Detailed Action
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This office action is final and is in response to claims filed on 03/17/2026 via amendment. Claims 1-9 and 11 are pending examination. Claims 1-9 and 11 are currently amended.
Response to Arguments
Objections to the abstract
Applicant has provided an abstract on a separate sheet apart from any other text. Therefore, the previous objection to the abstract has been withdrawn.
Rejections under 35 U.S.C. 112
Applicant has amended the claims at issue, and, therefore, the previous rejections have been withdrawn.
Rejections under 35 U.S.C. 101
Applicant’s arguments, see Remarks, filed 03/17/2026, with respect to claims 1-9, and 11 have been fully considered and are persuasive. Therefore, the rejections under 35 U.S.C. 101 have been withdrawn.
Specifically, Applicant’s remarks on pages 27-28 of the Remarks that the judicial exceptions are integrated into a practical application are persuasive.
Rejections under 35 U.S.C. 103
Applicant’s arguments regarding the 35 U.S.C. 103 rejections have been fully considered. Applicant argues “Based on the teachings above, it can be seen that You et al. does not disclose the claimed "applying, by the at least one digital processor, an algorithm to the computational problem". Here, "the computational problem" refers to the overall computational optimization problem to be solved by the method of amended independent claim 1 or the MILP, MINP, MIQP, or IQFP of You et al. In You et al., the classical computer 102 executes the first algorithm for the first sub-problem and not for the entire computational problem to be solved”. See Remarks 32.
Examiner respectfully disagrees with Applicant’s arguments. You paragraph [0246] recites “splitting, by the classical computing device, the computation model (e.g. the quadratic unconstrained binary optimization model) into two or more sub-problems (e.g. two or more sub-quadratic unconstrained binary optimization models which are each expressed into a Chimera lattice of qubits on the quantum computing device); iteratively minimizing, by the quantum computing device, each of the sub-problems (e.g. sub-quadratic unconstrained binary optimization models) expressed as the Chimera lattices of qubits by selecting different variables”. This shows that, while the problem is split into sub-problems, the algorithm still solves the whole problem.
Applicant further argues “Unlike the claimed limitations, the construction (not evaluation) of the claimed Hamiltonian does not realize a selection of one of two candidate values of each arbitrary variable vi of the computational problem and does not include any use of a binary value si for candidate value selection”. See Remarks 35.
Examiner respectfully disagrees with Applicant’s arguments. You is used to read on the variables and constructing the Hamiltonian using binary values. Padovani is used to read on the two candidate values.
Further, under the broadest reasonable interpretation of the claims, the Hamiltonian is constructed and used to determine which of the candidate values to take. Nowhere in the claims is the construction of the Hamiltonian used to determine which of the candidate values to take.
Applicant further argues “a person of skill in the art would not be motivated to combine the operation of a classical-quantum hybrid computing system of You et al. with the power management of a fossil fuel-electric hybrid vehicle of Padovani et al. The methods disclosed by these two references are in disparate fields and use unrelated technologies for the execution thereof”. See Remarks 35.
Examiner respectfully disagrees with Applicant’s arguments. It has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, both You and Padovani use computer systems and Hamiltonians to minimize the energy of the system, see You [0006] and Padovani Page 9 Paragraph 1.
Applicant further argues “As taught above, management device 13 is realized by only classical computing means and can only be used for the specified purpose of hybrid motor vehicle power management. One would not incorporate use of the quantum processor of You et al. into the on- board motor vehicle power management of Padovani et al., as the Hamiltonian of Padovani et al. is taught to be simply evaluated by classical means using management device 13 and there is no further computation to be performed by a quantum computer”. See Remarks 36.
Examiner respectfully disagrees with Applicant’s arguments. The test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Information Disclosure Statement
The Information Disclosure Statement (IDS) submitted on 03/09/2026 is in compliance with the provisions of 37 CFR 1.97, 1.98, and MPEP §609. It has been placed in the application file, and the information referred to therein has been considered as to the merits.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 5-9, and 11 are rejected under 35 U.S.C. 103 as being unpatentable over You et al. (US 20230419155 A1) hereinafter You, in view of Padovani et al. (English translation of WO 2016146911 A1) hereinafter Padovani further in view of Raymond et al. (US 20160071021 A1) hereinafter Raymond.
With regards to claim 1, You teaches A method to obtain a solution to a computational problem having n arbitrary variables vi using a processor-based system, (You [0004]: wherein the optimization problem is decomposed into a first sub-problem to be solved by a first algorithm on the classical computer… wherein the first sub-problem has a first set of one or more variables)
the processor- based system comprising at least one digital processor communicatively coupled to a quantum processor that includes a plurality of qubits, (You [0246]: splitting, by the classical computing device, the computation model (e.g. the quadratic unconstrained binary optimization model) into two or more sub-problems (e.g. two or more sub-quadratic unconstrained binary optimization models which are each expressed into a Chimera lattice of qubits on the quantum computing device; You [0018]: a communication engine configured to control a communication circuitry to send and/or receive data between the quantum computer part and a classical computer part (e.g. one or more classical computers) optionally via a local or remote network)
the method comprising: applying, by the at least one digital processor, an algorithm to the computational problem (You [0004]: wherein the optimization problem is decomposed into a first sub-problem to be solved by a first algorithm on the classical computer… wherein the first sub-problem has a first set of one or more variables)
constructing, by the at least one digital processor, a Hamiltonian representation of the computational problem that uses a binary value si [to determine which of the two candidate values each arbitrary variable vi should take;] (You [0065]: second sub-problem to be solved on the quantum computer 104 is expressed in the form of a quadratic unconstrained binary optimization (QUBO) problem with several binary variables; You [0080]: In block 812, the quantum computer 104 solves the QUBO problem described in Eq. (12). In this block, the quantum computer 104 determines a schedule for each machine and the assigned jobs. The quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor)
constructing, by the at least one digital processor, a binary quadratic model based on the Hamiltonian representation of the computational problem; (You [0004]: a second sub-problem to be solved by a second algorithm on a quantum processor; You [0005]: second problem is a quadratic unconstrained binary optimization (QUBO) problem; You [0080]: In block 812, the quantum computer 104 solves the QUBO problem described in Eq. (12). In this block, the quantum computer 104 determines a schedule for each machine and the assigned jobs. The quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor)
embedding, by the at least one digital processor, the binary quadratic model onto the plurality of qubits of the quantum processor; (You [0246]: splitting, by the classical computing device, the computation model (e.g. the quadratic unconstrained binary optimization model) into two or more sub-problems (e.g. two or more sub-quadratic unconstrained binary optimization models which are each expressed into a Chimera lattice of qubits on the quantum computing device)
causing, by control signals transmitted by the at least one digital processor, the plurality of qubits in the quantum processor [to undergo at least one of quantum annealing and adiabatic quantum computation to obtain a set of qubit states;] (You [0018]: a communication engine configured to control a communication circuitry to send and/or receive data between the quantum computer part and a classical computer part (e.g. one or more classical computers) optionally via a local or remote network; You [0246]: splitting, by the classical computing device, the computation model (e.g. the quadratic unconstrained binary optimization model) into two or more sub-problems (e.g. two or more sub-quadratic unconstrained binary optimization models which are each expressed into a Chimera lattice of qubits on the quantum computing device)
obtaining, by the at least one digital processor, the set of qubit states from the quantum processor as samples from the binary quadratic model the samples from the binary quadratic model that provides the solution to the computational problem (You [0080]: The quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor 302 in order to locate a feasible schedule in the integer space. The result of the QUBO is then sent to the classical computer 102; You [0246]: splitting, by the classical computing device, the computation model (e.g. the quadratic unconstrained binary optimization model) into two or more sub-problems (e.g. two or more sub-quadratic unconstrained binary optimization models which are each expressed into a Chimera lattice of qubits on the quantum computing device… iteratively minimizing, by the quantum computing device, each of the sub-problems (e.g. sub-quadratic unconstrained binary optimization models) expressed as the Chimera lattices of qubits by selecting different variables associated with each pair of beads and in view of one or more constraints related to each of the pair of beads until a total potential energy of the sub-problems (e.g. sub-quadratic unconstrained binary optimization models) is minimized; and outputting, by the quantum computing device or a classical computing device communicatively coupled to the quantum computing device, an optimized location for each of the pair of the beads, a bond length between each of the pair of the beads, and/or a bond angle for each of the pair of the beads for the molecule associated with the minimized total potential energy).
You fails to teach obtaining, by the at least one digital processor, two candidate values for each arbitrary variable vi of the computational problem from the algorithm; and [constructing, by the at least one digital processor, a Hamiltonian representation of the computational problem that uses a binary value si] to determine which of the two candidate values each arbitrary variable vi should take.
However, Padovani teaches obtaining, by the at least one digital processor, two candidate values for each arbitrary variable vi of the computational problem from the algorithm; (Padovani Page 9 Paragraph 1: For each pair of candidate values of the plurality of pairs of candidate values, during a selection step 43, an energy criterion H, called Hamiltonian, is evaluated in the optimal control theory, so as to determine which candidate value returns the lowest energy criterion value)
[constructing, by the at least one digital processor, a Hamiltonian representation of the computational problem that uses a binary value si] to determine which of the two candidate values each arbitrary variable vi should take (Padovani Page 9 Paragraph 1: For each pair of candidate values of the plurality of pairs of candidate values, during a selection step 43, an energy criterion H, called Hamiltonian, is evaluated in the optimal control theory, so as to determine which candidate value returns the lowest energy criterion value).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to combine the teachings of You with the two candidate values of Padovani. One of ordinary skill in the art would be motivated to make this combination because it would reduce the energy consumption of the system as the system would determine which candidate value returns the lowest energy criterion value as taught by Padovani (Padovani Page 9 Paragraph 1).
You in view of Padovani fails to teach [causing, by control signals transmitted by the at least one digital processor, the plurality of qubits in the quantum processor to] undergo at least one of quantum annealing and adiabatic quantum computation to obtain a set of qubit states.
However, Raymond teaches [causing, by control signals transmitted by the at least one digital processor, the plurality of qubits in the quantum processor to] undergo at least one of quantum annealing and adiabatic quantum computation to obtain a set of qubit states; (Raymond [0012]: Many techniques for using quantum annealing to solve computational problems involve finding ways to directly map/embed a representation of a problem to the quantum processor. Generally, a problem is solved by first casting the problem in a contrived formulation (e.g., Ising spin glass, QUBO, etc.)).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to combine the teachings of You in view of Padovani with the quantum annealing of Raymond. One of ordinary skill in the art would be motivated to make this combination because this operation is guaranteed to converge to the target distribution over sufficient time (at finite β) or, as discussed above, will at least lead to a more uniform distribution as taught by Raymond (Raymond [0251]). Also, such an approach, referred to as “annealed importance sampling,” may allow the system to avoid becoming biased to local optima and thus perform a more complete search as taught by Raymond (Raymond [097]).
With regards to claim 2, You in view of Padovani further in view of Raymond teaches all of the limitations of claim 1 above. You further teaches wherein applying the algorithm to the computational problem includes [applying a Gibbs sampler] to the computational problem with n arbitrary variables vi; (You [0004]: wherein the optimization problem is decomposed into a first sub-problem to be solved by a first algorithm on the classical computer… wherein the first sub-problem has a first set of one or more variables).
You fails to teach and obtaining the two candidate values for each arbitrary variable vi from the algorithm includes obtaining two candidate values for each arbitrary variable vi from the Gibbs sampler.
However, Padovani teaches and obtaining the two candidate values for each arbitrary variable vi from the algorithm includes obtaining two candidate values for each arbitrary variable vi from [the Gibbs sampler] (Padovani Page 9 Paragraph 1: For each pair of candidate values of the plurality of pairs of candidate values, during a selection step 43, an energy criterion H, called Hamiltonian, is evaluated in the optimal control theory, so as to determine which candidate value returns the lowest energy criterion value).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to combine the teachings of You in view of Padovani further in view of Raymond with the two candidate values of Padovani. One of ordinary skill in the art would be motivated to make this combination because it would reduce the energy consumption of the system as the system would determine which candidate value returns the lowest energy criterion value as taught by Padovani (Padovani Page 9 Paragraph 1).
You in view of Padovani fails to teach applying a Gibbs sampler to the problem of You and obtaining two candidate values for each arbitrary variable vi of Padovani from the Gibbs sampler.
However, Raymond teaches applying a Gibbs sampler to the problem of You (Raymond [0251]: Another example of a fast iteration scheme is Gibbs sampling (e.g. blocked Gibbs sampling). Using the independence of x.sub.2 given x.sub.1 and the independence of every element x.sub.1 given x.sub.2, the processor-based device may execute a Markov chain alternating sampling between the two halves)
Obtaining the two candidate values for each arbitrary variable vi of Padovani from the Gibbs sampler (Raymond [0251]: Another example of a fast iteration scheme is Gibbs sampling (e.g. blocked Gibbs sampling). Using the independence of x.sub.2 given x.sub.1 and the independence of every element x.sub.1 given x.sub.2, the processor-based device may execute a Markov chain alternating sampling between the two halves).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to combine the teachings of You in view of Padovani further in view of Raymond with the Gibbs sampler of Raymond. One of ordinary skill in the art would be motivated to make this combination because this operation is guaranteed to converge to the target distribution over sufficient time (at finite β) or, as discussed above, will at least lead to a more uniform distribution as taught by Raymond (Raymond [0251]).
With regards to claim 3, You in view of Padovani further in view of Raymond teaches all of the limitations of claim 1 above. You further teaches wherein applying the algorithm to the computational problem includes: for each of the arbitrary variables, computing an energy of each state of the arbitrary variable based on an interaction of the arbitrary variable with other arbitrary variables; (You [0004]: a second sub-problem to be solved by a second algorithm on a quantum processor; You [0005]: second problem is a quadratic unconstrained binary optimization (QUBO) problem; You [0080]: In block 812, the quantum computer 104 solves the QUBO problem described in Eq. (12). In this block, the quantum computer 104 determines a schedule for each machine and the assigned jobs. The quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor; You equation 12: shows a Hamiltonian that has a number of arbitrary variables)
for each of the arbitrary variables, [computing a respective exponential weight] for the arbitrary variable for each of a number Di of distinct values of the arbitrary variable; (You equation 12: shows a Hamiltonian that has a number of arbitrary variables; You [0082]: the variable assignments of the QUBO solution are sent from the quantum computer 104 to the classical computer; You [0083]: it should be noted that the hybrid QC-MILP decomposition method converges to an optimal solution or proves infeasibility in finite number of iteration)
[and computing normalized probabilities] that each arbitrary variable [takes one of the values] Di, [proportional to the exponential weights.] (You equation 12: shows a Hamiltonian that has a number of arbitrary variables; You [0082]: the variable assignments of the QUBO solution are sent from the quantum computer 104 to the classical computer; You [0083]: it should be noted that the hybrid QC-MILP decomposition method converges to an optimal solution or proves infeasibility in finite number of iteration).
You fails to teach computing a respective exponential weight and and computing normalized probabilities [that each arbitrary variable] takes one of the values [Di,] proportional to the exponential weights.
However, Raymond teaches computing a respective exponential weight (Raymond [0096]: The Standard Simulated Annealing (SA) algorithm is a memoryless optimization approach in which the transitions between solutions are independent from the previous search states; Raymond [0097]: One variation of the SA algorithm is to provide weights corresponding to the importance of exploring particular areas in the energy landscape of the problem)
and computing normalized probabilities [that each arbitrary variable] takes one of the values [Di,] proportional to the exponential weights (Raymond [0031]: operating the quantum processor as a sample generator to provide samples from a probability distribution, wherein a shape of the probability distribution depends on a configuration of a number of programmable parameters for the quantum processor and a number of low-energy states of the quantum processor; Raymond [0096]: The Standard Simulated Annealing (SA) algorithm is a memoryless optimization approach in which the transitions between solutions are independent from the previous search states; Raymond [0097]: One variation of the SA algorithm is to provide weights corresponding to the importance of exploring particular areas in the energy landscape of the problem).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to combine the teachings of You in view of Padovani further in view of Raymond with the weights and probabilities of Raymond. One of ordinary skill in the art would be motivated to make this combination because such an approach, referred to as “annealed importance sampling,” may allow the system to avoid becoming biased to local optima and thus perform a more complete search as taught by Raymond (Raymond [097]).
With regards to claim 5, You in view of Padovani further in view of Raymond teaches all of the limitations of claim 1 above. You further teaches wherein constructing the binary quadratic model includes defining a new variable xi in terms of si [and the two candidate values] (You [0065]: second sub-problem to be solved on the quantum computer 104 is expressed in the form of a quadratic unconstrained binary optimization (QUBO) problem with several binary variables; You [0080]: In block 812, the quantum computer 104 solves the QUBO problem described in Eq. (12). In this block, the quantum computer 104 determines a schedule for each machine and the assigned jobs. The quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor; You Equation 12: shows the quadratic model in terms of xim and Pim)
and converting the computational problem to an optimization problem in the space of si (You [0018]: In accordance with the present disclosure, a hybrid quantum computer-classical computer (QC-CC) based computation system, comprising: a quantum computer part (e.g. one or more quantum computers, or a quantum computer subsystem of a hybrid system) comprises a quantum processor configured to solve at least one type of computation problem (e.g. one type of optimization problem, or one type of computation problem that is convertible or can be reformulated to an optimization problem); You [0074]: Binary variables x.sub.im are assignment variables that indicate whether job i is assigned to machine m; You [0127]: The present QC-CC based hybrid systems and methods can also be used to solve any other computation problems that could be converted or formulated as optimization problems).
You fails to teach that the binary quadratic model includes the two candidate values.
However, Padovani teaches constructing the binary quadratic model of You with the two candidate values (Padovani Page 9 Paragraph 1: For each pair of candidate values of the plurality of pairs of candidate values, during a selection step 43, an energy criterion H, called Hamiltonian, is evaluated in the optimal control theory, so as to determine which candidate value returns the lowest energy criterion value).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to combine the teachings of You in view of Padovani further in view of Raymond with the two candidate values of Padovani. One of ordinary skill in the art would be motivated to make this combination because it would reduce the energy consumption of the system as the system would determine which candidate value returns the lowest energy criterion value as taught by Padovani (Padovani Page 9 Paragraph 1).
With regards to claim 6, You in view of Padovani further in view of Raymond teaches all of the limitations of claim 1 above. You further teaches wherein constructing the binary quadratic model includes relaxing a constrained binary optimization problem into an unconstrained binary optimization problem using a penalty term; (You [0065]: second sub-problem to be solved on the quantum computer 104 is expressed in the form of a quadratic unconstrained binary optimization (QUBO) problem with several binary variables; You [0076]: In block 802, the classical computer 102 decomposes the MILP problem of Eqs. (1)-(9) into a relaxed MILP problem and a QUBO problem; You [0080]: In block 812, the quantum computer 104 solves the QUBO problem described in Eq. (12). In this block, the quantum computer 104 determines a schedule for each machine and the assigned jobs. The quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor)
and summing over [the two candidate values] (You [0065]: second sub-problem to be solved on the quantum computer 104 is expressed in the form of a quadratic unconstrained binary optimization (QUBO) problem with several binary variables; You [0080]: In block 812, the quantum computer 104 solves the QUBO problem described in Eq. (12). In this block, the quantum computer 104 determines a schedule for each machine and the assigned jobs. The quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor; You Equation 12: shows the Hamiltonian summing over values).
You fails to teach summing over the two candidate values.
However, Padovani teaches summing over the two candidate values (Padovani Page 9 Paragraph 1: For each pair of candidate values of the plurality of pairs of candidate values, during a selection step 43, an energy criterion H, called Hamiltonian, is evaluated in the optimal control theory, so as to determine which candidate value returns the lowest energy criterion value).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to combine the teachings of You in view of Padovani further in view of Raymond with the two candidate values of Padovani. One of ordinary skill in the art would be motivated to make this combination because it would reduce the energy consumption of the system as the system would determine which candidate value returns the lowest energy criterion value as taught by Padovani (Padovani Page 9 Paragraph 1).
With regards to claim 7, You in view of Padovani further in view of Raymond teaches all of the limitations of claim 1 above. You further teaches wherein the embedding, by the at least one digital processor, the binary quadratic model of the computational problem onto the plurality of qubits of the quantum processor further comprising [applying an embedding algorithm to the binary quadratic model to generate an embedded representation of the computational problem] to send to the quantum processor (You [0004]: a second sub-problem to be solved by a second algorithm on a quantum processor; You [0005] second problem is a quadratic unconstrained binary optimization (QUBO) problem; You [0080]: In block 812, the quantum computer 104 solves the QUBO problem described in Eq. (12). In this block, the quantum computer 104 determines a schedule for each machine and the assigned jobs. The quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor 302 in order to locate a feasible schedule in the integer space. The result of the QUBO is then sent to the classical computer 102; You [0246]: splitting, by the classical computing device, the computation model (e.g. the quadratic unconstrained binary optimization model) into two or more sub-problems (e.g. two or more sub-quadratic unconstrained binary optimization models which are each expressed into a Chimera lattice of qubits on the quantum computing device).
You fails to teach applying an embedding algorithm to the binary quadratic model to generate an embedded representation of the computational problem.
However, Raymond teaches applying an embedding algorithm to the binary quadratic model to generate an embedded representation of the computational problem (Raymond [0012]: Many techniques for using quantum annealing to solve computational problems involve finding ways to directly map/embed a representation of a problem to the quantum processor. Generally, a problem is solved by first casting the problem in a contrived formulation (e.g., Ising spin glass, QUBO, etc.)).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to combine the teachings of You in view of Padovani further in view of Raymond with the embedding algorithm of Raymond. One of ordinary skill in the art would be motivated to make this combination because this would allow the quantum processor to solve the QUBO, speeding up operations. Also, because that particular formulation maps directly to the particular embodiment of the quantum processor being employed as taught by Raymond (Raymond [0012]).
With regards to claim 8, You in view of Padovani teaches all of the limitations of claim 1 above. You further teaches further comprising: iteratively repeating until an exit condition is met: (You [0004]: (iii) executing the second algorithm for the second sub-problem with use of the quantum processor based on the current result of the first algorithm to determine a current result of the second algorithm; (iv) executing the first algorithm for the first sub-problem with use of the classical computer and with use of the current result of the second algorithm and optionally with use of the current result of the first algorithm to determine the current result of the first algorithm; (v) repeating steps (iii) and (iv) one or more times; (vi) stop the repeating steps until a preset convergence criteria is reached)
applying, by the at least one digital processor, the algorithm to the computational problem; (You [0004]: wherein the optimization problem is decomposed into a first sub-problem to be solved by a first algorithm on the classical computer… wherein the first sub-problem has a first set of one or more variables)
constructing, by the at least one digital processor, a current Hamiltonian representation of the computational problem that uses a binary value si [to determine which of the two current candidate values each arbitrary variable vi should take] (You [0065]: second sub-problem to be solved on the quantum computer 104 is expressed in the form of a quadratic unconstrained binary optimization (QUBO) problem with several binary variables; You [0080]: In block 812, the quantum computer 104 solves the QUBO problem described in Eq. (12). In this block, the quantum computer 104 determines a schedule for each machine and the assigned jobs. The quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor)
constructing, by the at least one digital processor, a current binary quadratic model based on the current Hamiltonian representation of the computational problem; (You [0004]: a second sub-problem to be solved by a second algorithm on a quantum processor; You [0005]: second problem is a quadratic unconstrained binary optimization (QUBO) problem; You [0080]: In block 812, the quantum computer 104 solves the QUBO problem described in Eq. (12). In this block, the quantum computer 104 determines a schedule for each machine and the assigned jobs. The quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor)
embedding, by the at least one digital processor, the current binary quadratic model onto the plurality of qubits of the quantum processor; (You [0246]: splitting, by the classical computing device, the computation model (e.g. the quadratic unconstrained binary optimization model) into two or more sub-problems (e.g. two or more sub-quadratic unconstrained binary optimization models which are each expressed into a Chimera lattice of qubits on the quantum computing device)
causing, by control signals transmitted by the at least one digital processor, the plurality of qubits in the quantum processor [to undergo at least one of quantum annealing and adiabatic quantum computation to obtain a current set of qubit states;] (You [0018]: a communication engine configured to control a communication circuitry to send and/or receive data between the quantum computer part and a classical computer part (e.g. one or more classical computers) optionally via a local or remote network; You [0246]: splitting, by the classical computing device, the computation model (e.g. the quadratic unconstrained binary optimization model) into two or more sub-problems (e.g. two or more sub-quadratic unconstrained binary optimization models which are each expressed into a Chimera lattice of qubits on the quantum computing device)
obtaining, by the at least one digital processor, the current set of qubit states from the quantum processor as current samples from the current binary quadratic model (You [0080]: The quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor 302 in order to locate a feasible schedule in the integer space. The result of the QUBO is then sent to the classical computer 102; You [0246]: splitting, by the classical computing device, the computation model (e.g. the quadratic unconstrained binary optimization model) into two or more sub-problems (e.g. two or more sub-quadratic unconstrained binary optimization models which are each expressed into a Chimera lattice of qubits on the quantum computing device… iteratively minimizing, by the quantum computing device, each of the sub-problems (e.g. sub-quadratic unconstrained binary optimization models) expressed as the Chimera lattices of qubits by selecting different variables associated with each pair of beads and in view of one or more constraints related to each of the pair of beads until a total potential energy of the sub-problems (e.g. sub-quadratic unconstrained binary optimization models) is minimized; and outputting, by the quantum computing device or a classical computing device communicatively coupled to the quantum computing device, an optimized location for each of the pair of the beads, a bond length between each of the pair of the beads, and/or a bond angle for each of the pair of the beads for the molecule associated with the minimized total potential energy).
and integrating the current samples into the computational problem (You [0004]: (iii) executing the second algorithm for the second sub-problem with use of the quantum processor based on the current result of the first algorithm to determine a current result of the second algorithm; (iv) executing the first algorithm for the first sub-problem with use of the classical computer and with use of the current result of the second algorithm and optionally with use of the current result of the first algorithm to determine the current result of the first algorithm; (v) repeating steps (iii) and (iv) one or more times; (vi) stop the repeating steps until a preset convergence criteria is reached).
You fails to teach obtaining, by the at least one digital processor, two current candidate values for each arbitrary variable vi from the algorithm; and [constructing, by the at least one digital processor, a current Hamiltonian representation of the computational problem that uses a binary value si] to determine which of the two candidate values each arbitrary variable vi should take.
However, Padovani teaches obtaining, by the at least one digital processor, two current candidate values for each arbitrary variable vi from the algorithm; (Padovani Page 9 Paragraph 1: For each pair of candidate values of the plurality of pairs of candidate values, during a selection step 43, an energy criterion H, called Hamiltonian, is evaluated in the optimal control theory, so as to determine which candidate value returns the lowest energy criterion value)
[constructing, by the at least one digital processor, a current Hamiltonian representation of the computational problem that uses a binary value si] to determine which of the two candidate values each arbitrary variable vi should take (Padovani Page 9 Paragraph 1: For each pair of candidate values of the plurality of pairs of candidate values, during a selection step 43, an energy criterion H, called Hamiltonian, is evaluated in the optimal control theory, so as to determine which candidate value returns the lowest energy criterion value).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to combine the teachings of You in view of Padovani further in view of Raymond with the two candidate values of Padovani. One of ordinary skill in the art would be motivated to make this combination because it would reduce the energy consumption of the system as the system would determine which candidate value returns the lowest energy criterion value as taught by Padovani (Padovani Page 9 Paragraph 1).
You in view of Padovani fails to teach [causing, by control signals transmitted by the at least one digital processor, the plurality of the qubits in the quantum processor to] undergo at least one of quantum annealing and adiabatic quantum computation to obtain a current set of qubit states.
However, Raymond teaches [causing, by control signals transmitted by the at least one digital processor, the plurality of the qubits in the quantum processor to] undergo at least one of quantum annealing and adiabatic quantum computation to obtain a current set of qubit states; (Raymond [0012]: Many techniques for using quantum annealing to solve computational problems involve finding ways to directly map/embed a representation of a problem to the quantum processor. Generally, a problem is solved by first casting the problem in a contrived formulation (e.g., Ising spin glass, QUBO, etc.)).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to combine the teachings of You in view of Padovani further in view of Raymond with the quantum annealing of Raymond. One of ordinary skill in the art would be motivated to make this combination because this operation is guaranteed to converge to the target distribution over sufficient time (at finite β) or, as discussed above, will at least lead to a more uniform distribution as taught by Raymond (Raymond [0251]). Also, such an approach, referred to as “annealed importance sampling,” may allow the system to avoid becoming biased to local optima and thus perform a more complete search as taught by Raymond (Raymond [097]).
With regards to claim 9, You in view of Padovani further in view of Raymond teaches all of the limitations of claim 1 above. You further teaches further comprising determining whether the exit condition has been met (You [0004]: (iii) executing the second algorithm for the second sub-problem with use of the quantum processor based on the current result of the first algorithm to determine a current result of the second algorithm; (iv) executing the first algorithm for the first sub-problem with use of the classical computer and with use of the current result of the second algorithm and optionally with use of the current result of the first algorithm to determine the current result of the first algorithm; (v) repeating steps (iii) and (iv) one or more times; (vi) stop the repeating steps until a preset convergence criteria is reached)
and wherein determining whether the exit condition has been met includes determining whether a measure representative of a quality assessment of the arbitrary variables is satisfied (You [0004]: stop the repeating steps until a preset convergence criteria is reached).
With regards to claim 11, You in view of Padovani further in view of Raymond teaches all of the limitations of claim 1 above. You further teaches wherein the computational problem is a resource scheduling problem (You [0256]: establishing, by a quantum processor in a quantum computing device, a non-quantum processor in a classical computing device, a mixed integer linear or mixed integer non-linear programming model to schedule a plurality of jobs on a plurality of machines with at least a set of constraints).
Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over You in view of Padovani further in view of Raymond Further in view of Alesiani et al. (US 20190272752 A1) hereinafter Alesiani.
With regards to claim 4, You in view of Padovani further in view of Raymond teaches all of the limitations of claim 1 above. You further teaches wherein applying the algorithm to the computational problem includes: for each of the arbitrary variables, computing an energy of the arbitrary variable as a function of a magnitude of the arbitrary variable and a current state of all of the other arbitrary variables; (You [0004]: a second sub-problem to be solved by a second algorithm on a quantum processor; You [0005]: second problem is a quadratic unconstrained binary optimization (QUBO) problem; You [0080]: In block 812, the quantum computer 104 solves the QUBO problem described in Eq. (12). In this block, the quantum computer 104 determines a schedule for each machine and the assigned jobs. The quantum computer 104 uses the partial optimal solutions from the classical computer 102 to solve the Hamiltonian using the quantum processor; You equation 12: shows a Hamiltonian that has a number of arbitrary variables)
for each of the arbitrary variables, [computing a respective exponential weight] for the arbitrary variable for each of a number Di of distinct values of the arbitrary variable; (You equation 12: shows a Hamiltonian that has a number of arbitrary variables; You [0082]: the variable assignments of the QUBO solution are sent from the quantum computer 104 to the classical computer; You [0083]: it should be noted that the hybrid QC-MILP decomposition method converges to an optimal solution or proves infeasibility in finite number of iteration)
for each of the arbitrary variables, computing a feasible region for the arbitrary variable, the feasible region comprising a set of values that respect a set of constraints; (You [0077]: The objective function (10) is the same as that of the original MILP model in Eq. (1). Constraints (2)-(4) and (9) form the constraints of the hybrid model; You [0078]: In block 804, the classical computer 102 solves the relaxed MILP problem. The classical computer 102 may use any suitable algorithm to solve the relaxed MILP problem, such as a deterministic Gurobi solver. Solutions to the relaxed MILP problem produces an assignment of machines to process each job)
for each of the arbitrary variables, [computing a mask] for the arbitrary variable at each of the respective number Di of distinct values; (You equation 12: shows a Hamiltonian that has a number of arbitrary variables; You [0082]: the variable assignments of the QUBO solution are sent from the quantum computer 104 to the classical computer; You [0083]: it should be noted that the hybrid QC-MILP decomposition method converges to an optimal solution or proves infeasibility in finite number of iteration)
and for each of the arbitrary variables, [computing normalized probabilities that collectively represent a probability that] the arbitrary variable [takes one of the respective number] Di of distinct values of the arbitrary variable, [proportional to the exponential weights and the mask.] (You equation 12: shows a Hamiltonian that has a number of arbitrary variables; You [0082]: the variable assignments of the QUBO solution are sent from the quantum computer 104 to the classical computer; You [0083]: it should be noted that the hybrid QC-MILP decomposition method converges to an optimal solution or proves infeasibility in finite number of iteration).
You fails to teach computing a respective exponential weight and and computing normalized probabilities that collectively represent a probability that [that each arbitrary variable] takes one of the values [Di,] proportional to the exponential weights and the mask.
However, Raymond teaches computing a respective exponential weight (Raymond [0096]: The Standard Simulated Annealing (SA) algorithm is a memoryless optimization approach in which the transitions between solutions are independent from the previous search states; Raymond [0097]: One variation of the SA algorithm is to provide weights corresponding to the importance of exploring particular areas in the energy landscape of the problem)
and computing normalized probabilities that collectively represent a probability that [that each arbitrary variable] takes one of the values [Di,] proportional to the exponential weights [and the mask] (Raymond [0031]: operating the quantum processor as a sample generator to provide samples from a probability distribution, wherein a shape of the probability distribution depends on a configuration of a number of programmable parameters for the quantum processor and a number of low-energy states of the quantum processor; Raymond [0096]: he Standard Simulated Annealing (SA) algorithm is a memoryless optimization approach in which the transitions between solutions are independent from the previous search states; Raymond [0097]: One variation of the SA algorithm is to provide weights corresponding to the importance of exploring particular areas in the energy landscape of the problem).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to combine the teachings of You in view of Padovani further in view of Raymond with the weights and probabilities of Raymond. One of ordinary skill in the art would be motivated to make this combination because such an approach, referred to as “annealed importance sampling,” may allow the system to avoid becoming biased to local optima and thus perform a more complete search as taught by Raymond (Raymond [097]).
You in view of Padovani further in view of Raymond fails to teach computing a mask and the probabilities being based on the mask.
However, Alesiani teaches computing a mask (Alesiani [0161]: The selection mask selects the bus holding variable that will be solved in the optimization cycle, each element of the mask is either 0 or 1. FIG. 10 illustrates an example of a selection mask; Alesiani [0162]: In mask probabilities, for each variable or each mask entry, a probability is defined that measures the rate of success of the variable)
And that the probabilities of You in view of Padovani further in view of Raymond being based on the mask (Alesiani [0161]: The selection mask selects the bus holding variable that will be solved in the optimization cycle, each element of the mask is either 0 or 1. FIG. 10 illustrates an example of a selection mask; Alesiani [0162]: In mask probabilities, for each variable or each mask entry, a probability is defined that measures the rate of success of the variable).
Therefore, it would have been obvious before the effective filing date of the claimed invention for one of ordinary skill in the art to combine the teachings of You in view of Padovani further in view of Raymond with the mask of Alesiani. One of ordinary skill in the art would be motivated to make this combination because this would increase the efficiency of the system as the mask is used to select the variable to be optimized as taught by Alesiani (Alesiani [0162]).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/J.O.G./Examiner, Art Unit 2151
/James Trujillo/Supervisory Patent Examiner, Art Unit 2151
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