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 .
Response to Argument
The arguments filed 09/29/2025 have been entered. Claims 1-28 remain pending in the
application.
Applicant’s arguments, with respect to claim rejections of claims 1-28 under 35 U.S.C 103 filed 04/29/2025 have been considered and are not persuasive. Therefore, the previous rejections as set forth in the previous office action will be maintained.
The applicant first argues that the cited references do not teach the claimed GAN architecture. Applicant asserts that Donahue’s discriminator does not internally have or contain a latent space, nut instead receives latent vectors only as external inputs generated by a separated encoder. Applicant further asserts that Chudak feeds its generator using classical noise priors, not quantum-generated output, and uses the quantum processor to solely generate real target samples for discriminator comparison. Applicant contends that modifying the system to relocate quantum output as generator input would contradict the reference roles and require impermissible hindsight.
Secondly, the applicant argues that the references teach away from the proposed configuration. Applicant asserts that Chudak identifies quantum sampling speed as a drawback and motivates classical ML-based simulation or post-processing rather than using quantum output in a GAN generator training loop. Applicant similarly asserts that Liu criticizes adversarial discriminator training for quantum circuit-born models and instead advocates a two-sample kernel MMD objective that omits both a discriminator and encoder-generator latent coupling. Applicant contends that these express criticisms would steer a POSITAT away from the claimed arrangement.
Thirdly, the applicant argues that the prior art combination would not function and lacks a reasonable expectation of success. Applicant asserts that BiGAN style training depends on a joint min-max objective with gradient coupling through an encoder, generator, and discriminator, whereas Liu employs a discriminator-free MMD optimization path, yielding incompatible training objectives and gradient flows. Applicant contends that forcing these objectives together would break training dynamics and produce a non-operable system. Applicant therefore request withdrawal of the 103 rejections for claims 1, 16 and dependent claims.
Regarding the first argument that Donahue’s discriminator does not internally possess a latent space, the examiner respectfully disagrees. Applicant first argues the examiner maintains the prior art finding under the broadest reasonable interpretation, consistent with the non-final rejection, because Donahue explicitly discloses that “The BiGAN discriminator D discriminates not only in data space (x versus G(z)), but jointly in data and latent space” in Page 2 section 1 and Page 3 section 3 “The discriminator is also modified to take input from the latent space,”. Donahue discloses that the discriminator receives inputs and performing discrimination in both domain data space and latent space, while stablishing that the discriminator is a model trained to make real/fake determinations using latent representations within the adversarial decision path. While the latent vectors may originate from encoder E, Donahue nevertheless defines the discriminator functional operation ion the latent domain itself, which under the broadest reasonable interpretation reasonably corresponds to discriminator interaction with latent feature space during adversarial training, as claimed. Applicant’s argument does not negate that Donahue places the discriminator within the adversarial architecture to process latent representations as part of the discrimination objective, which was the basis of the examiner’s original 103 mapping.
Regarding the second argument that the Office Action fails to provide an articulated reason to combine the cited references, the examiner respectfully disagrees. The examiner maintains that the prior Office Action already set forth a clear technical rationale rooted in reference-role placements and structural compatibility under the broadest reasonable interpretation. The rejection combines Chudak as the primary reference for teaching a GAN architecture where the generator is driven by a classical noise prior and quantum-generated samples are routed as real target data for the discriminator path, with Donahue cited for teaching an adversarial discriminator that explicitly discriminates on latent representations from an encoder as part of the GAN objective, and Liu cited to teach the configuration of the QCBM that take the product state as an input and evolves it to generate samples for generative training. A person of ordinary skilled in the art would have been motivated to combine these teachings because Liu supplies the technical link that Chudak does not expressly articulate – improving the trainability and optimization stability of implicit generative models implemented at the quantum-circuit level when used inside a larger ML sampling workflow through implementation of the QCBM to evolve a quantum state of input sing a sequence of gates. Liu explicitly teaches that evolving a product input state through a parameterized sequence of gates yields bit-samples governed by a Born probability distribution, and further explains that gradient-based optimization of quantum Born machines scales favorably with circuit depth and parameter count while avoiding gradient vanishing or explosion that commonly limits deep classical generative model. Although Liu advocates MMD as a loss objective, the disclosure nonetheless provides the engineering rationale that QCBM remain efficiently optimizable and more stably trainable than deep classical generative networks. Chudak already teaches using machine learning, including GANs to simulate or post-process quantum samples to improve sampling throughput. Liu therefore does not undermine the combination, but rather reinforces why a skilled artisan would reasonably integrate Liu’s quantum gradient-training advantages into Chudak’s GAN sampling pipeline to prevent optimization bottlenecks, stabilize learning in deeper generator models, and improve sampling scalability – which is the exact technical basis the applicant alleges was missing from the Office Action. Accordingly, the combination is supported under the broadest reasonable interpretation, as it represents a predictable use of complementary roles where Liu provides optimization reasoning and Chudak provides the adversarial sampling framework.
Regarding the third argument that the Office Action’s combination as cited would fail to function, the examiner respectfully disagrees. Under the broadest reasonable interpretation and as understood by one person ordinary skilled in the art, Liu does not teach away from GAN-based learning, but rather identifies a known training challenge for implicit generative models and provides concrete, technical insights into improving gradient-based optimization stability and scalability for deeper parameterized generative circuits. Because Liu explicitly addresses gradient stability and scalability using QBCM machines inside a larger ML sampling pipeline, a person ordinary skilled in the art would have been motivated to integrate Liu’s quantum-gradient trainability insight into Chudak’s hybrid GAN sampling architecture, while retaining Donahue’s discriminator role on latent representations. The combination does not require structural re-design of Liu, and difference in loss formulation (MMD vs adversarial metric choice) do not equate to architectural incompatibility. Chudak already teaches Gan-based post-processing of quantum generated samples, establishing the necessary technical insertion, and thus supports operability and a reasonable expectation of success. The merged systems preserve end-to-end differentiability and discriminator gradient propagation, providing predictable convergence behavior and stable training dynamics to a skilled person in the art as understood based on the broadest reasonable interpretation. Therefore, the rejections of the claim under 103 will be maintained.
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.
Claims 1-4, 6-9, 12-20, 23-26 are rejected under 35 U.S.C. 103 as being unpatentable over Chudak et.al (US 20200193272 A1), further in view of Donahue et.al (NPL: Adversarial Feature Learning), further in view of Liu et.al (NPL: Differentiable Learning of Quantum Circuit Born Machine)
Chudak teaches the 1st limitation “a quantum computer comprising a plurality of qubits” (paragraph 61 “The quantum processor can comprise a number of qubits”, and paragraph 124 “Analog computer 350 may include an analog processor, such as quantum processor 340.” Chudak discloses systems, devices, and methods for simulating and post-processing samples generated by a hybrid computing system comprising a quantum computer and a digital computer. Within the disclosure, Chudak discloses an analog quantum computer comprising of a quantum processor which can comprise a number of qubits.)
Chudak teaches the 2nd limitation “a classical computer including a processor, a non-transitory computer-readable medium, and computer instructions stored in the non-transitory computer-readable medium” (paragraph 28 “A processor-based system to computationally efficiently producing sample sets, may be summarized as including: at least one processor; at least one nontransitory processor-readable medium communicatively coupled to the at least one processor and which stores processor executable instructions which, when executed by the at least one processor,” Chudak discloses the system is a processor-based system comprising of a processor, at least one non-transitory processor-readable medium in which computer instructions is stored within.)
Chudak teaches a part of the 3rd limitation “a generator and a discriminator operatively coupled to each other to function as a generative adversarial network (GAN) with neural network architectures for a given dataset, ...” (paragraph 52 “GANs can be useful for approximate model estimations. A GAN can include a generator and a discriminator, both of which can be multilayer perceptrons. In a typical GAN, the generator generates samples from a noise prior distribution that is defined on input noise variables, and the discriminator is trained to determine the probability of whether a sample is from the generator or from a target distribution” Chudak discloses utilizing a GAN model within the system, which comprise of the neural network architecture of a generator and a discriminator.)
Chudak teaches a part of the 4th limitation “a quantum component, operatively coupled to the generator to provide an input to the generator, ...” (paragraph 96 “In one implementation, the first set of biases h and the first set of coupling strengths J can be received from a quantum processor as inputs to the generator and the discriminator of the GAN”, and paragraph 115 “FIG. 3 illustrates a hybrid computing system 300 including a digital computer 305 coupled to an analog computer 350. In some implementations the analog computer 350 is a quantum computer”. Chudak discloses a hybrid computing system with a digital computer coupled to the quantum analog computer, such that the digital computer may be configured by a person ordinary skilled in the art to comprise of the GAN model with the generator such that data from the analog quantum computer can be used as input to the generator of the GAN within the digital computer.)
Chudak teaches the 5th limitation “the computer instructions, when executed by the processor, perform a method for generating, on the hybrid quantum-classical computer, a dataset having a plurality of datapoints, the method comprising” (paragraph 62 “FIG. 1 is a flowchart illustrating a method 100 for training an example GAN using samples generated by a quantum processor ... Method 100 can be performed by, for example, a hybrid computing system including a digital computer and a quantum processor in response to instructions or a program submitted by a user.” Chudak discloses program instructions executed by the processor as recited above to perform the method for training an example GAN using samples generated by a quantum processor, wherein the method can be performed by a hybrid computing system including a digital computer and a quantum processor.)
Chudak teaches the 6th limitation “initializing the sequence of instructions of the quantum component” (paragraph 123 “In some implementations, system memory 320 may store processor- or computer-readable calculation instructions to perform pre-processing, co-processing, and post-processing to analog computer 350”. Chudak discloses instructions to perform the process of the analog computer that comprises of the quantum processor, which include the pre-processing process instruction, suggesting an initializing process of the instructions of the quantum component within the claim.)
Chudak teaches the 7th limitation “initializing the generator and the discriminator of the generative adversarial network (GAN)” (paragraph 95 “At 202, a generator parameter θ and a discriminator parameter ϕ are each initialized”. Chudak discloses initializing a parameter of the generator and the discriminator of the GAN network model.)
Chudak teaches the 8th limitation “training the GAN using the output of the quantum component as an input to the generator of the GAN, wherein the training occurs iteratively in a first phase and a second phase” (paragraph 59 “One approach for adjusting the generator parameter θ and the discriminator parameter ϕ is to use gradient optimization for back-propagation.”, paragraph 93 “In some cases, it can be advantageous to direct at least one of the generator and the discriminator when training a GAN using samples generated by a quantum processor”, paragraph 96 “In one implementation, the first set of biases h and the first set of coupling strengths J can be received from a quantum processor as inputs to the generator and the discriminator of the GAN”, and paragraph 114 “Some of the exemplary acts or operations of the above described method(s), process(es), or technique(s) are performed iteratively.” Chudak discloses training a GAN using samples generated by a quantum processor of the analog quantum computer, such that samples output from the analog quantum computer may be used as input to the generator of the GAN model. The training may be performed iteratively with back-propagation, wherein a person ordinary skilled in the art would recognize that back-propagation comprise of two phases: a forward pass and a backward pass, which occur respectively at each model of generator and discriminator at each training iteration of the GAN.)
Chudak teaches the 9th limitation “wherein, in the first phase, the generator is not updated and the discriminator is updated” (paragraph 111 “The gradient can be updated with a respective step size γϕ for the discriminator parameter ϕ.” Chudak discloses the updating of the gradient for the discriminator parameter at the discriminator, such that a person ordinary skilled in the art would recognize that during back-propagation, a forward/backward pass may be performed on the discriminator such that the gradient is only computed at the discriminator to update the parameter of the discriminator, wherein the update is only performed at the discriminator.)
Chudak teaches the 10th limitation “wherein, in the second phase, the discriminator is not updated and the generator is updated” (paragraph 108 “The gradient can be updated with a respective step size γθ for the generator parameter θ” Chudak discloses the updating of the gradient for the generator parameter at the generator, such that a person ordinary skilled in the art would recognize that during back-propagation, a forward/backward pass may be performed on the generator such that the gradient is only computed at the generator to update the parameter of the generator, wherein the update is only performed at the generator. In other words, the forward/backward pass may be performed sequentially at each generator/discriminator model as each model get its own forward/backward and update step.)
Chudak does not teach a part of the 3rd limitation “the discriminator having a latent space”. However, Donahue teaches this limitation (Page 2 section 1 “Hence, we propose a novel unsupervised feature learning framework, Bidirectional Generative Adversarial Networks (BiGAN)... The BiGAN discriminator D discriminates not only in data space (x versus G(z)), but jointly in data and latent space”, and Page 3 section 3 “The discriminator is also modified to take input from the latent space,”. Donahue discloses a Bidirectional Generative Adversarial Networks (BiGANs) for inverse mapping/projecting data back into the latent space. Within the disclosure, Donahue discloses the discriminator of the BiGANs take input from the latent space to discriminate, suggesting that the discriminator within the network architecture is associated with the latent space containing data samples.)
Before the effective filing date, it would have been obvious to a person ordinary skilled in the art to combine the teaching of systems, devices, and methods for simulating and post-processing samples generated by a hybrid computing system comprising a quantum computer and a digital computer by Chudak with the teaching of Bidirectional Generative Adversarial Networks (BiGANs) for inverse mapping/projecting data back into the latent space by Donahue. The motivation to do so is disclosed in Donahue’s disclosure (page 1-2, section 1 “The GAN framework learns a generator mapping samples from an arbitrary latent distribution to data, as well as an adversarial discriminator which tries to distinguish between real and generated samples as accurately as possible ... the generator maps latent samples to generated data, but the framework does not include an inverse mapping from data to latent representation ... a trained BiGAN encoder may serve as a useful feature representation for related semantic tasks, in the same way that fully supervised visual models trained to predict semantic “labels” given images serve as powerful feature representations for related visual tasks ... Our empirical studies will show that despite their generality, BiGANs are competitive with contemporary approaches to self-supervised and weakly supervised feature learning designed specifically for a notoriously complex data distribution – natural images.” Donahue proposes the BiGANs as an improvement toward the GAN network, such that the BiGANs incorporate an inverse mapping from data to latent representation for training improvement, and the BiGANs provides advancements in generative image models such that the experiment of the BiGANs in visual learning demonstrate its competitive with other contemporary visual feature learning methods, despite its generality. Therefore, a person ordinary skilled in the art may further incorporate the teaching of BiGANs into the GAN model as disclosed by Chudak for further improvement.)
Chudak/Donahue does no teach a part of the 4th limitation “a quantum component, ... which accepts a sequence of instructions to evolve a quantum state based on a series of quantum gates”. However, Liu teaches this limitation (Page 2 section II column 1 “As shown in Fig. 1, the QCBM takes the product state |0i as an input and evolves it to a final state |ψθi by a sequence of unitary gates” Liu discloses an efficient gradient-based learning algorithm for the quantum circuit Born machine. Within the disclosure, Liu discloses the QCBM takes the product state as an input and evolves it by a sequence of unitary gates, wherein these gates suggest quantum gates.)
Before the effective filing date, it would have been obvious to a person ordinary skilled in the art to combine the teaching of systems, devices, and methods for simulating and post-processing samples generated by a hybrid computing system comprising a quantum computer and a digital computer by Chudak, and the teaching of Bidirectional Generative Adversarial Networks (BiGANs) for inverse mapping/projecting data back into the latent space by Donahue with the teaching of an efficient gradient-based learning algorithm for the quantum circuit Born machine by Liu. The motivation to do so is referred to in Liu’s disclosure (Page 3 section II-B column 2 “On the other hand, the unbiased gradient estimator of the QCBM takes advantage of the known structure of the unitary evolution and the MMD loss (see Appendix A), despite that the probability of the outcome is unknown. In this sense, quantum circuits exhibit a clear quantum advantage over classical neural nets since they fill the gap of differentiable learning of implicit generative models of discrete data.”, page 4 section III-A column 2 “Another advantage of using gradient-based learning is the efficiency comparing with gradient-free methods, which gets particularly significant when circuits get deeper and the number of parameters increases”, and page 6 section IV column 1 “Besides possessing stronger representation power, the QCBM does not suffer from the gradient vanishing/exploding problem as circuit depth increases compared to the classical deep neural networks. While compared to other quantum generative models [7, 12], the quantum circuit Born machine has fewer restrictions on hardware and circuit design, while both the training and sampling can be efficiently carried out” Liu discloses several advantages of the QCBM such as exhibiting a clear quantum advantage over classical neural networks, the efficiency of using gradient-based learning as the number of parameters increase, and the QCBM does not suffer from the gradient vanishing problem, as well as fewer restrictions on hardware and circuit design. Therefore, the teaching by Chudak/Donahue may further incorporate the teaching of the QCBM by Liu for further improvement. A person ordinary skilled in the art would have been able to configure such that the analog computer with the quantum processor for samples generating process to incorporate the QCBM machine as part of the analog computer to evolve the state of the dataset for further improvement on training.)
Regarding claim 2 depends on claim 1, thus the rejection of claim 1 is incorporated
Liu teaches the limitation “The system of claim 1, wherein training the GAN further comprises training the quantum component” (Page 3 section II-B column 1 “Viewing the QCBM as an implicit generative model ... we train it by employing the kernel two-sample test ... We refer the following loss function as the squared maximum mean discrepancy (MMD)” Liu discloses the training of the QCBM by viewing it as a generative model and apply a loss function to train the model.)
Regarding claim 3 depends on claim 2, thus the rejection of claim 2 is incorporated
Liu teaches the limitation “The system of claim 2, wherein training the quantum component comprises training the quantum component based on a cost function” (Page 3 section II-B column 1 “Viewing the QCBM as an implicit generative model ... we train it by employing the kernel two-sample test ... We refer the following loss function as the squared maximum mean discrepancy (MMD)” Liu discloses the training of the QCBM by viewing it as a generative model and apply a loss function to train the model, wherein the loss function suggests a cost function as understood by a person ordinary skilled in the art.)
Regarding claim 4 depends on claim 2, thus the rejection of claim 2 is incorporated.
Donahue teaches this limitation (Page 2 section 1 “Hence, we propose a novel unsupervised feature learning framework, Bidirectional Generative Adversarial Networks (BiGAN)... The BiGAN discriminator D discriminates not only in data space (x versus G(z)), but jointly in data and latent space”, and Page 3 section 3 “The discriminator is also modified to take input from the latent space,”. Donahue discloses the discriminator of the BiGANs take input from the latent space to discriminate, suggesting that the discriminator within the network architecture is associated with the latent space containing data samples, such that a person ordinary skilled in the art may incorporate the BiGANs during training of the quantum computer disclosed above to incorporate the inverse mapping of the discriminator associated with the latent space.)
Regarding claim 6 depends on claim 1, thus the rejection of claim 1 is incorporated
Liu teaches the limitation “The system of claim 1, wherein initializing the sequence of instructions of the quantum component comprises evolving the quantum state such that measurements of the quantum state output samples from a desired probability distribution.” (Page 2 section II column 1 “As shown in Fig. 1, the QCBM takes the product state |0i as an input and evolves it to a final state |ψθi by a sequence of unitary gates ... Then we can measure this output state on computation basis to obtain a sample of bits x ∼ pθ(x) ... The goal of the training is to let the model probability distribution pθ approach to π.” Liu discloses evolving the state of the product to an output final state such that a measurement of the final output state can be computed to obtain samples with a probability distribution approach to π as desired.)
Regarding claim 7 depends on claim 6, thus the rejection of claim 6 is incorporated
Chudak teaches the limitation “wherein the desired probability distribution is uniform over a selected range.” (paragraph 64 “At 104, a noise sample zk is drawn from a noise prior distribution r(z). The noise prior distribution can be a fixed distribution. For example, the noise prior distribution can be a uniform distribution ... (i.e., z ∈ [0,1] or z ∈ E (0,1)).” Chudak discloses the analog computer can be operated to provide samples from a probability distribution, wherein the probability distribution in which the samples are drawn from can be a uniform distribution over a range. A person ordinary skilled in the art would have been able to configure such that the probability distribution approach to π as desired as disclosed above by Liu is uniform over a selected range.)
Regarding claim 8 depends on claim 1, thus the rejection of claim 1 is incorporated
Liu teaches the limitation “wherein the quantum component is a quantum circuit born machine (QCBM).” (Page 3 section III column 2 “We carry out numerical experiments by simulating the learning of QCBM on a classical computer” Liu discloses the embodiment is carried out by simulating the QCBM on a classical computer, which suggest that it may be incorporated as part of the analog computer with the quantum processor by Chudak.)
Regarding claim 9 depends on claim 1, thus the rejection of claim 1 is incorporated
Liu teaches the limitation “The system of claim 1, further comprising measuring the quantum component using a multi-basis method.” (Page 2 section II column 1 “...Then we can measure this output state on computation basis to obtain a sample of bits”, and Page 3 section II-B column 2 “In order to estimate the gradient [Eq. (2)] on an actual quantum circuit, one can repeatedly send rotation and entangle pulses to the device according to the circuit parameters θ (±), and then perform projective measurements on the computational basis to collect binary sample”. Liu discloses perform the measurement of the output state of the QCBM on computation basis, wherein the computation basis suggests the multi-basis method as understood by a person ordinary skilled in the art, as the computational basis refers to a fundamental set of orthonormal states used to represent quantum information, often represented as |0⟩ and |1⟩ for a single qubit and are the building blocks for describing any quantum state within a system.)
Regarding claim 12 depends on claim 1, thus the rejection of claim 1 is incorporated.
Donahue teaches the limitation “The system of claim 1, wherein the dataset includes higher-resolution handwritten digits” (Page 7 figure 2 “Qualitative results for permutation-invariant MNIST BiGAN training, including generator samples G(z), real data x, and corresponding reconstructions G(E(x))” Donahue discloses using permutation-invariant MNIST test data set as illustrated in figure 2 which comprise of a higher-resolution handwritten digits after the training of the BiGAN model.)
Regarding claim 13 depends on claim 1, thus the rejection of claim 1 is incorporated.
Donahue teaches the limitation “The system of claim 1, wherein the dataset includes monochrome images and color images” (Page 8 figure 4 “Figure 4: Qualitative results for ImageNet BiGAN training, including generator samples G(z), real data x, and corresponding reconstructions G(E(x)).” Donahue discloses using ImageNet data et as illustrated in figure 4 which comprise of monochrome images and color images for the training of the BiGAN model.)
Regarding claim 14 depends on claim 1, thus the rejection of claim 1 is incorporated.
Donahue teaches the limitation “The system of claim 1, wherein the dataset includes video frames” (Page 7-8 figure 4 “Next, we present results from training BiGANs on ImageNet LSVRC (Russakovsky et al., 2015), a large-scale database of natural images.” Donahue discloses results from training the BiGAN on ImageNet LSVRC, which is a large scale database of natural images, wherein a person ordinary skilled in the art would recognize that this database of images comprise of video frame such that the training result in Figure 4 may comprise of video frame as well.)
Regarding claim 15 depends on claim 1, thus the rejection of claim 1 is incorporated
Liu teaches the limitation “The system of claim 1, wherein the plurality of qubits includes at least 8-qubits.” (Page 3 section III-A column 1 “We first train a QCBM on the Bars-and-Stripes dataset ... We model the pixels with a quantum circuit of 9 qubits.” Liu discloses training a QCBM and using a quantum circuit of 9 qubits.)
Regarding claim 16, the applicant is further directed to the rejection of claim 1 above, because claim 16 comprise of similar limitations to claim 1, thus they are rejected based on the same rationale.
Regarding claim 17, depends on claim 16, thus the rejection of claim 16 is incorporated. The applicant is further directed to the rejection of claim 2 above, because claim 17 comprise of similar limitations to claim 2, thus they are rejected based on the same rationale.
Regarding claim 18, depends on claim 17, thus the rejection of claim 17 is incorporated. The applicant is further directed to the rejection of claim 3 above, because claim 18 comprise of similar limitations to claim 3, thus they are rejected based on the same rationale.
Regarding claim 19, depends on claim 16, thus the rejection of claim 16 is incorporated. The applicant is further directed to the rejection of claim 8 above, because claim 19 comprise of similar limitations to claim 8, thus they are rejected based on the same rationale.
Regarding claim 20, depends on claim 16, thus the rejection of claim 16 is incorporated. The applicant is further directed to the rejection of claim 9 above, because claim 9 comprise of similar limitations to claim 9, thus they are rejected based on the same rationale.
Regarding claim 23, depends on claim 16, thus the rejection of claim 16 is incorporated. The applicant is further directed to the rejection of claim 12 above, because claim 12 comprise of similar limitations to claim 23, thus they are rejected based on the same rationale.
Regarding claim 24, depends on claim 16, thus the rejection of claim 16 is incorporated. The applicant is further directed to the rejection of claim 13 above, because claim 13 comprise of similar limitations to claim 24, thus they are rejected based on the same rationale.
Regarding claim 25, depends on claim 16, thus the rejection of claim 16 is incorporated. The applicant is further directed to the rejection of claim 14 above, because claim 14 comprise of similar limitations to claim 25, thus they are rejected based on the same rationale.
Regarding claim 26, depends on claim 16, thus the rejection of claim 16 is incorporated. The applicant is further directed to the rejection of claim 7 above, because claim 7 comprise of similar limitations to claim 26, thus they are rejected based on the same rationale.
Claims 5, 11, 28 are rejected under 35 U.S.C. 103 as being unpatentable over Chudak et.al (US 20200193272 A1), further in view of Donahue et.al (NPL: Adversarial Feature Learning), further in view of Liu et.al (NPL: Differentiable Learning of Quantum Circuit Born Machine), further in view of Spinner et.al (NPL: Towards an Interpretable Latent Space : an Intuitive Comparison of Autoencoders with Variational Autoencoders)
Regarding claim 5 depends on claim 1, thus the rejection of claim 1 is incorporated
Chudak/Donahue/Liu does not teach the limitation “The system of claim 1, wherein the latent space contains a layer of neurons equal in number to the size of the input of the generator”. However, Spinner teaches this limitation (“Figure 1 shows the schematical layout of an autoencoder (AE). It consists of two parts, the encoder and the decoder. The encoder decreases the dimensionality of the input up to the layer with the fewest neurons, called latent space. The decoder then tries to reconstruct the input from this low-dimensional representation. This way, the latent space forms a bottleneck, which forces the autoencoder to learn an effective compression of the data.” Spinner discloses the schematical layout of an autoencoder such that the dimensionality of the input is decreased to match with the latent space, wherein the latent space is constructed as a hidden layer of neurons, such that the number of neurons is equal to the reduced size of the input through the encoder as illustrated in Figure 1. A person ordinary skilled in that art would have been able to construct the latent space as a layer with a number of neurons as desired as well as increase the neuron to increase the latent space size.)
Before the effective filing date, it would have been obvious to a person ordinary skilled in the art to combine the teaching of systems, devices, and methods for simulating and post-processing samples generated by a hybrid computing system comprising a quantum computer and a digital computer, the teaching of Bidirectional Generative Adversarial Networks (BiGANs) for inverse mapping/projecting data back into the latent space, and the teaching of an efficient gradient-based learning algorithm for the quantum circuit Born machine by Chudka/Donahue/Liu with the teaching of an autoencoders to compress data by Spinner. The motivation to do so is referred to in Spinner’s disclosure (“The capability to compress data can be used for a variety of tasks and different types of data: Vincent et al. train an AE to remove noise from images, whereas Theis et al. present a lossy image compression algorithm based on AEs. Artetxe et al. use an AE for machine translation.” Spinner discloses the benefit of the autoencoder to encode and decode data for benefits such as removing noise from images data or image compression. A person ordinary skilled in the art would have been able to incorporate an autoencoder to compress data to match the latent space and configure the latent space as a hidden layer within the neural network, such that the discriminator as disclosed by Donahue may take as input the output of the encoder passing through the latent space process by Spinner to discriminate.)
Regarding claim 11 depends on claim 5, thus the rejection of claim 5 is incorporated.
Liu teaches the limitation “The system of claim 5, wherein training the quantum component further comprises the measuring a loss function for the quantum component explicitly measured” (Page 3 section II-B column 1 “Viewing the QCBM as an implicit generative model ... we train it by employing the kernel two-sample test ... We refer the following loss function as the squared maximum mean discrepancy (MMD)” Liu discloses using the loss function to train the QCBM.)
Regarding claim 28 depends on claim 16, thus the rejection of claim 16 is incorporated. The applicant is further directed to the rejection of claim 9 and claim 5 above, because claim 28 comprise of similar limitations to claim 9 and 5, thus they are rejected based on the same rationale.
Claims 10, 21, 27 are rejected under 35 U.S.C. 103 as being unpatentable over Chudak et.al (US 20200193272 A1), further in view of Donahue et.al (NPL: Adversarial Feature Learning), further in view of Liu et.al (NPL: Differentiable Learning of Quantum Circuit Born Machine), further in view of Shehab et.al (US 20200372390 A1)
Regarding claim 10 depends on claim 1, thus the rejection of claim 1 is incorporated
Chudak/Donahue/Liu does not teach the limitation “The system of claim 1, wherein the quantum component comprises a trapped-ion quantum device”. However, Shehab teaches this limitation (paragraph 0006 “For solving some optimization problems, a NISQ device having shallow circuits (with small number of gate operations to be executed in time-sequence) can be used in combination with a classical computer (referred to as a hybrid quantum-classical computing system... The classical computer (also referred to as a “classical optimizer”) instructs a controller to prepare the NISQ device (also referred to as a “quantum processor”) in an N-qubit state, execute quantum gate operations, and measure an outcome of the quantum processor.”, and paragraph 23 “The quantum processor includes trapped ions that are coupled with various hardware, including lasers to manipulate internal hyperfine states (qubit states) of the trapped ions and an acousto-optic modulator to read-out the internal hyperfine states (qubit states) of the trapped ions.” Shehab discloses the incorporation of an NISQ device for many problems, wherein the device can be referred to as a quantum processor. The quantum processor includes trapped ions that are coupled with various hardware.)
Before the effective filing date, it would have been obvious to a person ordinary skilled in the art to combine the teaching of systems, devices, and methods for simulating and post-processing samples generated by a hybrid computing system comprising a quantum computer and a digital computer, the teaching of Bidirectional Generative Adversarial Networks (BiGANs) for inverse mapping/projecting data back into the latent space, and the teaching of an efficient gradient-based learning algorithm for the quantum circuit Born machine by Chudka/Donahue/Liu with the teaching of Noise reduced circuits for trapped-ion quantum computers by Shehab. The motivation to do so is referred to in Shehab’s disclosure (paragraph 0006 “For solving some optimization problems, a NISQ device having shallow circuits ... The classical computer (also referred to as a “classical optimizer”) instructs a controller to prepare the NISQ device (also referred to as a “quantum processor”) in an N-qubit state, execute quantum gate operations, and measure an outcome of the quantum processor.” Shehab discloses the NISQ device may be referred to as a quantum processor, wherein Chudak also discloses a quantum processor of the analog computer. Therefore, a person ordinary skilled in the art would have been able to recognize that the NISQ device as referred to in Shehab is similar to the device in Chudak and the embodiment of the NISQ device by Shehab may be applied to the quantum processor as disclosed in Chudak and the teaching combination. Shehab further discloses improvement of using a hybrid quantum-classical computing system at paragraph 44 “This hybrid quantum-classical computing system has at least the following advantages. First, an initial guess is derived from a classical computer, and thus the initial guess does not need to be constructed in a quantum processor that may not be reliable due to inherent and unwanted noise in the system. Second, a quantum processor performs a small-sized (e.g., between a hundred qubits an a few thousand qubits) but accelerated operation (that can be performed using a small number of quantum logic gates) between an input of a guess from the classical computer and a measurement of a resulting state, and thus a NISQ device can execute the operation without accumulating errors. Thus, the hybrid quantum-classical computing system may allow challenging problems to be solved, such as small but challenging combinatorial optimization problems, which are not practically feasible on classical computers, or suggest ways to speed up the computation with respect to the results that would be achieved using the best known classical algorithm”, thus the teaching combination may further improve upon the teaching by Shehab.)
Regarding claim 21 depends on claim 16, thus the rejection of claim 16 is incorporated. The applicant is further directed to the rejection of claim 10 above, because claim 21 comprise of similar limitations to claim 10, thus they are rejected based on the same rationale.
Regarding claim 27 depends on claim 16, thus the rejection of claim 16 is incorporated
Chudak/Donahue/Liu does not teach the limitation “The method of claim 16, wherein the quantum component is a noisy intermediate-scale (NISQ) device” However, Shehab teaches this limitation (paragraph 0006 “For solving some optimization problems, a NISQ device having shallow circuits (with small number of gate operations to be executed in time-sequence) can be used in combination with a classical computer (referred to as a hybrid quantum-classical computing system... The classical computer (also referred to as a “classical optimizer”) instructs a controller to prepare the NISQ device (also referred to as a “quantum processor”) in an N-qubit state, execute quantum gate operations, and measure an outcome of the quantum processor.”, Shehab discloses the NISQ device for many problems, wherein the device can be referred to as a quantum processor.)
The motivation to combine the teaching combination with the teaching by Shehab is similar to the motivation as recited in claim 10.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Chudak et.al (US 20200193272 A1), further in view of Donahue et.al (NPL: Adversarial Feature Learning), further in view of Liu et.al (NPL: Differentiable Learning of Quantum Circuit Born Machine), further in view of Dallaire-Demers et.al (NPL: Quantum generative adversarial networks)
Regarding claim 22 depends on claim 16, thus the rejection of claim 16 is incorporated
Chudak/Donahue/Liu does not teach the limitation “The method of claim 16, wherein a QC – AAN framework is used for the quantum component”. However, Dallaire-Demers teaches this limitation (Page 1 “In this work and a companion paper, we extend adversarial training to the quantum domain and show how to construct generative adversarial networks using quantum circuits.”, Dallaire-Demers discloses an embodiment of constructing a generative adversarial network using quantum circuits, which suggest a QC – AAN framework.)
Before the effective filing date, it would have been obvious to a person ordinary skilled in the art to combine the teaching of systems, devices, and methods for simulating and post-processing samples generated by a hybrid computing system comprising a quantum computer and a digital computer, the teaching of Bidirectional Generative Adversarial Networks (BiGANs) for inverse mapping/projecting data back into the latent space, and the teaching of an efficient gradient-based learning algorithm for the quantum circuit Born machine by Chudka/Donahue/Liu with the teaching of quantum generative adversarial networks by Dallaire-Demers. The motivation to do so is referred to in Dallaire-Demers’s disclosure (Page 8 “It is expected that QuGANs will have a more versatile representation power than their classical counterpart. For example, one can speculate that a large enough QuGAN could learn to generate encrypted data labeled by RSA public encryption key ... In this work, we have explored the practical issues of QuGANs, namely, explicit quantum circuits for the generator and discriminator, as well as quantum methods for computing the gradients of these circuits” Dallaire-Demers discloses how to construct a generative adversarial network using quantum circuits, as well as its improvement over the conventional GAN by employing the quantum circuit. Therefore, a person ordinary skilled in the art may incorporate the teaching of Dallaire-Demers into the teaching combination for further improvement.)
Conclusion
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/DUY T DIEP/Examiner, Art Unit 2123
/ALEXEY SHMATOV/Supervisory Patent Examiner, Art Unit 2123
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