DETAILED ACTION
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
2. This communication is in response to the Applicant’s submission filed 27 February 2023, where:
Claims 1-9 are pending.
Claims 1-9 are rejected.
Foreign priority is claimed to JP2022-149218, filed 20 September 2022,. A certified copy of this paper, however, does not appear to have been filed as required by 37 CFR 1.55.
Information Disclosure Statement
3. Information disclosure statements were submitted on 27 February 2023, 13 April 2023, and 23 January 2026. The submissions comply with the provisions of 37 CFR 1.97. Accordingly, the Examiner considered the information disclosure statements.
Specification
4. The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
Claim Interpretation
5. 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.
6. 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) 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):
(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). The presumption that the claim limitation is interpreted under 35 U.S.C. § 112(f), 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). The presumption that the claim limitation is not interpreted under 35 U.S.C. § 112(f), 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), 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) 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), 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 limitations are:
Claim 1, line 7, recites “an update unit,”
Claim 1, lines 9-10, recites “an optimization unit,”
Claim 1, line 19, recites “a first update unit,”
Claim 1, line 26, recites “a second update unit;
And corresponding each “step of…” in each of claims 8 and 9.
Because these claim limitations are being interpreted under 35 U.S.C. § 112(f), they 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 these limitations interpreted under 35 U.S.C. § 112(f), Applicant may:
(1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. § 112(f) (e.g., by reciting sufficient structure to perform the claimed function); or
(2) present a sufficient showing that the claim limitations recite sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. § 112(f).
Claim Rejections - 35 U.S.C. § 101
7. 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.
8. Claims 1-9 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more.
Examiner notes that the broadest reasonable interpretation of the claim terms “quantum computer,” “parameterized quantum circuit” or “quantum circuit” cover a simulation of the respective device, which is not inconsistent with the Applicant’s disclosure. (MPEP § 2111; see, e.g., Specification ¶ 0075 (“simulation of the quantum circuit was performed using Qiskit)).
Claim 1 recites a system, which is a product, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “[(a)] a quantum computer that uses a parameterized quantum circuit defined by a circuit parameter to calculate a loss function from a coordinate parameter of a processing target molecule,” “[(b.1)] an update unit that updates the coordinate parameter and the circuit parameter based on the loss function,” and “[(b.2)] an optimization unit that repeats a variational optimization procedure.” The activities of [(a)] . . . to calculate,” “[(b.1)] updates,” and “[(b.2)] repeats a variational optimization procedure” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. Also, the activities of [(a)] . . . to calculate,” “[(b.1)] updates . . . based on the loss function,” and “[(b.2)] repeats a variational optimization procedure” include mathematical relationships, mathematical formulas or equations, and mathematical calculations, and accordingly, are a mathematical concept, (MPEP § 2106.04(a)(2) sub I), which is one of the groupings of abstract ideas.
The claim recites more details or specifics of the abstract idea of “[(b.2)] repeats a variational optimization procedure” where “[(b.2.1)] the calculation of the loss function by the quantum computer and the update of the coordinate parameter and the circuit parameter by the update unit until a stop condition is satisfied,” and “[(b.2.2)] determines an optimum value of the circuit parameter and an optimum value of the coordinate parameter that minimize or maximize the loss function,” and accordingly, are merely more specific to the abstract idea.
The claim also recites more details or specifics of the abstract idea of “[(b.1)] updates,” wherein “[(b.1.1)] a first update unit that [(b.1.1.1)] estimates a second provisional value of a second parameter out of the circuit parameter and the coordinate parameter while fixing a first parameter out of the circuit parameter and the coordinate parameter to a first provisional value and [(b.1.1.2)] changing the second parameter according to a Bayesian optimization algorithm based on the loss function,” and “[(b.1.2)] a second update unit that [(b.1.2.1)] updates the first provisional value of the first parameter while fixing the second parameter to the second provisional value and [(b.1.2.2)] changing the first parameter according to the Bayesian optimization algorithm based on the loss function,” and accordingly, are merely more specific to the abstract idea. Thus, claim 1 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include a “quantum computer,” and a “classical computer,” which are recited at a high level of generality, and accordingly, are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. The claim also recites an “update unit,” and “optimization unit,” and the “update unit” includes a “first update unit,” and a “second update unit,” which are recited at a high level of generality, and accordingly, are generic computer components, (MPEP § 2106.05(f)), that do not serve to integrate the abstract idea into a practical application. Therefore, claim 1 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include a “quantum computer,” and a “classical computer,” which are recited at a high level of generality, and accordingly, are generic computer components used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to being significantly more than the abstract idea. The claim also recites an “update unit,” and “optimization unit,” and the “update unit” includes a “first update unit,” and a “second update unit,” which are recited at a high level of generality, and accordingly, are generic computer components, (MPEP § 2106.05(f)), that do not amount to significantly more than the abstract idea. Therefore, claim 1 is subject-matter ineligible.
Claims 2, 3, and 4 depend directly or indirectly from claim 1. The claims recite more details or specifics to the abstract idea of “[(a)] . . . to calculate,” (claim 2: [(a.1)] wherein the circuit parameter is a rotation angle vector of a quantum gate constituting the parameterized quantum circuit;” claim 3: [(a.1)] wherein the coordinate parameter is a vector of coordinates of an atom included in the processing target molecule;” and claim 4: [(a.1)] wherein the loss function is a Hamiltonian defined by the coordinate parameter of the processing target molecule), and accordingly, are merely more specific to the abstract idea. Also, additional elements of the claim do not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Therefore, claims 2, 3, and 4 are subject-matter ineligible.
Claims 5, 6, and 7 depend directly or indirectly from claim 1. The claims recite more details or specifics to the abstract idea of “[(b.2)] an optimization unit that repeats a variational optimization procedure,” (claim 5: “[(b.2.3)] wherein the variational optimization procedure is a variational quantum eigenvalue method;” claim 6: [(b.2)] wherein the optimization unit [(b.2.3)] determines an optimum value of the coordinate parameter when the processing target molecule has a ground-state molecular structure by minimizing the loss function;” and claim 7: “[(b.2)] wherein the optimization unit [(b.2.3)] determines an optimum value of the coordinate parameter when the processing target molecule has a transition-state molecular structure by maximizing the loss function”), and accordingly, are merely more specific to the abstract idea. . Also, additional elements of the claim do not serve to integrate the abstract idea into integrated into a practical application, (see MPEP § 2106.04(d)), nor do the additional elements amount to significantly more than the abstract idea, (MPEP § 2106.05 sub I; see also MPEP § 2106.05(a) – (h)), and thus, the claim recites no more than the abstract idea. Therefore, claims 5, 6, and 7 is subject-matter ineligible.
Claim 8 recites a method, which is a process, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “[(a)] a quantum computing step of using a parameterized quantum circuit defined by a circuit parameter to calculate a loss function from a coordinate parameter of a processing target molecule,” and “,” [(b)] an update step of sequentially updating the coordinate parameter and the circuit parameter based on the loss function,” and [(c)] an optimization step of repeating a variational optimization procedure.” The activities of [(a)] . . . to calculate,” “[(b.1)] updates,” and “[(b.2)] repeats a variational optimization procedure” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. Also, the activities of [(a)] . . . to calculate,” “[(b.1)] updates . . . based on the loss function,” and “[(b.2)] repeats a variational optimization procedure” include mathematical relationships, mathematical formulas or equations, and mathematical calculations, and accordingly, are a mathematical concept, (MPEP § 2106.04(a)(2) sub I), which is one of the groupings of abstract ideas.
The claim recites more details or specifics of the abstract idea of “[(c)] repeating a variational optimization procedure” where “[(c.1)] the calculation of the loss function by the quantum computer and the update of the coordinate parameter and the circuit parameter by the update unit until a stop condition is satisfied,” and “[(c.2)] determining an optimum value of the circuit parameter and an optimum value of the coordinate parameter that minimize or maximize the loss function,” and accordingly, are merely more specific to the abstract idea.
The claim also recites more details or specifics of the abstract idea of “[(b)] sequentially updating,” wherein “[(b.1)] a first update step that [(b.1.1)] estimating a second provisional value of a second parameter out of the circuit parameter and the coordinate parameter while fixing a first parameter out of the circuit parameter and the coordinate parameter to a first provisional value and [(b.1. 2)] changing the second parameter according to a Bayesian optimization algorithm based on the loss function,” and “[(b. 2)] a second update step that [(b.2.1)] updating the first provisional value of the first parameter while fixing the second parameter to the second provisional value and [(b.2.2)] changing the first parameter according to the Bayesian optimization algorithm based on the loss function,” and accordingly, are merely more specific to the abstract idea. Thus, claim 8 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include a “parameterized quantum computer,” which is recited at a high level of generality, and accordingly, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application. Therefore, claim 8 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include a “parameterized quantum computer,” which is recited at a high level of generality, and accordingly, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to being significantly more than the abstract idea. Therefore, claim 8 is subject-matter ineligible.
Claim 9 recites a parameterized quantum circuit, which is a product, and thus one of the statutory categories of patentable subject matter. (35 U.S.C. § 101).
However, under Step 2A Prong One, the claim recites the limitations of “[(a)] a quantum computing step of using a parameterized quantum circuit defined by a circuit parameter to calculate a loss function from a coordinate parameter of a processing target molecule,” and “,” [(b)] an update step of sequentially updating the coordinate parameter and the circuit parameter based on the loss function,” and [(c)] an optimization step of repeating a variational optimization procedure.” The activities of [(a)] . . . to calculate,” “[(b.1)] updates,” and “[(b.2)] repeats a variational optimization procedure” can practically be performed in the human mind, including, for example, observations, evaluations, judgments, and opinions, and accordingly, are a mental process, (MPEP § 2106.04(a)(2) sub III), which is one of the groupings of abstract ideas. Also, the activities of [(a)] . . . to calculate,” “[(b.1)] updates . . . based on the loss function,” and “[(b.2)] repeats a variational optimization procedure” include mathematical relationships, mathematical formulas or equations, and mathematical calculations, and accordingly, are a mathematical concept, (MPEP § 2106.04(a)(2) sub I), which is one of the groupings of abstract ideas.
The claim recites more details or specifics of the abstract idea of “[(c)] repeating a variational optimization procedure” where “[(c.1)] the calculation of the loss function by the quantum computer and the update of the coordinate parameter and the circuit parameter by the update unit until a stop condition is satisfied,” and “[(c.2)] determining an optimum value of the circuit parameter and an optimum value of the coordinate parameter that minimize or maximize the loss function,” and accordingly, are merely more specific to the abstract idea.
The claim also recites more details or specifics of the abstract idea of “[(b)] sequentially updating,” wherein “[(b.1)] a first update step that [(b.1.1)] estimating a second provisional value of a second parameter out of the circuit parameter and the coordinate parameter while fixing a first parameter out of the circuit parameter and the coordinate parameter to a first provisional value and [(b.1. 2)] changing the second parameter according to a Bayesian optimization algorithm based on the loss function,” and “[(b. 2)] a second update step that [(b.2.1)] updating the first provisional value of the first parameter while fixing the second parameter to the second provisional value and [(b.2.2)] changing the first parameter according to the Bayesian optimization algorithm based on the loss function,” and accordingly, are merely more specific to the abstract idea. Thus, claim 9 recites an abstract idea.
Under Step 2A Prong Two, the claim as a whole is not integrated into a practical application, because the additional elements recited in the claim beyond the identified judicial exception include a “parameterized quantum computer,” which is recited at a high level of generality, and accordingly, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not serve to integrate the abstract idea into a practical application.
Therefore, claim 9 is directed to the abstract idea.
Finally, under Step 2B, the additional elements, taken alone or in combination, do not represent significantly more than the abstract idea itself. The additional elements include a “parameterized quantum computer,” which is recited at a high level of generality, and accordingly, is a generic computer component used to implement the abstract idea, (MPEP § 2106.05(f)), that does not amount to being significantly more than the abstract idea.
Therefore, claim 9 is subject-matter ineligible.
Claim Rejections – 35 U.S.C. § 103 %%%
9. 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.
10. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. § 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
11. 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.
12. Claims 1-9 are rejected under 35 U.S.C. § 103 as being unpatentable over US Published Application 20200394537 to Wang et al. [hereinafter Wang ‘537] in view of Self et al., “Variational quantum algorithm with information sharing,” arXiv (2021) [hereinafter Self].
Regarding claims 1, 8, and 9, Wang ‘537 teaches [a] system (Wang ‘537 ¶ 0224 teaches “system 100 includes a quantum computer 102”) of claim 1, [a] molecular structure optimization method (Wang ‘537 ¶ 0224 teaches “a method 200 performed by the system 100 [that] includes a quantum computer 102”) of claim 8, and [a] parameterized quantum circuit assigned with an optimum value of a circuit parameter determined by a molecular structure optimization method (Wang ‘537 ¶ 0224”) of claim 9, comprising:
[(a)] a quantum computer that uses a parameterized quantum circuit defined by a circuit parameter to calculate a loss function from a coordinate parameter of a processing target molecule(Wang ‘537, Fig. 4, teaches a hybrid quantum-classical system [Examiner annotations in dashed-line text boxes]:
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Wang ‘537 ¶ 0028 teaches “quantum-circuit parameter values are real numbers that control how quantum gates operate on qubits [(that is, a quantum computer that uses a parameterized quantum circuit defined by a circuit parameter)]”; Wang ‘537 ¶ 0029 teaches an “accuracy-improvement rate is a function that expresses by how much a corresponding accuracy of the statistic improves with each iteration of the present method embodiments. The accuracy-improvement rate is a function of the quantum-circuit parameters, and may additionally be a function of the statistic (e.g., the mean). The accuracy is any quantitative measure of an error of the statistic [(that is, “accuracy improvement rate functions” pertains to “error,” which is a loss function)]”) from a coordinate parameter of a processing target molecule (Wang ‘537 ¶ 0100 teaches “coordinate ascent [that] allows each variable to change dramatically in a single step. . . . Specifically, embodiments of the present invention which implement this algorithm may start with a random initial point, and successively maximize the objective function :/ (μ; f, x) along coordinate directions, until a convergence criterion is satisfied. At the j-th step of each round, it solves the following single-variable optimization problem for a coordinate Xj [(that is, a coordinate parameter)]”; Wang ‘537 ¶ 0234 teaches “[i]n embodiments in which some or all of the qubits 104 are implemented using nuclear magnetic resonance (NMR) (in which case the qubits may be molecules, e.g., in liquid or solid form) [(that is, from a coordinate parameter of a processing target molecule)]”); and
[(b)] a classical computer (see above, Wang ‘537, Fig. 4, “classical computer 434”) including
[(b.1)] an update unit that updates the coordinate parameter and the circuit parameter based on the loss function (Wang ‘537 ¶ 0032 teaches “the plurality of qubits in the reflected quantum state are measured with respect to the observable P to obtain a set of measurement outcomes. In the block 410 [(that is, an update unit)], which is performed on the classical computer 434, the statistic is updated with the set of measurement outcomes to obtain an estimate of
s
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P
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s
with higher accuracy [(that is, an update unit that updates the . . . circuit parameter based on the loss function)]”), and
[(b.2)] an optimization unit that repeats a variational optimization procedure (Wang ‘537 ¶ 0037 teaches “a Bayesian inference method for operator measurement that smoothly interpolates between the standard sampling regime and phase estimation regime. This is proposed as ‘α-VQE’, where the asymptotic scaling for performing an operator measurement is O(1/εα) with the extremal values of α=2 corresponding to the standard sampling regime (typically realized in [variational quantum eigensolver (VQE)] and α=1 corresponding to the quantum-enhanced regime where the scaling reaches the Heisenberg limit (typically realized with phase estimation) [(that is, “α-VQE” is a variational optimization procedure)]”; Wang ‘537 ¶ 0039 teaches “[e]mbodiments of the present invention may optimize the parameters for maximal information gain during each step of inference [(that is, an optimization unit that repeats a variational optimization procedure)]”) including
[(b.2.1)] the calculation of the loss function by the quantum computer (Wang ‘537 ¶ 0029 teaches “The accuracy-improvement rate is a function that expresses by how much a corresponding accuracy of the statistic improves with each iteration of the present method embodiments. The accuracy-improvement rate is a function of the quantum-circuit parameters [(that is, the calculation of the loss function by the quantum computer)], and may additionally be a function of the statistic (e.g., the mean). The accuracy is any quantitative measure of an error of the statistic. For example, the accuracy may be a mean squared error, standard deviation, variance, mean absolute error, or another moment of the error. Alternatively, the accuracy may be an information metric, such as Fisher information or information entropy [(that is, the calculation of the loss function)]”) and the update of the coordinate parameter and the circuit parameter by the update unit until a stop condition is satisfied (Wang ‘537 ¶ 0044 teaches “1) tuning the circuit parameter [θ] to maximize information gain, and 2) Bayesian inference for updating the current belief about the distribution of the true value of [the expectation] Π; Wang ‘537 ¶ 0078 teaches “a Gaussian distribution to represent knowledge of θ and make this distribution gradually converge to the true value of 0 as the inference process proceeds. Embodiments of the present invention may start with an initial distribution of [the expectation] Π (which can be generated by standard sampling or domain knowledge) and convert it to the initial distribution of [the circuit parameter] θ. Then embodiments of the present invention may iterate the following procedure until a convergence criterion is satisfied [(that is, “convergence criterion” is the update of the coordinate parameter and the circuit parameter by the update unit until a stop condition is satisfied)]”), and
[(b.2.2)] determines an optimum value of the circuit parameter and an optimum value of the coordinate parameter that minimize or maximize the loss function (Wang ‘537 ¶ 0170 teaches “maximizing the Fisher information [(that is, the loss function)] of the likelihood function
P
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θ
;
f
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→
at a given point θ=μ (i.e., the prior mean of θ). Namely, our goal is to find
x
→
∈
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2
L
that maximize [the Fisher Information of the Likelihood Function] [(that is, determines an optimum value of the circuit parameter and an optimum value of the coordinate parameter that . . . maximize the loss function)]”),
* * *
Though Wang ‘537 teaches updating a plurality of parameters for a quantum computer where qubits implemented in a physical media including molecules exhibiting qubit behavior comprising quantum states and transitions therebetween that can be controllably induced or detected, Wang ‘537, however, does not explicitly teach –
* * *
[(b.1)] wherein the update unit includes:
[(b.1.1)] a first update unit that
[(b.1.1.1)] estimates a second provisional value of a second parameter out of the circuit parameter and the coordinate parameter while fixing a first parameter out of the circuit parameter and the coordinate parameter to a first provisional value and
[(b.1.1.2)] changing the second parameter according to a Bayesian optimization algorithm based on the loss function; and
[(b.1.2)] a second update unit that
[(b.1.2.1)] updates the first provisional value of the first parameter while fixing the second parameter to the second provisional value and
[(b.1.2.2)] changing the first parameter according to the Bayesian optimization algorithm based on the loss function.
But Self teaches a “coordinate parameter” (Self, right column of p. 2, “Bayesian optimization and BOIS,” first paragraph, teaches physical coordinate xα and xβ [(that is, coordinate parameter)] and also circuit parameters θα, θβ . . . [(that is, circuit parameters)].
Self also teaches -
[(b.1)] wherein the update unit (Self, Fig. 1 caption, teaches “[e]ach [Bayesian optimization (BO)] can then be updated using the measurement results obtained for several θα, θβ, . . . parameter points [(that is, update unit)]”; Self, Fig. 1, teaches an update unit having a first and a second update unit [Examiner annotations in dashed-line text boxes]:
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Self, right column of p. 2, “B. Bayesian optimization and BOIS,” first paragraph, teaches BOIS employs an array of Bayesian optimisers running in parallel [(that is, the update unit)]) includes:
[(b.1.1)] a first update unit that
[(b.1.1.1)] estimates a second provisional value of a second parameter out of the circuit parameter and the coordinate parameter while fixing a first parameter out of the circuit parameter and the coordinate parameter to a first provisional value (“Self, Fig. 1 caption, teaches that “[s]eparate BO's B(xɑ) optimise for different cost functions Cɑ, corresponding to different values of the physical parameters xɑ, using the same parameterised ansatz circuit U(θ). Every iteration, each B(xɑ) requests a new variational parameter point θɑ, [(that is, “requests a new variational parameter point” is fixing a first parameter out of the circuit parameter and the coordinate parameter toa first provisional value)] at which the set of Pauli strings {Pi} are measured”) and
[(b.1.1.2)] changing the second parameter according to a Bayesian optimization algorithm based on the loss function (Self, Fig. 1 caption, teaches “[t]hese expectation values [(that is, the “expectation values” are changing the second parameter according to a Bayesian optimization algorithm)] are used to compute any Cβ cost functions (dashed lines) at θα, for all ɑ, β [(that is, “cost functions” is based on the loss function)]”); and
[(b.1.2)] a second update unit that
[(b.1.2.1)] updates the first provisional value of the first parameter while fixing the second parameter to the second provisional value (Self, Fig. 1 caption, teaches “[e]ach [Bayesian optimization (BO)] can then be updated using the measurement results obtained for several θɑ, θβ, . . . parameter points each iteration (bold arrows), dramatically speeding up convergence at all xɑ [(that is, “updating the Bayesian optimization (BO)” is updates the first provisional value of the first parameter while fixing the second parameter to the second provisional value)]”) and
[(b.1.2.2)] changing the first parameter according to the Bayesian optimization algorithm based on the loss function (Self, Fig. 1 caption, teaches “[e]ach [Bayesian optimization (BO)] can then be updated using the measurement results obtained for several θɑ, θβ, . . . parameter points each iteration (bold arrows), dramatically speeding up convergence at all xɑ [(that is, “convergence at all xɑ” is changing the first parameter according to the Bayesian optimization algorithm)]”).
Wang ‘537 and Self are from the same or similar field of endeavor. Wang ‘537 teaches hybrid quantum-classical computer device based on phase estimation, and variational estimation with Bayesian inference with regard to a molecular target. Self teaches an optimization method for variational quantum algorithms obtaining multi- dimensional energy surfaces for small molecules and a spin model by solving variational problems in parallel by exploiting the global nature of Bayesian optimization and sharing information between different optimizers.
Thus, it would have been obvious to a person having ordinary skill in the art to modify Wang ‘537 pertaining to a hybrid quantum-classical device implementing variational estimation and Bayesian inference with the parallel variational molecular problem solving of Self.
The motivation to do so is because “an optimisation method for variational quantum algorithms and experimentally demonstrate a 100-fold improvement in efficiency compared to naive implementations.” (Self, Abstract).
Regarding claim 2, the combination of Wang ‘537 and Self teaches all of the limitations of claim 1, as described above in detail.
Wang ‘537 teaches –
[(a.1)] wherein the circuit parameter is a rotation angle vector of a quantum gate constituting the parameterized quantum circuit (Wang ‘537 ¶ 0132 teaches “[c]hanging the state of a qubit state typically is referred to as a single-qubit rotation, and may also be referred to herein as a state change or a single-qubit quantum-gate operation vector. A rotation corresponds to a rotation of a qubit state within its Hilbert space, which may be conceptualized as a rotation of the Bloch sphere. (As is well-known to those having ordinary skill in the art, the Bloch sphere is a geometrical representation of the space of pure states of a qubit.) . . . (As is well-known to those having ordinary skill in the art, a Hilbert space is an abstract vector space possessing the structure of an inner product that allows length and angle to be measured. Furthermore, Hilbert spaces are complete: there are enough limits in the space to allow the techniques of calculus to be used) [(that is, a “length and angle” is the circuit parameter is a rotation angle vector of a quantum gate constituting the parameterized quantum circuit)]”).
Regarding claim 3, the combination of Wang ‘537 and Self teaches all of the limitations of claim 1, as described above in detail.
Self teaches -
[(a.1)] wherein the coordinate parameter is a vector of coordinates of an atom included in the processing target molecule (Self, left column of p. 4, “D. Experimental demonstration on IBMQ devices,” first paragraph, teaches “a linear chain of H3, parameterized by two relative inter-atomic distances x = (x1; x2) [(that is, vector of coordinates)], which we discretize into a grid to find the 2D energy surface [(that is, “parameterized distances” is the coordinate parameter is a vector of coordinates of an atom included in the processing target molecule)]”).
Regarding claim 4, the combination of Wang ‘537 and Self teaches all of the limitations of claim 1, as described above in detail.
Self teaches -
[(a.1)] wherein the loss function is a Hamiltonian defined by the coordinate parameter of the processing target molecule (Self, right column of p. 5, A. Qubit Hamiltonians for Small Molecules,” third paragraph, teaches “[w]e use the symmetry conserving Bravi-Kitaev transform for H3, and the parity transform for H2 and LiH to express the fermionic Hamiltonian as a qubit Hamiltonian”; Self, left column of p. 7, “B. Ansatz Design,” first full paragraph, teaches “[w]ith minimal adjustments the same tensor-network-based framework can be adapted to use the expectation of a Hamiltonian as a cost function [(that is, the loss function is a Hamiltonian)] to be minimised, resulting in a scheme that is qualitatively similar to hardware-efficient ADAPT-VQE [23] [(that is, the loss function is a Hamiltonian defined by the coordinate parameter of the processing target molecule)]”).
Regarding claim 5, the combination of Wang ‘537 and Self teaches all of the limitations of claim 1, as described above in detail.
Wang ‘537 teaches -
[(b.2.3)] wherein the variational optimization procedure is a variational quantum eigenvalue method (Wang ‘537 ¶ 0037 teaches “a Bayesian inference method for operator measurement that smoothly interpolates between the standard sampling regime and phase estimation regime. This is proposed as ‘α-VQE’, where the asymptotic scaling for performing an operator measurement is O(1/εα) with the extremal values of α=2 corresponding to the standard sampling regime (typically realized in [variational quantum eigensolver (VQE)] and α=1 corresponding to the quantum-enhanced regime where the scaling reaches the Heisenberg limit (typically realized with phase estimation) [(that is, “α-VQE” is the variational optimization procedure is a variational quantum eigenvalue method)]”;.
Regarding claim 6, the combination of Wang ‘537 and Self teaches all of the limitations of claim 1, as described above in detail.
Self teaches -
[(b.2)] wherein the optimization unit [(b.2.3)] determines an optimum value of the coordinate parameter Self, left column of p. 4, “D. Experimental demonstration on IBMQ devices,” first paragraph, teaches “a linear chain of H3, parameterized by two relative inter-atomic distances x = (x1; x2), which we discretize into a grid to find the 2D energy surface [(that is, the “energy surface” is determines an optimum value of the coordinate parameter)]”) when the processing target molecule has a ground-state molecular structure by minimizing the loss function (Self, right column of p. 1, “A. Parallelising VQE,” first paragraph, teaches “VQE uses a quantum ansatz circuit, parameterised by a set of angles θ, to prepare a state
ᴪ
θ
, and estimate the expectation value
H
θ
≡
ᴪ
θ
H
ᴪ
θ
. Classical optimization is used to minimize the cost function
H
θ
[(that is, minimizing the loss function)] with respect to θ, which . . . corresponds to the ground state [(that is, when the processing target molecule has a ground-state molecular structure by minimizing the loss function)]”; Self, right column of p. 6, “C. Ansatz Design,” last partial paragraph, teaches “[i]n our VQE+BOIS simulations we are attempting to find ground state energy surfaces across a physical parameter space. For each VQE+BOIS simulation a target state was chosen as a ground state sitting on the energy surface in question”).
Regarding claim 7, the combination of Wang ‘537 and Self teaches all of the limitations of claim 1, as described above in detail.
Self teaches -
[(b.2)] wherein the optimization unit [(b.2.3)] determines an optimum value of the coordinate parameter Self, left column of p. 4, “D. Experimental demonstration on IBMQ devices,” first paragraph, teaches “a linear chain of H3, parameterized by two relative inter-atomic distances x = (x1; x2), which we discretize into a grid to find the 2D energy surface [(that is, the “energy surface” is determines an optimum value of the coordinate parameter)]”) when the processing target molecule has a transition-state molecular structure by maximizing the loss function (Self, left column of p. 7, “B. Ansatz Design,” first partial paragraph, teaches “Finally, to further reduce the number of optimisation parameters, the optimal gate parameters for each BOIS physical parameter xɑ are found (again by maximising the fidelity) and any single-qubit gate angles within this optimal parameter set that are found to remain effectively constant across the energy surface are fixed to these constant values in the ansatz [(that is, “physical parameter xɑ” and “energy surface” is when the processing target molecule has a transition-state molecular structure by maximizing the loss function)]”).
Examiner notes that the Applicant’s preamble does not afford patentable weight to the Applicant’s claims because the claim preamble is not “necessary to give life, meaning, and vitality” to the claim. Moreover, because the Applicant’s preamble merely states the purpose or intended use of the invention rather than any distinct definition of any of the claimed invention’s limitations, the preamble is not considered a limitation and is of no significance to claim construction.
Conclusion
12. The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
(McClean et al., "The theory of variational hybrid quantum-classical algorithms," arXiv (2015)) teaches the concept of quantum variational error suppression that allows some errors to be suppressed naturally in this algorithm on a pre-threshold quantum device. Additionally, we analyze truncation and correlated sampling in Hamiltonian averaging as ways to reduce the cost of this procedure. Finally, we show how the use of modern derivative free optimization techniques can offer dramatic computational savings of up to three orders of magnitude over previously used optimization techniques.
(US Published Application 20230153373 to Wang) teaches continuous-time quantum walk (CTQW) is implemented on a weighted, undirected graph that connects the feasible solutions. An iterative optimizer tunes the quantum circuit parameters to maximize the probability of obtaining high-quality solutions from the final state. The ansatz circuit design ensures that only feasible solutions are obtained from the measurement. The disclosed method solves constrained problems without modifying their cost functions, confines the evolution of the quantum state to the feasible subspace, and does not rely on efficient indexing of the feasible solutions as some previous methods require.
13. Any inquiry concerning this communication or earlier communications from the Examiner should be directed to KEVIN L. SMITH whose telephone number is (571) 272-5964. Normally, the Examiner is available on Monday-Thursday 0730-1730.
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If attempts to reach the Examiner by telephone are unsuccessful, the Examiner’s supervisor, KAKALI CHAKI can be reached on 571-272-3719. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.L.S./
Examiner, Art Unit 2122
/KAKALI CHAKI/Supervisory Patent Examiner, Art Unit 2122