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 .
Status of Claims
Claims 1-16 and 18-25 are pending and are examined herein.
Claims 1-16 and 18-22 are rejected under 35 USC 112(b).
Claims 1-16 and 18-25 are rejected under 35 USC 103.
Response to Arguments
Applicant’s arguments filed 04/24/2026 have been fully considered, but are moot in view of the new grounds of rejection necessitated by amendment.
Information Disclosure Statement
The attached information disclosure statement(s) (IDS) is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the attached information disclosure statement(s) is/are being considered by the examiner.
Claim Rejections - 35 USC § 112(b)
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-16 and 18-22 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites “said computational platform” in step (b); however, this limitation lacks proper antecedent basis. For the purposes of examination, this limitation is being interpreted as “a
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-3, 10, 14, and 18-21 are rejected under 35 U.S.C. 103 as being unpatentable over Carrasquilla (Reconstructing quantum states with generative models, ArXiv version dated 24 October 2018) in view of Yoshioka (Constructing neural stationary states for open quantum many-body systems), further in view of Caruana (Multitask Learning).
Regarding claim 1, Carrasquilla teaches
(a) receiving a plurality of measurements of a quantum state from a quantum device; (Carrasquilla, Page 5, second full paragraph. E.g., “Given a collection of experimental measurement outcomes E = {a1,a2,...,aNs}, with Ns samples, our strategy to infer the state of the system begins with learning a model Pmodel(a) that describes the measurement statistics in terms of expressive neural generative models.” The experimental measurement outcomes are necessarily received at least from the experimental device.)
(b) training, at said computational platform, a neural network to prepare a representation of said quantum state with said plurality of measurements, wherein said training comprises adjusting one or more tunable parameters of said neural network using a cost function, and (Carrasquilla, Pages 6-7 describe training an RBM (a type of neural network) to produce Pmodel(a), which is a representation of the quantum state P(a) which represents measurement outcomes of the quantum state ρ as described on page 5, first full paragraph. As the measurement outcomes P(a) is a representation of ρ, Pmodel(a) is also a representation of the quantum state ρ. The parameterization of the RBM including the weights/parameters is described in Appendix A, section 1 on pages 21-22. Several cost functions used to train the model are described on page 7 (i.e., KL divergence, classical divergence or quantum divergence are all discussed as options). Training a neural network using a cost function means determining parameters so as to decrease the cost function.)
Carrasquilla does not appear to explicitly teach
(c) variationally improving said one or more tunable parameters corresponding to said representation of said quantum state using a loss function different from said cost function, wherein said loss function is constructed using said observable of said quantum state, and wherein said variationally improving educes an error in said estimation of said observable of said quantum state.
However, Yoshioka—directed to analogous art--teaches
A method for reducing an error in an estimation of an observable of a quantum state, the method comprising: ...(c) variationally improving said one or more tunable parameters corresponding to said representation of said quantum state using a loss function ..., wherein said loss function is constructed using said observable of said quantum state, and wherein said variationally improving reduces an error in said estimation of said observable of said quantum state. (Yoshioka, Abstract describes using an RBM to simulate a state of a quantum system. Page 2, section II, first paragraph indicates that this is performed using VMC (variational monte carlo) to optimize for the cost function (which would correspond to the “loss function” in the language of the claim) which is the expectation value of L†L which corresponds to the zero-energy eigenstate. In this case, the energy is the observable of the quantum state. This is described in more detail with respect to the RBM specifically in section III on pages 3-4. Note that the cost function considered by Yoshioka is different from the cost functions considered by Carrasquilla, although neither reference teaches both cost functions.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Carrasquilla by Yoshioka because both are directed to reconstructing a representation of a quantum state using a neural network and because the technique taught by Yoshioka allows for efficient simulation as described by Yoshioka in the Introduction.
The combination of Carrasquilla and Yoshioka does not appear to explicitly teach
a loss function different from said cost function
To clarify, what Carrasquilla and Yoshioka do not teach is using both functions to train the neural network.
However, Caruana—directed to analogous art--teaches
a loss function different from said cost function (Caruana, Abstract and section 1.3. describe using a neural network with the same inputs and hidden layers to perform different tasks and training these in parallel. In the context of Carrasquilla and Yoshioka, both teach using a neural network to model a quantum state and training these networks based on cost/loss functions for different measurements of the quantum state. In the language of multi-task learning, training the neural network on the different measurements corresponds to training the network to perform different tasks.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Carrasquilla and Yoshioka because “Multitask Learning is an approach to inductive transfer that improves generalization by using the domain information contained in the training signals of related tasks as an inductive bias. It does this by learning tasks in parallel while using a shared representation; what is learned for each task can help other tasks be learned better” (Caruana, Abstract). That is, training the network to perform multiple tasks can help the network to perform better on each individual task.
Regarding claim 2, the rejection of claim 1 is incorporated herein. Furthermore, Yoshioka teaches
wherein (c) comprises performing a variational Monte Carlo procedure. (Yoshioka, Abstract describes using an RBM to simulate a state of a quantum system. Page 2, section II, first paragraph indicates that this is performed using VMC (variational Monte Carlo) to optimize for the cost function which is the expectation value of L†L which corresponds to the zero-energy eigenstate. This is described in more detail with respect to the RBM specifically in section III on pages 3-4.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 1.
Regarding claim 3, the rejection of claim 2 is incorporated herein. Furthermore, Yoshioka teaches
wherein said variational Monte Carlo procedure comprises one or more neural networks that are representative of a wavefunction, wherein said wavefunction is an ansatz ground state wavefunction, a tensor network ansatz, a Jastrow wavefunction, or a Hartree-Fock wavefunction. (Yoshioka, Abstract describes using an RBM to simulate a state of a quantum system. Page 2, section II, first paragraph indicates that this is performed using VMC (variational Monte Carlo) to optimize for the cost function which is the expectation value of L†L which corresponds to the zero-energy eigenstate (i.e., ansatz ground state wavefunction). This is described in more detail with respect to the RBM specifically in section III on pages 3-4.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 1.
Regarding claim 10, the rejection of claim 1 is incorporated herein. Furthermore, Carrasquilla teaches
wherein said quantum state comprises a ground state of a Hamiltonian. (Carrasquilla, page 9, last paragraph.)
Regarding claim 14, the rejection of claim 1 is incorporated herein. Furthermore, Carrasquilla teaches
wherein (b) comprises performing a variational quantum computing procedure. (Carrasquilla, page 12, first paragraph. E.g., “Thus, our ansatz may prove applicable in the study of ground and thermal states of quantum many-body systems via variational energy minimization.”)
Regarding claim 18, the rejection of claim 1 is incorporated herein. Furthermore, Yoshioka teaches
wherein said observable of said quantum state is an expected energy of said quantum state. (Yoshioka, Abstract describes using an RBM to simulate a state of a quantum system. Page 2, section II, first paragraph indicates that this is performed using VMC (variational monte carlo) to optimize for the cost function (which would correspond to the “loss function” in the language of the claim) which is the expectation value of L†L which corresponds to the zero-energy eigenstate. In this case, the energy is the observable of the quantum state.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 1.
Regarding claim 19, the rejection of claim 1 is incorporated herein. Furthermore, Carrasquilla teaches
wherein said neural network comprises at least one of an autoregressive model, a recurrent neural network (Carrasquilla, Appendix A.2, starting page 22.) , a transformer, an autoregressive generative model, an attention-based architecture, a dense deep neural network, a convolutional neural network, a variational autoencoder (Carrasquilla, page 5, last paragraph), a generative adversarial network(Carrasquilla, page 5, last paragraph), a restricted Boltzmann machine (Carrasquilla, page 5, last paragraph), a general Boltzmann machine (Carrasquilla, page 5, last paragraph, noting that an RBM is a species of the genus of general Boltzmann machines), an energy-based model (Carrasquilla, page 5, last paragraph, noting that RBMs are energy based models), an invertible neural network, and a flow-based generative model.
Regarding claim 20, the rejection of claim 1 is incorporated herein. Furthermore, Carrasquilla teaches
wherein said quantum state is of a parametrized Hamiltonian, further wherein a parametrization of said parametrized Hamiltonian is continuous. (Carrasquilla, page 9, last paragraph)
Regarding claim 21, the rejection of claim 20 is incorporated herein. Carrasquilla does not appear to explicitly teach
wherein said neural network is configured to further receive a parameter value of said parameterization as an input.
However, Yoshioka—directed to analogous art--teaches
wherein said neural network is configured to further receive a parameter value of said parameterization as an input. (Yoshioka, Figure 1(c) on page 2 and first paragraph of section III on page 3, and also first paragraph of section IV.B on page 4. Note that teaching of both Carrasquilla and Yoshioka encompass using an Ising model.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 1.
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Carrasquilla (Reconstructing quantum states with generative models, ArXiv version dated 24 October 2018) in view of Yoshioka (Constructing neural stationary states for open quantum many-body systems), further in view of Caruana (Multitask Learning), and further in view of “Onishi” (US 2021/0375403 A1).
Regarding claim 4, the rejection of claim 1 is incorporated herein. Carrasquilla does not appear to explicitly teach
further comprising prior to (a) receiving an indication of said observable of said quantum state to be estimated at an interface of a digital computer; and subsequent to (c) providing said estimation of said observable of said quantum state at an interface.
However, Onishi—directed to analogous art--teaches
further comprising prior to (a) receiving an indication of said observable of said quantum state to be estimated at an interface of a digital computer; and subsequent to (c) providing said estimation of said observable of said quantum state at an interface. (Onishi, [0050, 0081] describes a user providing a desired physical property value to be estimated and, subsequent to intermediate processing, receiving an output of an estimate of the physical property.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination applied to claim 1 by Onishi because the interface would allow for a user to specify properties which are to be estimated and to receive output as described by Onishi at [0050, 0081].
Claims 5-7 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Carrasquilla (Reconstructing quantum states with generative models, ArXiv version dated 24 October 2018) in view of Yoshioka (Constructing neural stationary states for open quantum many-body systems), further in view of Caruana (Multitask Learning), and further in view of Goodfellow (Deep Learning, Chapters 16 and 18).
Regarding claim 5, the rejection of claim 1 is incorporated herein. Furthermore, Carrasquilla teaches steps (a) and (b) as explained with respect to claim 1. Carrasquilla, page 22, first paragraph indicates that the RBMs are trained using standard block Gibbs sampling and identifies Goodfellow (see below) as providing details of the training algorithm.
Furthermore, Yoshioka teaches
further comprising repeating ...(c) until a stopping criterion is met. (Yoshioka, page 7, Figure 7 indicates that the VMC calculation is performed for 1500 iterations in which case the stopping criterion is simply a number of iterations.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 1.
Carrasquilla does not appear to explicitly teach
repeating (a) [and (b)] until a stopping criterion is met
However, Goodfellow—as used in the context of Carrasquilla--teaches
repeating (a) [and (b)] until a stopping criterion is met (Goodfellow, section 18.2 includes three algorithms 18.1, 18.2 and 18.3, for training an RBM that include taking samples from the training set, computing a value of the RBM (i.e., p~(x, θ)), applying a contrastive divergence, computing the gradient, and using the gradient to update the tunable parameters and repeating until convergence is reached. In the context of Carrasquilla, the iterative process taught by Goodfellow would require iterating steps (a) and (b) mapped by Carrasquilla.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Carrasquilla by Goodfellow because Carrasquilla indicates on page 22, last sentence of first paragraph, that this is the method used to train the RBMs.
Regarding claim 6, the rejection of claim 1 is incorporated herein. Furthermore, Carrasquilla teaches
further comprising prior to (a) receiving an indication of a set of measurement operators; and wherein (a) further comprises,...: (i) experimentally preparing an approximation of said quantum state; (ii) selecting a measurement operator from said set of measurement operators; and (iii) performing a measurement of said approximation of said quantum state prepared in (i) using said measurement operator from said set of measurement operators selected in (ii). (Carrasquilla, Figure 4, caption describes preparing an approximation of the quantum state and repeatedly measuring the 1-body and 2-body correlators. In this case, the method which implements the technique described in Figure 4 necessarily takes as input a specification of the operators, selects the operator to measure (note that they cannot be performed simultaneously and even a simple technique such as alternating the measurements is a way of selecting the measurement to perform). Page 22, first paragraph indicates that the RBMs are trained using standard block Gibbs sampling, which a person of ordinary skill in the art would know is an iterative method that obtains samples and uses them to update the weights of the model. This is necessarily performed until the training is stopped. Whatever mechanism determines whether the training stops is a stopping criterion. While the particular example of Figure 4 is described with respect to RNNs, page 5 last paragraph indicates that the techniques may be applied with a variety of different models, so a person of ordinary skill in the art would understand that the examples are illustrative rather than limiting.)
Carrasquilla does not appear to explicitly teach
until a stopping criterion is met
However, Goodfellow—directed to analogous art--teaches
until a stopping criterion is met (Goodfellow, section 18.2 includes three algorithms 18.1, 18.2 and 18.3, for training an RBM that include taking samples from the training set, computing a value of the RBM (i.e., p~(x, θ)), applying a contrastive divergence, computing the gradient, and using the gradient to update the tunable parameters and repeating until convergence is reached.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 6.
Regarding claim 7, the rejection of claim 1 is incorporated herein. Furthermore, Carrasquilla teaches
wherein (b) comprises: (i) using said plurality of measurements to provide an input to said neural network; (ii) computing a value of said cost function; (Carrasquilla, page 6, last paragraph continuing onto page 7 describes the cost function used to train the network. This is computed based on PRBM(a), which is the output of the RBM when applied to the observation a (see page 5, last paragraph for the description of the measurements.)
Carrasquilla does not appear to explicitly teach
(iii) computing a gradient of said cost function with respect to said one or more tunable parameters of said neural network;
(iv) using said gradient and said cost function to update said one or more tunable parameters of said neural network; and
(v) repeating (i) – (iv) any number of times.
However, Goodfellow—directed to analogous art--teaches
(iii) computing a gradient of said cost function with respect to said one or more tunable parameters of said neural network; (iv) using said gradient and said cost function to update said one or more tunable parameters of said neural network; and (v) repeating (i) – (iv) any number of times. (Goodfellow, section 18.2 includes three algorithms 18.1, 18.2 and 18.3, for training an RBM that include taking samples from the training set, computing a value of the RBM (i.e., p~(x, θ)), applying a contrastive divergence, computing the gradient, and using the gradient to update the tunable parameters and repeating until convergence is reached.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Carrasquilla by Goodfellow because Carrasquilla indicates on page 22, last sentence of first paragraph, that this is the method used to train the RBMs.
Regarding claim 9, the rejection of claim 6 is incorporated herein. Furthermore, Carrasquilla teaches
wherein said quantum experiment comprises one or more of a quantum computation (Carrasquilla, Page 12, last paragraph, indicates that an application of the technique is to quantum computation, in which the measurements would be for a quantum computation.), a circuit model quantum computation, a quantum annealing measurement-based quantum computation, and an adiabatic quantum computing.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Carrasquilla (Reconstructing quantum states with generative models, ArXiv version dated 24 October 2018) in view of Yoshioka (Constructing neural stationary states for open quantum many-body systems), further in view of Caruana (Multitask Learning), and further in view of Yang (Artificial Neural Networks Applied as Molecular Wave Function Solvers).
Regarding claim 8, the rejection of claim 3 is incorporated herein. The combination of Carrasquilla, Yoshioka and Caruana does not appear to explicitly teach
wherein (c) further comprises: (i) using said neural network to sample at least one configuration;
(ii) using said at least one configuration to estimate a variational energy of said wavefunction represented by a mean of a local energy;
(iii) using said at least one configuration to estimate a gradient of said variational energy with respect to said one or more tunable parameters of said neural network;
(iv) using said variational energy and said gradient of said variational energy to update said one or more tunable parameters of said neural network;
(v) repeating (i)-(iv) until a stopping criterion is met.
However, Yang—directed to analogous art—teaches
wherein (c) further comprises: (i) using said neural network to sample at least one configuration; (Yang, Abstract describes using a Boltzmann Machine to encode wavefunctions. The training is described in section 2.2, starting on page 3516. In particular, page 3516, right hand column, paragraph following equation (24) indicates that samples are taken from the distribution. The probability distribution from which the configurations are sampled is modeled by a Boltzmann machine as described in section 2.1.)
(ii) using said at least one configuration to estimate a variational energy of said wavefunction represented by a mean of a local energy; (Yang, page 3516, right hand column equation (24) and preceding paragraph indicate that the observation may be an expectation (i.e., mean) of a local energy. See also equation (26) and surrounding paragraph.)
(iii) using said at least one configuration to estimate a gradient of said variational energy with respect to said one or more tunable parameters of said neural network; (iv) using said variational energy and said gradient of said variational energy to update said one or more tunable parameters of said neural network; (v) repeating (i)-(iv) until a stopping criterion is met. (Yang, page 3516, right hand column, last paragraph determines the gradient of the energy with respect to the variational parameters θ, performing the update (i.e., equation (27)) and iterating until the expectation value converges (i.e., a stopping criterion is met).)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified the combination of Carrasquilla, Yoshioka and Caruana by Yang because the techniques taught therein were shown to be promising as described in the last paragraph of the conclusion of Yang.
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over Carrasquilla (Reconstructing quantum states with generative models, ArXiv version dated 24 October 2018) in view of Yoshioka (Constructing neural stationary states for open quantum many-body systems), further in view of Caruana (Multitask Learning), further in view of Goodfellow (Deep Learning, Chapters 16 and 18), and further in view of “Pastorello” (Quantum Annealing Learning Search for solving QUBO problems, arXiv:1810.09342v3).
Regarding claim 11, the rejection of claim 9 is incorporated herein. Furthermore, Carrasquilla teaches
further wherein said quantum state comprises a ground state of a Hamiltonian. (Carrasquilla, page 9, last paragraph.)
Carrasquilla does not appear to explicitly teach
wherein said quantum computation comprises solving an optimization problem; and
However, Pastorello—directed to analogous art--teaches
wherein said quantum computation comprises solving an optimization problem; and (Pastorello, Abstract and Introduction describe solving an optimization problem (in particular QUBOs) by identifying a problem Hamiltonian whose ground state represents a solution to the optimization problem.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Carrasquilla, Yoshioka, and Caruana by Pastorello because doing so allows for the solution of QUBO (quadratic unconstrained binary optimization) problems as described by Pastorello in the Abstract.
Regarding claim 12, the rejection of claim 11 is incorporated herein. Carrasquilla does not appear to explicitly teach
wherein said Hamiltonian is representative of a classical optimization problem.
However, Pastorello—directed to analogous art--teaches
wherein said Hamiltonian is representative of a classical optimization problem. (Pastorello, Abstract and Introduction describe solving an optimization problem (in particular QUBOs) by identifying a problem Hamiltonian whose ground state represents a solution to the optimization problem. QUBOs are a classical optimization problem.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 11.
Regarding claim 13, the rejection of claim 11 is incorporated herein. Carrasquilla does not appear to explicitly teach
wherein said ground state of said Hamiltonian is representative of an optimal solution of said optimization problem.
However, Pastorello—directed to analogous art--teaches
wherein said ground state of said Hamiltonian is representative of an optimal solution of said optimization problem. (Pastorello, Abstract and Introduction describe solving an optimization problem (in particular QUBOs) by identifying a problem Hamiltonian whose ground state represents a solution to the optimization problem. QUBOs are a classical optimization problem.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 11.
Claims 15-16 are rejected under 35 U.S.C. 103 as being unpatentable over Carrasquilla (Reconstructing quantum states with generative models, ArXiv version dated 24 October 2018) in view of Yoshioka (Constructing neural stationary states for open quantum many-body systems), further in view of Caruana (Multitask Learning), further in view of Goodfellow (Deep Learning, Chapters 16 and 18), and further in view of “Whitfield” (Simulation of Electronic Structure Hamiltonians Using Quantum Computers, ArXiv: 1001.3855v3).
Regarding claim 15, the rejection of claim 9 is incorporated herein. Carrasquilla does not appear to explicitly teach
However, Whitfield—directed to analogous art—teaches
wherein said quantum computation comprises a quantum chemistry simulation; and wherein said quantum state is of a Hamiltonian representative of a quantum chemistry problem. (Whitfield, Abstract and Introduction describe simulating the electronic structure of a molecule by simulating the chemical Hamiltonian of the molecule using a quantum computer. An overview of the algorithm is given in section 2.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Carrasquilla by Whitfield because “Since the dynamics are simulated by a quantum system rather than calculated by a classical system, quantum simulation often offers exponential advantage over classical simulation for calculation of electronic energies [7], reaction rates [8, 9], correlation functions [10] and molecular properties [11].” See Whitfield, Introduction.
Regarding claim 16, the rejection of claim 15 is incorporated herein. Furthermore, Whitfield teaches
wherein said Hamiltonian comprises electronic structure Hamiltonian of one of a molecule and material. (Whitfield, Abstract and Introduction describe simulating the electronic structure of a molecule by simulating the chemical Hamiltonian of the molecule using a quantum computer. An overview of the algorithm is given in section 2.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 15.
Claim 22 is rejected under 35 U.S.C. 103 as being unpatentable over Carrasquilla (Reconstructing quantum states with generative models, ArXiv version dated 24 October 2018) in view of Yoshioka (Constructing neural stationary states for open quantum many-body systems), further in view of Caruana (Multitask Learning), and further in view of “Carolan” (US 2020/0372334 A1).
Regarding claim 22, the rejection of claim 20 is incorporated herein. The combination of Carrasquilla, Yoshioka, and Caruana does not appear to explicitly teach
further comprising providing said estimation of said observable of said quantum state using a neural network inference for estimation of a property of a quantum state of said parametrized Hamiltonian with a second parameter value, wherein the second parameter value is not being used in training.
However, Carolan—directed to analogous art--teaches
further comprising providing said estimation of said observable of said quantum state using a neural network inference for estimation of a property of a quantum state of said parametrized Hamiltonian with a second parameter value, wherein the second parameter value is not being used in training. (Carolan, [0069-0071] describes applying a trained neural network for generating states of a quantum system on Hamiltonian parameters on which the network was not previously trained. The combination of references would consequently have suggested applying the neural network taught by Carrasquilla to Hamiltonians with previously unseen parameters.)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Carrasquilla, Yoshioka, and Caruana because doing so allows for the generalization of the neural network to be assessed as described by Carolan at [0069].
Claims 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over Carrasquilla (Reconstructing quantum states with generative models, ArXiv version dated 24 October 2018) in view of Yoshioka (Constructing neural stationary states for open quantum many-body systems), further in view of Caruana (Multitask Learning), and further in view of “Lee” (US 2021/0011748 A1).
Regarding claim 23, the combination of Carrasquilla, Yoshioka and Caruana as applied in claim 1 teaches every aspect of the claim except for:
A system for improving an estimation of an observable of a quantum state, the system comprising: a digital computer comprising an interface, a memory comprising instructions, wherein said digital computer is configured to execute said instructions to at least:... instruct at least one computational platform to:
However, Lee—directed to analogous art--teaches
A system for improving an estimation of an observable of a quantum state, the system comprising: a digital computer comprising an interface, a memory comprising instructions, wherein said digital computer is configured to execute said instructions to at least:... instruct at least one computational platform to: (Lee, Figure 3, element 350, described at [0041].)
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have modified Nagy by Lee because the system of Lee provides a computationally feasible solution to solving problems involving quantum computations by using a classical/quantum hybrid system as described by Lee at [0021].
Regarding claim 24, the rejection of claim 23 is incorporated herein. Carrasquilla does not appear to explicitly teach
wherein said computational platform comprises at least one member of the group consisting of a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), a central processing unit (CPU), a graphics processing unit (GPU), a tensor processing unit (TPU), and a tensor streaming processor (TSP).
However, Lee—directed to analogous art—teaches
wherein said computational platform comprises at least one member of the group consisting of a field-programmable gate array (FPGA), an application- specific integrated circuit (ASIC) (Lee, [0048]), a central processing unit (CPU) (Lee, Figure 3, element 352. See also [0048]. A SoC comprises a CPU.), a graphics processing unit (GPU), a tensor processing unit (TPU), and a tensor streaming processor (TSP).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 23.
Regarding claim 25, the rejection of claim 23 is incorporated herein. Carrasquilla does not appear to explicitly teach
wherein said quantum device comprises at least one of a quantum annealer, a trapped ion quantum computer, an optical quantum computer, a photonics-based quantum computer, a spin-based quantum dot computer, and a superconductor-based quantum computer.
However, Lee—directed to analogous art—teaches
wherein said quantum device comprises at least one of a quantum annealer, a trapped ion quantum computer (Lee, [0022]), an optical quantum computer (Lee, [0022]), a photonics-based quantum computer, a spin-based quantum dot computer, and a superconductor-based quantum computer (Lee, [0022]).
It would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to have combined these references in this way for the same reasons given above with respect to claim 23.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Markus A Vasquez whose telephone number is (303)297-4432. The examiner can normally be reached Monday to Friday 10AM to 2PM PT.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Li Zhen can be reached at (571) 272-3768. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/MARKUS A. VASQUEZ/Primary Examiner, Art Unit 2121
Prosecution Insights