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 1-25 are pending and are examined herein.
Claims 23-25 recite at least one limitation which is invokes 35 USC 112(f).
Claims 23-25 are rejected under 35 USC 112(b).
Claims 1-25 are rejected under 35 USC 103.
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 Interpretation – 35 U.S.C. 112(f)
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “computational platform” and “readout control system” in claim 23 and claims dependent thereon.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For the purposes of examination, the “computational platform” is being interpreted as computer hardware comprising an FPGA, ASIC, CPU, GPU, TPU, TSP, or equivalents thereof (see published specification at [0025]).
For the purposes of examination, the “readout control system” is being interpreted as any computer hardware capable of controlling a readout. The specification does not describe the structure which performs this function.
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 23-25 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 limitation “readout control system” recited in claims 23-25 invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. The specification only repeats the claim language without linking it to any structure capable of performing readout control. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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 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 1-2, 5, 7, 14, 17 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over “Nagy” (Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems) in view of “Torlai” (Latent Space Purification via Neural Density Operators).
Regarding claim 1, Nagy teaches
A method for reducing an error in an estimation of a property of a quantum state, the method comprising: (Nagy, Abstract: “...we develop a variational method to efficiently simulate the non-equilibrium steady state of Markovian open quantum systems based on variational Monte Carlo and on a neural network representation of the density matrix...”)
(a) receiving a plurality of measurements of a quantum state... (Nagy, page 3, “Sampling” and “Observables” sections describe sampling measurements from a simulation of a quantum process.)
(b) using a computational platform and said plurality of measurements to prepare a representation of said quantum state, wherein said representation comprises a neural network comprising one or more tunable parameters; and (Nagy, page 2: “...neural network ansatz [...] RBM ansatz [...] Each node is associated with a bias (a- and b-parameters) and nodes in the different layers are connected via a set of weighted edges (Х-parameters)...”, Fig. 1. Nagy, page 3, “Optimization” describes training/preparing the neural network based on the samples described in “Sampling” and “Observables”.)
(c) training said neural network by adjusting said one or more tunable parameters using said computational platform to variationally improve said quantum state, and wherein said training reduces an error in said estimation of said property of said quantum state. (Nagy, page 3: “Optimization” section. For example, “...The parameter values that best approximate <<Lx>> = 0 can be found by means of various optimization procedures [...] we choose to adopt the Stochastic Reconfiguration (SR) scheme...”. The training is iterative, so the “preparing” could be interpreted as comprising earlier training iterations and the “training” could be interpreted as comprising later training iterations.)
Nagy does not appear to explicitly teach the following. The portion of the limitation in italics emphasizes the portion of the limitation not taught by Nagy:
(a) receiving a plurality of measurements of a quantum state from a quantum device;
However, Torlai—directed to analogous art—teaches
(a) receiving a plurality of measurements of a quantum state from a quantum device; (Torlai, Abstract and page 3 “Quantum state reconstruction” describes solving the problem of reconstructing an unknown quantum state from a set of experimental measurements based on near-term quantum devices.)
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 Torlai because Nagy identifies Torlai as being on way of performing the optimization (see Nagy, page 3, “Optimization”. Torlai is reference 44). Moreover, as described by Torlai, page 5: “Restricted Boltzmann machines offer a powerful method for generative modeling, with training algorithms that are well studied by the machine-learning community. Their demonstrated ability to provide practical tradeoffs between representation, computation, and statistics offers a rich field of study in the case of quantum states, which will be important in the integration of classical and quantum algorithms inevitable in near-term devices and computers.”
Regarding claim 2, the rejection of claim 1 is incorporated herein. Furthermore, Nagy teaches
wherein (c) comprises performing a variational Monte Carlo procedure. (Nagy, page 3, optimization describes training the neural network using VMC (variational Monte Carlo).)
Regarding claim 5, the rejection of claim 1 is incorporated herein. Furthermore, Nagy teaches
further comprising repeating (a)-(c) until a stopping criterion is met. (Nagy, page 3, “Optimization” indicates that the procedure is iterative and that the step size is selected to be small enough that convergence is guaranteed (i.e., the stopping criterion is met).)
Regarding claim 7, the rejection of claim 1 is incorporated herein. Nagy does not appear to explicitly teach
wherein said neural network further comprises a cost function; further wherein (b) comprises:
(i) using said plurality of measurements to provide an input to said neural network;
(ii) computing a value of said neural network cost function;
(iii) computing a gradient of said cost function with respect to said one or more tunable parameter of said neural network;
(iv) using said computed gradient and said computed cost function to update said one or more tunable parameter of said neural network; and
(v) repeating (i) — (iv) any number of times.
However, Torlai—directed to analogous art—teaches
wherein said neural network further comprises a cost function; (Torlai, Abstract, page 3, equation 10 and surrounding explanation. Equation (10) is the cost function.)
further wherein (b) comprises: (i) using said plurality of measurements to provide an input to said neural network; (Torlai, page 3, equation 10 includes the value ρλ,μ(σbk, σbk). The previous two paragraphs explain that this is the value computed by the neural network based on the measurements σbk.)
(ii) computing a value of said neural network cost function; (iii) computing a gradient of said cost function with respect to said one or more tunable parameter of said neural network; (iv) using said computed gradient and said computed cost function to update said one or more tunable parameter of said neural network; and (Torlai, page 3, equation (10) computes the negative log-likelihood and update (11) shows that the gradient of the negative log-likelihood is computed with respect to the neural network parameters θ, which is used to update the neural network parameters θ.)
(v) repeating (i) — (iv) any number of times. (Torlai, page 3, text between equations (10) and (11) indicates that the process is iterated.)
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 14, the rejection of claim 1 is incorporated herein. Furthermore, Nagy teaches
wherein (b) comprises performing a variational quantum computing procedure. (Nagy, page 2: “...neural network ansatz [...] RBM ansatz [...] Each node is associated with a bias (a- and b-parameters) and nodes in the different layers are connected via a set of weighted edges (Х-parameters)...”, Fig. 1. Nagy, page 3, “Optimization” describes training/preparing the neural network based on the samples described in “Sampling” and “Observables”. This is performed using variational Monte Carlo to estimate the quantum system (i.e., it is a variational quantum computing procedure).)
Regarding claim 17, the rejection of claim 1 is incorporated herein. Furthermore, Nagy teaches
wherein said property of said quantum state comprises an observable of said quantum state. (Nagy, Page 3, “Observables” indicates that the estimated properties are observables.)
Regarding claim 19, the rejection of claim 1 is incorporated herein. Furthermore, Nagy teaches
wherein said neural network comprises at least one of an autoregressive model, a recurrent neural network, a transformer, an autoregressive generative model, an attention-based architecture, a dense deep neural network, a convolutional neural network, a variational autoencoder, a generative adversarial network, a restricted Boltzmann machine (Nagy, page 2, last paragraph of last column, through first paragraph of page 3, describes using a restricted Boltzmann machine.), a general Boltzmann machine, an energy-based model, an invertible neural network, and a flow-based generative model.
Claims 4 is rejected under 35 U.S.C. 103 as being unpatentable over “Nagy” (Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems) in view of “Torlai” (Latent Space Purification via Neural Density Operators), and further in view of “Onishi” (US 2021/0375403 A1).
Regarding claim 4, the rejection of claim 1 is incorporated herein. The combination of Nagy and Torlai does not appear to explicitly teach
further comprising prior to (a) using an interface of a digital computer to receive an indication of a property of a quantum state to be estimated; and subsequent to (c) providing said estimation of said property of said quantum state at said interface.
However, Onishi—directed to analogous art—teaches
further comprising prior to (a) using an interface of a digital computer to receive an indication of a property of a quantum state to be estimated; and subsequent to (c) providing said estimation of said property of said quantum state at said 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. In the combination with Nagy, the technique of receiving an input from and providing an output to a user would be used in conjunction with the technique taught by Nagy.)
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 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 6 and 9 are rejected under 35 U.S.C. 103 as being unpatentable over “Nagy” (Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems) in view of “Torlai” (Latent Space Purification via Neural Density Operators), and further in view of “Itoko” (US 2020/0342343 A1).
Regarding claim 6, the rejection of claim 1 is incorporated herein. The combination of Nagy and Torlai does not appear to explicitly teach
further comprising prior to (a) receiving an indication of a set of measurement operators; and wherein (a) further comprises, until a stopping criterion is met:
(i) using a quantum experiment to experimentally prepare an approximation of said quantum state;
(ii) selecting a measurement operator from said set of measurement operators; and
(iii) performing a measurement of said prepared approximation of said quantum state using said selected operator from said set of measurement operators.
However, Itoko—directed to analogous art—teaches
further comprising prior to (a) receiving an indication of a set of measurement operators; and (Itoko, Abstract, Figure 10, step 1002, described at [0108].)
wherein (a) further comprises, until a stopping criterion is met: (Itoko, Figure 10, decision 1022. The procedure taught by Itoko iterates until the list is empty.)
(i) using a quantum experiment to experimentally prepare an approximation of said quantum state; (Itoko, Figure 10, step 1004 describes initializing the qubits to a particular state. In the combination with Nagy, this would be the quantum state taught by Nagy. See also [0039-0040] where the state is initialized to some |ψ(θ)> used to determine an energy of the Hamiltonian Hq.)
(ii) selecting a measurement operator from said set of measurement operators; and (Itoko, Figure 10, step 1012 describes selecting a BBC (Bell basis candidate, see description at [0103]).)
(iii) performing a measurement of said prepared approximation of said quantum state using said selected operator from said set of measurement operators. (Itoko, Abstract describes performing the measurement using the BBC. See also [0060]. See also [0099] and Figure 7 for a particular example.)
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 and Torlai by Itoko because the techniques taught by Itoko “reduce the number of measurements by selecting appropriate measurement basis reduces the computation time and computation cost of the quantum processor” (Itoko, [0015]).
Regarding claim 9, the rejection of claim 6 is incorporated herein. Furthermore, Nagy teaches
wherein said quantum [sampling] comprises one or more of a quantum computation (Nagy, page 3, “Sampling” and “observables” indicates that the samples take measurements of the observables of the steady state. The determination of the measurements is a quantum computation of the measurement values.) , a circuit model quantum computation, a quantum annealing measurement-based quantum computation, and an adiabatic quantum computing.
The combination of Nagy and Torlai does not appear to explicitly teach the samples being obtained via experiments as required by claim 6.
However, Itoko—directed to analogous art—teaches
experiment (Itoko, Figure 10, step 1004 describes initializing the qubits to a particular state. In the combination with Nagy, this would be the quantum state taught by Nagy. See also [0039-0040] where the state is initialized to some |ψ(θ)> used to determine an energy of the Hamiltonian Hq.)
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.
Claims 3, 8, 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over “Nagy” (Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems) in view of “Torlai” (Latent Space Purification via Neural Density Operators), and further in view of “Carleo” (Solving the quantum many-body problem with artificial neural networks).
Regarding claim 3, the rejection of claim 2 is incorporated herein. Furthermore, Nagy teaches
wherein said variational Monte Carlo procedure comprises one or more neural networks that are representative of, respectively, an ansatz ...wavefunction (Nagy, pages 2-3: Neural network density matrix, Optimization), a tensor network ansatz, a Jastrow wavefunction, or a Hartree-Fock wavefunction.
Nagy and Torlai does not appear to explicitly teach
an ansatz ground state wavefunction
However, Carleo—directed to analogous art—teaches
an ansatz ground state wavefunction (Carleo, Abstract and page 2, “Ground state” section going onto page 3.)
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 and Torlai by Carleo because “Our approach achieves high accuracy in describing prototypical interacting spins models in one and two dimensions” (Carleo, Abstract). Furthermore, Torlai suggests this on page 2: “For example, this could be the variational minimization of the total energy [3,13].” Carleo is reference [3] in Torlai.
Regarding claim 8, the rejection of claim 3 is incorporated herein. Furthermore, Nagy teaches
wherein (c) further comprises: (i) using said neural network to sample at least one configuration; (Nagy, Page 2, equation (4) shows the neural network estimating/sampling a density matrix.)
[iterating] until a stopping criterion is met. (Nagy, page 3, “Optimization” indicates that the procedure is iterative and that the step size is selected to be small enough that convergence is guaranteed (i.e., the stopping criterion is met).)
Nagy does not appear to explicitly teach
(ii) using said at least one sampled configuration to estimate a variational energy of said wavefunction represented by a mean of a local energy;
(iii) using said at least one sampled 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 estimated variational energy and said estimated gradient of said variational energy to update said one or more tunable parameters of said neural network; and
(v) repeating (i) — (iv)
However, Torlai—directed to analogous art—teaches
(iii) using said at least one sampled configuration to estimate a gradient of [an objective function] with respect to said one or more tunable parameters of said neural network; (iv) using said [objective function] and said estimated gradient of said [objective function] to update said one or more tunable parameters of said neural network; and (Torlai, Abstract, page 3, equation 10 and surrounding explanation. Equation (10) is the objective function. Page 3, equation (10) computes the negative log-likelihood and update (11) shows that the gradient of the negative log-likelihood is computed with respect to the neural network parameters θ, which is used to update the neural network parameters θ.)
(v) repeating (i) — (iv) (Torlai, page 3, text between equations (10) and (11) indicates that the process is iterated.)
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.
The combination of Nagy and Torlai does not appear to explicitly teach (italics emphasizes portion not taught by Nagy and Torlai):
(ii) using said at least one sampled configuration to estimate a variational energy of said wavefunction represented by a mean of a local energy;
(iii) using said at least one sampled 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 estimated variational energy and said estimated gradient of said variational energy to update said one or more tunable parameters of said neural network; and
However, Carleo—directed to analogous art—teaches
(ii) using said at least one sampled configuration to estimate a variational energy of said wavefunction represented by a mean of a local energy; (iii) using said at least one sampled 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 estimated variational energy and said estimated gradient of said variational energy to update said one or more tunable parameters of said neural network; and (Carleo, pages 2, “Ground state”, last paragraph going onto page 3 describes computing an expectation (mean) value of an energy of the wavefunction I of a Hamiltonian and performing gradient descent on the energy to train the neural network by iteratively determining the next set of weights.)
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 3.
Regarding claim 10, the rejection of claim 1 is incorporated herein. The combination of Nagy and Torlai does not appear to explicitly teach
wherein said quantum state comprises a ground state of a Hamiltonian.
However, Carleo—directed to analogous art—teaches
wherein said quantum state comprises a ground state of a Hamiltonian. (Carleo, Abstract and page 2, “Ground state” section going onto page 3.)
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 3.
Regarding claim 18, the rejection of claim 17 is incorporated herein. Nagy and Torlai does not appear to explicitly teach
wherein said observable of said quantum state is an expected energy of said quantum state.
However, Carleo—directed to analogous art—teaches
wherein said observable of said quantum state is an expected energy of said quantum state. (Carleo, pages 2, “Ground state”, last paragraph going onto page 3 describes computing an expectation (mean) value of an energy of the wavefunction I of a Hamiltonian and performing gradient descent on the energy to train the neural network by iteratively determining the next set of weights.)
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 3.
Claims 11-13 are rejected under 35 U.S.C. 103 as being unpatentable over “Nagy” (Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems) in view of “Torlai” (Latent Space Purification via Neural Density Operators), and further in view of “Itoko” (US 2020/0342343 A1), 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. The combination of Nagy and Torlai does not appear to explicitly teach
wherein said quantum computation comprises solving an optimization problem; and further wherein said quantum state comprises a ground state of a Hamiltonian.
However, Itoko—directed to analogous art—teaches
wherein said quantum computation comprises solving an optimization problem (Itoko, [0036-0038])
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 9.
The combination of Nagy, Torlai, and Itoko does not appear to explicitly teach
further wherein said quantum state comprises a ground state of a Hamiltonian.
However, Pastorello—directed to analogous art—teaches
further wherein said quantum state comprises a ground state of a Hamiltonian. (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 Nagy, Torlai and Itoko 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. Furthermore, Pastorello 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. Furthermore, Pastorello 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 “Nagy” (Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems) in view of “Torlai” (Latent Space Purification via Neural Density Operators), and further in view of “Itoko” (US 2020/0342343 A1), 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. The combination of Nagy, Torlai, and Itoko does not appear to explicitly teach
wherein said quantum computation comprises a quantum chemistry simulation; and wherein said quantum state is of a Hamiltonian representative of a quantum chemistry problem.
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 Nagy, Torlai, and Itoko 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.
Claims 20-22 are rejected under 35 U.S.C. 103 as being unpatentable over “Nagy” (Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems) in view of “Torlai” (Latent Space Purification via Neural Density Operators), and further in view of “Lee” (US 2021/0011748 A1).
Regarding claim 20, the rejection of claim 1 is incorporated herein. Furthermore, Nagy teaches
wherein said quantum state is of a parametrized Hamiltonian (Nagy, Page 3, equation *8( shows the Hamiltonian parameterized by the J values.)
The combination of Nagy and Torlai does not appear to explicitly teach
further wherein a parametrization of said parameterized Hamiltonian is continuous.
However, Lee—directed to analogous art—teaches
further wherein a parametrization of said parameterized Hamiltonian is continuous. (Lee [0037-0039] describes representing a Hamiltonian using the parameters θ. [0078-0079] describe updating the parameters θ using continuous values, so θ is a continuous parameterization.)
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 and Torlai by Lee because Lee allows for computationally feasible solution to simulating quantum systems as described by Lee at [0020-0021].
Regarding claim 21, the rejection of claim 20 is incorporated herein. The combination of Nagy and Torlai 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, Lee—directed to analogous art—teaches
wherein said neural network is configured to further receive a parameter value of said parameterization as an input. (Lee [0037-0039] describes representing a Hamiltonian using the parameters θ. The parameters θ are provide as input to the RBM Hamiltonian.)
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 20.
Regarding claim 22, the rejection of claim 20 is incorporated herein. Furthermore, Nagy teaches
further comprising providing an estimation of a property 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 is not being used in training. (Nagy, pages 3-5, “Results” section describes applying the technique to the Hamiltonian shown in equation (8). The expectation value <<LX>> and the local magnetization were estimated. The parameters J are not used in the training equations (5) and (6) on page 3.)
Claims 23-25 are rejected under 35 U.S.C. 103 as being unpatentable over “Nagy” (Variational Quantum Monte Carlo Method with a Neural-Network Ansatz for Open Quantum Systems) in view of “Lee” (US 2021/0011748 A1).
Regarding claim 23, Nagy teaches
A system for improving an estimation of a property of a quantum state, the system comprising: (Nagy, Abstract: “...we develop a variational method to efficiently simulate the non-equilibrium steady state of Markovian open quantum systems based on variational Monte Carlo and on a neural network representation of the density matrix...”)
...receive a plurality of measurements of a quantum state; (Nagy, page 3, “Sampling” and “Observables” sections describe sampling measurements from a simulation of a quantum process.)
use a computational platform and said plurality of measurements to prepare a representation of said quantum state, wherein said representation comprises a neural network comprising one or more tunable parameters; and (Nagy, page 2: “...neural network ansatz [...] RBM ansatz [...] Each node is associated with a bias (a- and b-parameters) and nodes in the different layers are connected via a set of weighted edges (Х-parameters)...”, Fig. 1. Nagy, page 3, “Optimization” describes training/preparing the neural network based on the samples described in “Sampling” and “Observables”.)
train said neural network by adjusting said one or more tun”ble parameters using said computational platform to variationally improve said quantum state; (Nagy, page 3: “Optimization” section. For example, “...The parameter values that best approximate <<Lx>> = 0 can be found by means of various optimization procedures [...] we choose to adopt the Stochastic Reconfiguration (SR) scheme...”. The training is iterative, so the “preparing” could be interpreted as comprising earlier training iterations and the “training” could be interpreted as comprising later training iterations.)
...(ii) to train said neural network representative of said quantum state by adjusting said at least one tunable parameter of said neural network to variationally improve said quantum state. (Nagy, page 3: “Optimization” section. For example, “...The parameter values that best approximate <<Lx>> = 0 can be found by means of various optimization procedures [...] we choose to adopt the Stochastic Reconfiguration (SR) scheme...”. The training is iterative, so the “preparing” could be interpreted as comprising earlier training iterations and the “training” could be interpreted as comprising later training iterations.)
Nagy does not appear to explicitly teach
(a) a digital computer comprising an interface, a memory comprising instructions, wherein said digital computer is configured to execute said instructions to at least:
... (b) at least one quantum device operatively connected to said digital computer, wherein said at least one quantum device comprises at least a quantum processor and a readout control system,
wherein said at least one quantum device is configured to conduct a quantum experiment to obtain said plurality of measurements of said quantum state using said readout control system; and
(c) said at least one computational platform operatively connected to said digital computer, wherein said at least one computational platform comprises at least one processor and a readout control system, wherein said at least one computational platform is configured to
...(i) receive from said digital computer a configuration of a neural network comprising at least one tunable parameter, and said plurality of measurements;
However, Lee—directed to analogous art—teaches
(a) a digital computer comprising an interface, a memory comprising instructions, wherein said digital computer is configured to execute said instructions to at least: (Lee, Figure 3, element 350, described at [0041].)
... (b) at least one quantum device operatively connected to said digital computer, wherein said at least one quantum device comprises at least a quantum processor and a readout control system, wherein said at least one quantum device is configured to conduct a quantum experiment to obtain said plurality of measurements of said quantum state using said readout control system; and (Lee, Figure 3, element 330, described at [0041-0042], and Figure 5, described at [0050-0052]. Note that any run of the quantum device would fall within the broadest reasonable interpretation of a “quantum experiment”.)
(c) said at least one computational platform operatively connected to said digital computer, wherein said at least one computational platform comprises at least one processor and a readout control system, wherein said at least one computational platform is configured to...(i) receive from said digital computer a configuration of a neural network comprising at least one tunable parameter, and said plurality of measurements; (Lee, Figure 4, described at [0043-0049]. The computational platform is connected to at least one digital processor 421 and the readout control system of Figure 5, element 520. The platform also comprises quantum/classical interfaces 407 and classical interfaces 402 and 406 for transmitting data between the various digital and classical devices.
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. Nagy 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. Nagy 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
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure.
Carrasquilla (Reconstructing quantum state with generative models, arXiv:1810.10584v1) – First paragraph describes using a neural network to generate the density matrix for a quantum state.
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/MARKUS A. VASQUEZ/ Primary Examiner, Art Unit 2121