuit may be parameterized in a suitable device-specific manner [a parameterized quantum circuit processing,]. More generally, the quantum gates making up a quantum circuit may have an associated plurality of tuning parameters. For example, in embodiments based on optical switching, tuning parameters may correspond to the angles of individual optical element)
and a measurement layer processing. (13:43-46: The final state 272 of the quantum computer 252 is measured, thereby producing results 276 (i.e., measurements) (FIG. 2B, operation 274). The measurement operation 274 may be performed, for example, in any of the ways disclosed herein, such as in any of the ways disclosed herein in connection with the measurement unit 110 [a measurement layer processing] in FIG. 1.)
Rom does not expressly teach the limitation …a quantum circuit in which quantum information undergoes processing in an order of a Hartree-Fock state construction… wherein the plurality of block circuits includes a first block circuit and a second block circuit concatenated to the first block circuit, and wherein a measured value of a quantum state output from the first block circuit is provided to the second block circuit as an initial state of the second block circuit.
Sim does expressly teach the limitation …a quantum circuit in which quantum information undergoes processing in an order of a Hartree-Fock state construction… wherein the plurality of block circuits includes a first block circuit and a second block circuit concatenated to the first block circuit (As depicted in Fig. 10:
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Figure 10. Gate composition of a simplified version of the LDCA from reference [38]. Circuit in (a) is an instance of LDCA for 8 qubits. Each entangling operation in LDCA is composed of five two-qubit gate operations with three free parameters, as shown in (b). The YX and XY operations sharing a parameter (with opposite signs) and XX and YY operations sharing another parameter leads to an ansatz that preserves particle number, assuming the initial state is e.g. the Hartree–Fock state. [a quantum-classical hybrid neural network comprising a repetitive structure of block circuits each comprising a quantum circuit in which quantum information undergoes processing in an order of a Hartree-Fock state construction.. wherein the plurality of block circuits includes a first block circuit and a second block circuit concatenated to the first block circuit] )
Sim and Rom are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for developing an optimization strategy ansatze used in variational quantum algorithms, as disclosed by Sim with the method for developing and implementing information processing techniques using a hybrid quantum classical computer comprising a variational quantum generator circuit, as disclosed by Rom.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Sim and Rom as disclosed above; Doing so enables optimizations of certain ansatze that were previously difficult to converge and more generally can improve the performance of variational algorithms by reducing the optimization runtime and/or the depth of circuits that encode the solution candidate(s), (Sim, Abstract).
Rom and Sim disclose the system for combining/concatenating blocks of quantum information as claimed.
Rom and Sim do not expressly teach the input from one block to the initial input in a second block.
Holle expressly teaches the input from one block to the initial input in a second block claimed in wherein the plurality of block circuits includes a first block circuit and a second block circuit concatenated to the first block circuit, and wherein a measured value of a quantum state output from the first block circuit is provided to the second block circuit as an initial state of the second block circuit. as depicted in Fig. 7 and in [0057] FIG. 7 [wherein the plurality of block circuits includes a first block circuit and a second block circuit concatenated to the first block circuit, and wherein a measured value of a quantum state output from the first block circuit is provided to the second block circuit as an initial state of the second block] illustrates hybrid quantum algorithmic approaches to energy determination/optimisation problems—VQE and QAOA. These approaches generally use relatively long circuits and may suffer from catastrophic error accumulation. And in [0087] In a QC context, the problem in question is first transformed into the corresponding Hamiltonian. The determination of the system energy via a VQE approach proceeds by creating a variational trial state |ψ.sub.0(α.sub.0)custom-character, w.r.t. parameters α.sub.0, and measuring custom-characterH.sup.1custom-character term by term in the Hamiltonian as shown in FIG. 7 to determine the energy E(α.sub.0). The whole process is contained within a classical loop to choose a new value of α.sub.0 to eventually minimise E(α.sub.0). The quality of the VQE result is directly governed by the quality of the trial-state choice |ψ.sub.0(α.sub.0)custom-character—generally this is kept as simple as possible to be created via a short depth circuit to minimize the effect of cumulative logic errors in the QC [wherein the plurality of block circuits includes a first block circuit and a second block circuit concatenated to the first block circuit, and wherein a measured value of a quantum state output from the first block circuit is provided to the second block circuit as an initial state of the second block]. One could systematically improve on the result using the Quantum Approximate Optimisation Algorithm (QAOA) (represented in FIG. 7 as higher iterations) [wherein the plurality of block circuits includes a first block circuit and a second block circuit concatenated to the first block circuit, and wherein a measured value of a quantum state output from the first block circuit is provided to the second block circuit as an initial state of the second block], but again this is at the expense of circuit depth and hence compromising the fidelity of the output.
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Holle, Sim and Rom are analogous art because both involve developing information retrieval and processing techniques using machine learning systems and algorithms.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the teachings of the prior art for developing and modeling a quantum computer using Variational Quantum Eigensolver as disclosed by Holle with the method for developing and implementing information processing techniques using a hybrid quantum classical computer comprising a variational quantum generator circuit, as collectively disclosed by Sim and Rom.
One of ordinary skill in the arts would have been motivated to combine the disclosed methods disclosed by Holle, Sim and Rom as disclosed above; Doing so help control errors in the implementation of quantum logic gates, (Holle, [0002] and [0087]).
Allowable Subject Matter
The following is an examiner’s statement of reasons for allowance:
Claims 1, 12 and 13 and their respective dependent claims (2-11) are considered allowable since when reading the claims in light of the specification, as per MPEP $2111.01 none of the references of record alone or in combination disclose or suggest the combination of encoding parameters and a transformation gate that is a gate parametrized with a learning parameter for transforming a Hartee-Fock state using a concatenated quantum circuits when combined with the remaining limitations of the claims.
Any comments considered necessary by applicant must be submitted no later than the payment of the issue fee and, to avoid processing delays, should preferably accompany the issue fee. Such submissions should be clearly labeled “Comments on Statement of Reasons for Allowance.”
Response to Arguments
Applicant's arguments filed 01/20/2026 have been fully considered.
The remarks directed to the interpretation of the claim term “concatenate” are not persuasive as noted in the interview with applicant. The examiner has included a claim interpretation section as discussed and art has been cited in current office to support the interpretation taken and used to address the amended limitations in claim 14. See current office action and relevant cited prior art.
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).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Morten Hjorth-Jensen (NPL: Hartree-Fock method): teaches Hartree-Fock (HF) theory is an algorithm for finding an approximative expression for the ground state of a given Hamiltonian. The basic ingredients are
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EKERT (NPL: BASIC CONCEPTS IN QUANTUM COMPUTATION): teaches concatenated codes are used in quantum fault tolerance computations.
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McClean et al. (US 11894860): teaches a level of concatenation, i.e., how many times the code is chained together to increase robustness.
Elfving et al. (US 20230020166): teaches the iterative process for chain/concatenating quantum circuits, in [0076] FIGS. 3A and 3B depict a schematic of a variational quantum eigensolver VQE system that may be used for quantum chemistry simulation using the above-described paired-electron unitary coupled cluster with double excitations (pUCCD) ansatz. As shown in FIG. 3A, the VQE system 302 may include a quantum processor 304 and a classical processor 306…[0179] In an embodiment, a variational circuit .Math..sub.θ one or more layers of parametrized rotations may be selected. These layers may be followed by layers of CNOT operations. This is known as a hardware efficient ansatz (HEA), which was proposed for variational quantum encoder VQE schemes for chemistry applications. The structure of a HEA quantum circuit corresponds to concatenated layers of single qubit rotations and global entangling layers for all N qubits or at least a large part thereof FIGS. 4A and 4B illustrate exemplary variational quantum circuits according to various embodiments of the invention. In particular, FIG. 4A shows a variational ansatz 402 in the so-called hardware-efficient form. It includes parametrized rotation layers forming an {circumflex over (R)}.sub.z−{circumflex over (R)}.sub.x−{circumflex over (R)}.sub.z pattern, such that arbitrary single qubit rotations can be implemented. Variational angles θ are set for each rotation individually 406, 408. The rotation layer is applied to a qubit initial state |ψ(x)custom-character 404 coming from the preceding quantum circuit, the feature map circuit. The variational rotations may be followed by one or more entangling layers, which may include controlled NOT (CNOT) operations between nearest-neighboring qubits.
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Cong et al. (US 20210383189): teaches processes for chain/concatenating quantum circuits for quantum convolutional neural networks as depicted in Figs. 1A& B
Bondesan et al. (US 20210089955): teaches concatenating quantum circuits as depicted in Figs. 2 and 5, in [0103] The inner loop, includes two steps 502 and 504. In step 502, a three-qubit circuit (or gate) (e.g., U in FIG. 2) is applied to a vector of input data (e.g., 202 in FIG. 2) formed by concatenating As.
Coreas et al. (NPL: Everything You Always Wanted to Know About Quantum Circuits): teaches A qubit is probabilistic in nature. Thus, a qubit is 1 with a probability α and 0 with a probability β. Thus, a qubit can assume an infinite range of values (any point on the surface of the sphere). With gates we can adjust the probability values of a qubit. Measurement is defined as the result of computation of a quantum circuit is read via measurement. Consequently, qubits in superposition are either set to values |0> and |1>. Which of these computational basis states (or eigenstates) appears on the measured qubit shall depend on the associated state probabilities.
Google AI Quantum and Collaborators et al. (NPL: Hartree-Fock on a superconducting qubit quantum computer): teaches the commutator of the Hamiltonian H with respect to any generator of rotation G is zero—hyj½H;Gjyi¼0—and sequential basis change circuits can be concatenated into a single basis change circuit (UaUb=Uab). Thus, teaches concatenated circuit elements as combined/connected elements.
Wang et al. (US 20210264309): teaches concatenation as a connection operation in [0104] In some aspects of the present disclosure, the quantum resource cost reduction may result in between the circuit representation of the elements of the same equivalence class, since the target qubit of the two-qubit gates from each element of the same class is the same. Once some or all the equivalence classes are specified, the subroutine permutes the orderings by which the elements are implemented on a quantum circuit. Since considering all permutations may be prohibitively expensive, the subroutine may implement the greedy approach by starting out with two of the elements that result in the most resource cost reduction. Next, the subroutine concatenates a next element, identified from the set of elements that have not been implemented in the circuit, based on the resource cost reduction. The concatenation process is repeated until no more element is left in the set. In some instances, each trial of testing out which element may be the best for the given iteration may include four cases. The circuit concatenation may be performed as a prefix or suffix, and the element to be concatenated may be considered in its original intra-term order or the reverse.
Ekert et al. (NPL: BASIC CONCEPTS IN QUANTUM COMPUTATION): teaches a qubit is a quantum system in which the Boolean states 0 and 1 are represented by a prescribed pair of normalized and mutually orthogonal labeled quantum states; and a simple concatenation of the Toffoli gate and the c-not gives a simplified quantum adder, as a good starting point for constructing full adders, multipliers and more elaborate networks.
Nguyen et al. (US 20230289501): teaches ansatz is also known as a trail wave function.
Matsuura et al. (US 20210166148): teaches Updating of parameters and solutions may use but may not be limited to one or more optimizers selected from the group consisting of: a Bayesian optimization method, a black-box optimization, a gradient-free optimization, a gradient-based optimization, a first-order or second-order method, a gradient descent method, a stochastic gradient decent method, and an adaptive gradient descent method. Nonlimiting examples of optimizers include: Nelder-Mead methods, Powell methods, constrained optimization by linear approximation (COBYLA), and Broyden-Fletcher-Goldfarb-Shanno (BFGS) methods.
Tavernelli et al. (US 20200342959): teaches the Hamiltonian of each molecule is allowed to operate on the quantum object …That is, the Hamiltonian is allowed to operate on each sets of qubits representing the wavefunction to compute the ground state or minimum energy of that molecule, using the VQE method. In an embodiment, the value of the Euler angle θ is adjusted to change the state of the qubit and thus search for the minimum energy in the molecule in the environment. In an embodiment, an optimizer method is used, for example by using the L-BFGS-B optimizer, to optimize the Euler angle θ used. L-BFGS is a limited memory algorithm based on the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. It is a conventional algorithm for parameter estimation in machine learning. BFGS-B is a variant that handles simple box constraints. After a set number of iterations, the optimizer method 112 returns an optimized state for optimization over the weights of each Hamiltonian by employing the Sequential Least Squares Programming (SLSQP) optimizer, since it specializes on optimizing scalar parameters, such as coefficients in question. SLSQP is a sequential least squares algorithm which uses the Han-Powell quasi-Newton method with a BFGS update of the B-matrix and an L1-test function in the step length algorithm…
Any inquiry concerning this communication or earlier communications from the examiner should be directed to OLUWATOSIN ALABI whose telephone number is (571)272-0516. The examiner can normally be reached Monday-Friday, 8:00am-5:00pm EST.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Huntley can be reached at (303) 297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/OLUWATOSIN ALABI/Primary Examiner, Art Unit 2129
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