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 .
DETAILED ACTION
Status of Claims
This action is in reply to the application filed on December 10, 2021.
This application claims priority to provisional application 63/123,917, filed on December 10, 2020.
Claims 1-20 are currently pending.
Information Disclosure Statement
The information disclosure statement (IDS) filed December 28, 2021, has been considered.
The reference “Quantum Monte Carlo Approaches for Correlated Systems” was not considered because no copy was provided and the link on the IDS did not direct to a copy of the document; see MPEP 609.04(a).
The listing of references in the specification is not a proper information disclosure statement; see [0069] and [00187] of the instant specification. 37 CFR 1.98(b) requires a list of all patents, publications, or other information submitted for consideration by the Office, and MPEP § 609.04(a) states, "the list may not be incorporated into the specification but must be submitted in a separate paper." Therefore, unless the references have been cited by the examiner on form PTO-892, they have not been considered.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 11 is rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the enablement requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to enable one skilled in the art to which it pertains, or with which it is most nearly connected, to make and/or use the invention. Claim 11 recites: “estimating a number of solutions of the optimization problem by calculating a residual entropy.” Paragraph [0083] of the specification states: “Likewise, the residual entropy Eq. (3) at T(l) = 0 provides an approach to count the number of solutions to the problem Hamiltonian. Further details are provided in the Methods section below.” The methods section does not provide further details on how to estimate a number of solutions by calculating a residual entropy and the only other appearance of the term “residual entropy” is in [0017], which language mirrors the claim language. Additionally, it is not clear how the temperature, T, factors into equation 3 as T only appears in equation 2 and the results of equation 2 is not used in equation 3.
Considering the Wands factors:
The breadth of the claims: the claims are broad.
The nature of the invention:
The state of the prior art: the state of the art is high.
The level of one of ordinary skill: the level of ordinary skill is high.
The level of predictability in the art: the level of predictability is low.
The amount of direction provided by the inventor: the amount of direction is low.
The existence of working examples: there are no known working examples.
The quantity of experimentation needed to make or use the invention based on the disclosure: the amount of experimentation is high.
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.
Claim 10 is 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 10 recites the limitations "the locality of the optimization task," “the connectivity of the optimization task,” and “the uniformity or nonuniformity of the optimization task.” There is insufficient antecedent basis for these limitations in the claim.
Claim 14 is 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 14 recites the limitations "the future sampling” in line 3. There is insufficient antecedent basis for these limitations in the claim.
Claim 15 is 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 15 recites the limitations "the future sampling” in line 3. There is insufficient antecedent basis for these limitations in the claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
When considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (Step 1). If the claim does fall within one of the statutory categories, the second step in the analysis is to determine whether the claim is directed to a judicial exception (Step 2A). The step 2A analysis is broken into two prongs. In the first prong (Step 2A, Prong 1), it is determined whether or not the claims recite a judicial exception (e.g. mathematical concepts, mental processes, certain methods of organizing human activity). If it is determined in step 2A, Prong 1 that the claims recite a judicial exception, the analysis proceeds to the second prong (Step 2A, Prong 2), where it is determined whether or not the claims integrate the judicial exception into a practical application. If it is determined that step 2A, Prong that the claims do not integrate the judicial exception into a practical application, the analysis proceeds to determining whether the claim is a patent-eligible application of the exception (Step 2B). If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim integrates the judicial exception into a practical application, or else amounts to significantly more than the abstract idea itself.
According to Step 1 of the analysis, in the instant case claims 1-19 are directed to a method and claim 20 is directed to a non-transitory computer readable medium. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Considering independent claim 1 and Step 2A, Prong One, the limitations including: “performing an annealing step while maintaining the values of the plurality of parameters; and performing a training step to modulate the values of the plurality of parameters according to a cost function, thereby generating a plurality of trained values of the respective plurality of parameters, the plurality of trained values having a lower cost, according to the cost function, than a cost of the values of the plurality of parameters prior to the modulation” covers performance of the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind.
MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind and examples of mental processes include observations, evaluations, judgments, and opinions.” The “annealing” and “training” steps in claim 1 are an evaluations and/or judgments, and mental steps. Claim 1 does not detail the training step or indicate what is being trained, and a human is capable of generating a plurality of values having a lower cost according to a cost function. Therefore, the claim contains abstract elements and the evaluation continues.
Considering Step 2A, Prong Two, the judicial exception in claim 1 is not integrated into a practical application. Claim 1 includes the additional elements: “obtaining a plurality of initial input values; obtaining a variational ansatz comprising a plurality of initial values for the plurality of parameters” The obtaining steps do not integrate the abstract idea into a practical application because they are insignificant extra-solution data activity, mere data gathering; see MPEP 2106.05(g).
Considering Step 2B, the additional elements do not amount to significantly more. The obtaining steps are insignificant extra-solution activity, mere data gathering, and do not amount to significantly more; see MPEP 2106.05(g).
Therefore, claim 1 is ineligible in view of 35 U.S.C. 101.
Considering claim 2, dependent on claim 1, and Step 2A, Prong One, the limitations including: “the annealing step comprises changing a temperature parameter of the cost function” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 2’s annealing is an evaluation and/or judgment.
Claim 2 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 2 is ineligible in view of 35 U.S.C. 101.
Considering claim 3, dependent on claim 2, and Step 2A, Prong One, the limitations including: “the variational emulation of annealing is variational emulation of classical annealing; and the cost function comprises a variational free energy function” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 3’s annealing and cost function details are an evaluation and/or judgment.
Claim 3 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 3 is ineligible in view of 35 U.S.C. 101.
Considering claim 4, dependent on claim 1, and Step 2A, Prong One, the limitations including: “the annealing step comprises changing a driving coupling of the cost function” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 4’s annealing step is an evaluation and/or judgment.
Claim 4 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 4 is ineligible in view of 35 U.S.C. 101.
Considering claim 5, dependent on claim 4, and Step 2A, Prong One, the limitations including: “the variational emulation of annealing is variational emulation of quantum annealing; and the cost function comprises a variational energy function” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 5’s annealing step and cost function details are an evaluation and/or judgment.
Claim 5 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 5 is ineligible in view of 35 U.S.C. 101.
Considering claim 6, dependent on claim 4, and Step 2A, Prong One, the limitations including: “positive wavefunctions ansatzes are used to implement stoquastic drivers” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 6’s wavefunction details are an evaluation and/or judgment.
Claim 6 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 6 is ineligible in view of 35 U.S.C. 101.
Considering claim 7, dependent on claim 5, and Step 2A, Prong One, the limitations including: “complex wavefunctions ansatzes are used to implement non-stoquastic drivers” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 7’s wavefunction details are an evaluation and/or judgment.
Claim 7 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 7 is ineligible in view of 35 U.S.C. 101.
Considering claim 8, dependent on claim 1, and Step 2A, Prong One, the limitations including: “wherein the annealing step comprises: changing a driving coupling of the ansatz; and changing a fictitious temperature parameter of the ansatz” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 8’s annealing details are an evaluation and/or judgment.
Claim 8 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 8 is ineligible in view of 35 U.S.C. 101.
Considering claim 9, dependent on claim 1, and Step 2A, Prong One, the claim does not contain an abstract idea. However, the limitations including: “wherein the variational ansatz comprises an autoregressive neural network” is an additional element that does not integrate the abstract idea into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B because it is mere data gathering. The variational ansatz is “obtained” in claim 1 and adding details about what the ansatz contains does not change the analysis. Therefore, claim 9 would not render the claim eligible if incorporated into the independent claim.
Considering claim 10, dependent on claim 9, and Step 2A, Prong One, the limitations including: “wherein the autoregressive neural network encodes one or more of the following: the locality of the optimization task; the connectivity of the optimization task; and the uniformity or nonuniformity of the optimization task” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 10’s optimization details are an evaluation and/or judgment.
Claim 10 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 10 is ineligible in view of 35 U.S.C. 101.
Considering claim 11, dependent on claim 1, and Step 2A, Prong One, the limitations including: “estimating a number of solutions of the optimization problem by calculating a residual entropy” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 11’s estimation details are an evaluation, judgment, and/or opinion.
Claim 11 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 11 is ineligible in view of 35 U.S.C. 101.
Considering claim 12, dependent on claim 1, and Step 2A, Prong One, the limitations including: “wherein the training step comprises: performing gradient descent on the plurality of parameters based on the cost function” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 12’s estimation details are an evaluation and/or judgment.
Claim 12 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 12 is ineligible in view of 35 U.S.C. 101.
Considering claim 13, dependent on claim 1, and Step 2A, Prong One, the limitations including: “comprising, after repeating the annealing step and training step one or more times: storing the variational ansatz for future sampling” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 13’s annealing details are an evaluation and/or judgment.
Claim 13 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 13 is ineligible in view of 35 U.S.C. 101.
Considering claim 14, dependent on claim 13, and Step 2A, Prong One, the limitations including: “the future sampling comprises using the variational ansatz as an on-demand sampler for generating solutions of the optimization task” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 14’s sampling is an evaluation and/or judgment.
Claim 14 contains the additional element “the variational ansatz comprises an autoregressive neural network;” which does not integrate the abstract idea into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B because it is mere data gathering; the variational ansatz is “obtained” in claim 1 and adding details about what the ansatz contains does not change the analysis. Therefore, claim 14 is ineligible in view of 35 U.S.C. 101.
Considering claim 15, dependent on claim 13, and Step 2A, Prong One, the limitations including: “the future sampling comprises using the variational ansatz as an on-demand sampler for generating solutions of a different optimization task” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 15’s sampling is an evaluation and/or judgment.
Claim 15 contains the additional element “the variational ansatz comprises an autoregressive neural network;” which does not integrate the abstract idea into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B because it is mere data gathering; the variational ansatz is “obtained” in claim 1 and adding details about what the ansatz contains does not change the analysis. Therefore, claim 15 is ineligible in view of 35 U.S.C. 101.
Considering claim 16, dependent on claim 1, and Step 2A, Prong One, the claim does not include an abstract idea. However, the limitations “comprising, after repeating the annealing step and training step one or more times: using the values of the plurality of parameters as an input to train a neural network to perform an optimization task that the neural network was not previously trained to perform” are additional elements that are not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. The claim amounts to mere instructions to apply an exception on a neural network and would not make the claims eligible if incorporated into the independent claims; see MPEP 2106.05(f). Therefore, claim 16 is ineligible in view of 35 U.S.C. 101.
Considering claim 17, dependent on claim 1, and Step 2A, Prong One, the limitations including: “wherein the training step comprises: setting a temperature parameter of the cost function to zero; and setting a transverse field parameter of the cost function to zero” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 17’s parameter details are an evaluation and/or judgment.
Claim 17 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 17 is ineligible in view of 35 U.S.C. 101.
Considering claim 18, dependent on claim 1, and Step 2A, Prong One, the limitations including: “wherein the variational ansatz comprises one of the following: a mean field model; a tensor network; or a non-autoregressive artificial neural network” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 18’s ansatz details are an evaluation and/or judgment.
Claim 18 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 18 is ineligible in view of 35 U.S.C. 101.
Considering claim 19, dependent on claim 18, and Step 2A, Prong One, the limitations including: “wherein the variational ansatz encodes one or more of the following: the locality of the optimization task; the connectivity of the optimization task; and the uniformity or nonuniformity of the optimization task” covers performance of the mind. MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind, and examples of mental processes include observations, evaluations, judgments, and opinions.” Claim 19’s encoding details are an evaluation and/or judgment.
Claim 19 does not contain any new additional elements and, therefore, is not integrated into a practical application or amount to significantly more under Step 2A, Prong Two, and Step 2B. Therefore, claim 19 is ineligible in view of 35 U.S.C. 101.
Considering independent claim 20 and Step 2A, Prong One, the limitations including: “performing an annealing step while maintaining the values of the plurality of parameters; and performing a training step to modulate the values of the plurality of parameters according to a cost function, thereby generating a plurality of trained values of the respective plurality of parameters, the plurality of trained values having a lower cost, according to the cost function, than a cost of the values of the plurality of parameters prior to the modulation” covers performance of the mind. That is, nothing in the claim element precludes the step from practically being performed in the mind.
MPEP 2106.04(a)(2)(III) notes “the "mental processes" abstract idea grouping is defined as concepts performed in the human mind and examples of mental processes include observations, evaluations, judgments, and opinions.” The “annealing” and “training” steps in claim 1 are an evaluations and/or judgments, and mental steps. Claim 20 does not detail the training step or indicate what is being trained, and a human is capable of generating a plurality of values having a lower cost according to a cost function. Therefore, the claim contains abstract elements and the evaluation continues.
Considering Step 2A, Prong Two, the judicial exception in claim 20 is not integrated into a practical application. Claim 20 includes the additional elements: “[a] non-transitory computer readable medium”, “processor of a device”, and “obtaining a plurality of initial input values; obtaining a variational ansatz comprising a plurality of initial values for the plurality of parameters”. The non-transitory computer readable medium, processor, and device are generic computer components that do not integrate the abstract idea into a practical application; see MPEP 2106.05(b)(I). Further, the obtaining steps do not integrate the abstract idea into a practical application because they are insignificant extra-solution data activity, mere data gathering; see MPEP 2106.05(g).
Considering Step 2B, the additional elements do not amount to significantly more. The non-transitory computer readable medium, processor, and device are generic computer components that do not amount to significantly more; see MPEP 2106.05(d)(II). Further, the obtaining steps are insignificant extra-solution activity, mere data gathering, and do not amount to significantly more; see MPEP 2106.05(g).
Therefore, claim 20 is ineligible in view of 35 U.S.C. 101.
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(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Crosson et al., “Simulated Quantum Annealing Can Be Exponentially Faster than Classical Simulated Annealing” (Crosson); in view of Roth, “Iterative Retraining of Quantum Spin Models using Recurrent Neural Networks” (Roth).
With respect to independent claim 1, Crosson teaches:
A method for providing a solution to an optimization task using a variational emulation of annealing, the solution comprising a plurality of values for a respective plurality of parameters (Crosson teaches a method for simulated quantum annealing including a plurality of parameters in at least in the abstract and section II.), the method comprising:
obtaining a plurality of initial input values (Crosson teaches input as an n-bit string in the abstract.);
…
repeating one or more times:
performing an annealing step while maintaining the values of the plurality of parameters (Crosson teaches quantum and simulated quantum annealing in sections II and III.); and
…
the plurality of trained values having a lower cost, according to the cost function, than a cost of the values of the plurality of parameters prior to the modulation (Crosson teaches cost functions in the abstract and paragraph one of page 715. The last paragraph of page 715 discusses finding a global minimum of the cost function, which corresponds to a low cost.).
Crosson does not teach:
obtaining a variational ansatz comprising a plurality of initial values for the plurality of parameters; and
performing a training step to modulate the values of the plurality of parameters according to a cost function, thereby generating a plurality of trained values of the respective plurality of parameters,
However Roth teaches these limitations:
obtaining a variational ansatz comprising a plurality of initial values for the plurality of parameters (Roth teaches using an RNN based Ansatz that is able to learn the ground state energy of extremely large lattices (plurality of values) by continuously retraining the same model on progressively larger systems; see section I., last paragraph. Section II.A. also teaches using a variational Monte Carlo using an RNN-based Ansatz.); and
…
performing a training step to modulate the values of the plurality of parameters according to a cost function, thereby generating a plurality of trained values of the respective plurality of parameters (Roth teaches trainable parameters in the first paragraph of section II.C. Section II.D. discloses iterative retraining. Training, and retraining, involves modulating parameters.),
Crosson and Roth are analogous art directed to quantum simulation. Crosson teaches methods for simulated quantum annealing and Roth teaches using neural networks to train quantum spin models.
It would have been obvious for one of ordinary sill in quantum simulation to incorporate Roth’s teachings into Crosson’s invention before the filing data of the claimed invention. It would have been obvious because one would be motivated to simulate large systems and make efficient use of data; see Roth section I., last paragraph.
With respect to claim 2, the rejection of claim 1 is incorporated. Further, Crosson teaches:
wherein the annealing step comprises changing a temperature parameter of the cost function (Crosson teaches a temperature parameter, T, that is gradually lowered in the first paragraph on page 2.).
With respect to claim 3, the rejection of claim 2 is incorporated. Further, Crosson teaches:
wherein:
the variational emulation of annealing is variational emulation of classical annealing (Crosson teaches considering the classical algorithm known as simulated quantum annealing (SQA) in the abstract.); and
the cost function comprises a variational free energy function (Crosson teaches quantum annealing and energy states in the equations beginning in the last paragraph of page 720.).
With respect to claim 4, the rejection of claim 1 is incorporated. Further, Crosson teaches:
wherein the annealing step comprises changing a driving coupling of the cost function (Crosson teaches SQA convergence in section III and that the spin-spin coupling is lowered in the analysis.).
With respect to claim 5, the rejection of claim 4 is incorporated. Further, Crosson teaches:
wherein:
the variational emulation of annealing is variational emulation of quantum annealing (Crosson teaches considering the classical algorithm known as simulated quantum annealing (SQA) in the abstract. It is noted that the instant spec, paragraph [0085], indicates that SQA emulates this process on classical computers.); and
the cost function comprises a variational energy function (Crosson teaches quantum annealing and energy states in the equations beginning in the last paragraph of page 720.).
With respect to claim 6, the rejection of claim 5 is incorporated. Further, Roth teaches:
wherein positive wavefunctions ansatzes are used to implement stoquastic drivers (Roth teaches using Hamiltonian methods in section III. [00151] of the instant specification teaches that Hamiltonians are stoquastic.).
See the rejection of claim 1 for the motivation to combine references.
With respect to claim 7, the rejection of claim 5 is incorporated. Further, teaches:
wherein complex wavefunctions ansatzes are used to implement non-stoquastic drivers (Roth teaches Markov Chain Monte Carlo methods in section II.B. [0030] of the instant specification teaches that Monte Carlo methods implement non-stoquastic drivers.).
See the rejection of claim 1 for the motivation to combine references.
With respect to claim 8, the rejection of claim 1 is incorporated. Further, Crosson teaches:
wherein the annealing step comprises:
changing a driving coupling of the ansatz (Crosson teaches a spin-spin coupling in section III on page 719.); and
changing a fictitious temperature parameter of the ansatz (Crosson teaches simulating various parameters, including temperature on page 715.).
With respect to claim 9, the rejection of claim 1 is incorporated. Further, Roth teaches:
wherein the variational ansatz comprises an autoregressive neural network (Roth teaches a recurrent neural network in section II.D. RNNs are autoregressive. Additionally, section II.C. of Roth describes implementing an autoregressive model using a recurrent neural network.).
See the rejection of claim 1 for the motivation to combine references.
With respect to claim 10, the rejection of claim 9 is incorporated. Further, Roth teaches:
wherein the autoregressive neural network encodes one or more of the following:
the locality of the optimization task;
the connectivity of the optimization task (Roth teaches the autoregressive model encodes information about sequences in a vector; see section II.C.); and
the uniformity or nonuniformity of the optimization task.
See the rejection of claim 1 for the motivation to combine references.
With respect to claim 11, the rejection of claim 1 is incorporated. Further, Roth teaches:
estimating a number of solutions of the optimization problem by calculating a residual entropy (Roth calculates an entropy in section V.C.).
See the rejection of claim 1 for the motivation to combine references.
With respect to claim 12, the rejection of claim 1 is incorporated. Further, Roth teaches:
wherein the training step comprises:
performing gradient descent on the plurality of parameters based on the cost function (Roth teaches using gradient information to make small updates to parameters and minimize the energy; see section II.A, last paragraph.).
See the rejection of claim 1 for the motivation to combine references.
With respect to claim 13, the rejection of claim 1 is incorporated. Further, Roth teaches:
comprising, after repeating the annealing step and training step one or more times:
storing the variational ansatz for future sampling (Roth teaches the model allows for a persistent memory state in section II.C.).
See the rejection of claim 1 for the motivation to combine references.
With respect to claim 14, the rejection of claim 13 is incorporated. Further, teaches:
wherein:
the variational ansatz comprises an autoregressive neural network (Roth teaches a recurrent neural network in section II.D. RNNs are autoregressive. Additionally, section II.C. of Roth describes implementing an autoregressive model using a recurrent neural network.); and
the future sampling comprises using the variational ansatz as an on-demand sampler for generating solutions of the optimization task (Roth teaches sampling in II.A.).
See the rejection of claim 1 for the motivation to combine references.
With respect to claim 15, the rejection of claim 13 is incorporated. Further, teaches:
wherein:
the variational ansatz comprises an autoregressive neural network (Roth teaches a recurrent neural network in section II.D. RNNs are autoregressive. Additionally, section II.C. of Roth describes implementing an autoregressive model using a recurrent neural network.); and
the future sampling comprises using the variational ansatz as an on-demand sampler for generating solutions of a different optimization task (Roth teaches sampling in II.A.).
See the rejection of claim 1 for the motivation to combine references.
With respect to claim 16, the rejection of claim 1 is incorporated. Further, Roth teaches:
comprising, after repeating the annealing step and training step one or more times:
using the values of the plurality of parameters as an input to train a neural network to perform an optimization task that the neural network was not previously trained to perform (Roth teaches training machine learning models on trainable parameters in section II.C.).
See the rejection of claim 1 for the motivation to combine references.
With respect to claim 17, the rejection of claim 1 is incorporated. Further, Crosson teaches:
wherein the training step comprises:
setting a temperature parameter of the cost function to zero (Crosson teaches a zero temperature in section II.); and
setting a transverse field parameter of the cost function to zero (Crosson teaches a transverse filed that sweeps the interval 0 to 1 in section II.).
With respect to claim 18, the rejection of claim 1 is incorporated. Further, Roth teaches:
wherein the variational ansatz comprises one of the following:
a mean field model (Roth teaches using the circulant mean in section V.B.);
a tensor network (Roth teaches tensor states in section IV.); or
a non-autoregressive artificial neural network.
See the rejection of claim 1 for the motivation to combine references.
With respect to claim 19, the rejection of claim 18 is incorporated. Further, Roth teaches:
wherein the variational ansatz encodes one or more of the following:
the locality of the optimization task;
the connectivity of the optimization task (Roth teaches the autoregressive model encodes information about sequences in a vector; see section II.C.); and
the uniformity or nonuniformity of the optimization task.
See the rejection of claim 1 for the motivation to combine references.
With respect to independent claim 20, Crosson teaches:
A non-transitory computer readable medium storing instructions that, when executed by a processor of a device, cause the device to provide a solution to an optimization task using a variational emulation of annealing, the solution comprising a plurality of values for a respective plurality of parameters (Crosson teaches a method for simulated quantum annealing including a plurality of parameters in at least in the abstract and section II.), by:
obtaining a plurality of initial input values (Crosson teaches input as an n-bit string in the abstract.);
…
repeating one or more times:
performing an annealing step while maintaining the values of the plurality of
parameters (Crosson teaches quantum and simulated quantum annealing in sections II and III.); and
…
the plurality of trained values having a lower cost, according to the cost function, than a cost of the values of the plurality of parameters prior to the modulation (Crosson teaches cost functions in the abstract and paragraph one of page 715. The last paragraph of page 715 discusses finding a global minimum of the cost function, which corresponds to a low cost.).
Crosson does not teach:
obtaining a variational ansatz comprising a plurality of initial values for the plurality of parameters; and
performing a training step to modulate the values of the plurality of parameters according to a cost function, thereby generating a plurality of trained values of the respective plurality of parameters,
Roth teaches these limitations:
obtaining a variational ansatz comprising a plurality of initial values for the plurality of parameters (Roth teaches using an RNN based Ansatz that is able to learn the ground state energy of extremely large lattices (plurality of values) by continuously retraining the same model on progressively larger systems; see section I., last paragraph. Section II.A. also teaches using a variational Monte Carlo using an RNN-based Ansatz.); and
…
performing a training step to modulate the values of the plurality of parameters according to a cost function, thereby generating a plurality of trained values of the respective plurality of parameters, (Roth teaches trainable parameters in the first paragraph of section II.C. Section II.D. discloses iterative retraining. Training, and retraining, involves modulating parameters.),
Crosson and Roth are analogous art directed to quantum simulation. Crosson teaches methods for simulated quantum annealing and Roth teaches using neural networks to train quantum spin models.
It would have been obvious for one of ordinary sill in quantum simulation to incorporate Roth’s teachings into Crosson’s invention before the filing data of the claimed invention. It would have been obvious because one would be motivated to simulate large systems and make efficient use of data; see Roth section I., last paragraph.
Conclusion
Claims 1-20 are rejected.
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/DANIEL T PELLETT/Primary Examiner, Art Unit 2121
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