DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1, 3, 12, and 15 with an earliest effective filing date of 3/7/22 is/are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Gambetta et al. (U.S. Publication No. 2020/0320437 published on 10/8/20 as cited on IDS).
With respect to claim 1, the Gambetta reference teaches a method for solving a regression problem over a problem domain using a hybrid computer system, the hybrid computer system comprising a quantum computer system and a classical computer system, the method comprising:
receiving or determining, by the classical computer system, a regression problem description and a set of kernel points in the problem domain (prepare quantum state [paragraph 91 and Figure 5] classification problem also applies to regression [paragraph 13]);
receiving or determining, by the classical computer system, a trial function associated with the regression problem, the trial function being a quantum kernel function based on a quantum kernel and being parameterized by a plurality of kernel coefficients (SVM objective function, training samples, the kernel parameters are optimized by the quantum computer and the remaining SVM parameters of the SVM object function by the classical computer [paragraphs 45-47, 55, 56, and 115]);
determining, using the quantum computer system, for each of the kernel points, a kernel value of the quantum kernel and/or a kernel derivative value of a derivative of the quantum kernel (step 1208 [figures 5 & 12 and paragraphs 81 & 114]);
determining, by the classical computer system, a set of optimal kernel coefficients based on the kernel value and/or kernel derivative value, and based on the regression problem description (classification optimization scheme to update parameters [Figures 5 & 12 and paragraphs 45-47, 55, 56, 91, 92, & 115]); and
determining, by the classical computer system, a solution function based on the trial function and the set of optimal kernel coefficients (classification using the classifier also applies to regression [paragraphs 13 and 116]).
With respect to claim 3, the Gambetta reference teaches all the limitations of claim 1 as described above. In addition, the Gambetta reference teaches that the regression problem includes data regression and wherein the regression problem description comprises a set of training data in the problem domain and associated training values (the optimization program uses training samples [paragraph 47]).
With respect to claim 12, the limitations of claim 12 are merely the hybrid computer system embodiment of claim 1 and claim 12 recites no further significant limitations therein. Therefore, the limitations of claim 12 are rejected in the analysis of claim 1 and claim 12 is likewise rejected on the same basis.
With respect to claim 15, the limitations of claim 15 are merely the non-transitory computer-readable storage medium embodiment of claim 1 and claim 15 recites no further significant limitations therein. Therefore, the limitations of claim 15 are rejected in the analysis of claim 1 and claim 15 is likewise rejected on the same basis.
Claims 2, 4-11, 13, and 16-21 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Herbert U.S. Patent No. 12,639,610
A quantum computing system includes a classical computer coupled in combination with a quantum computer, wherein the quantum computing system is configurable to execute program instructions to process input data to generate corresponding output data. The program instructions include one or more arithmetic functions to be executed using the quantum computer. The quantum computing system is configured to apply a transformation to transform the one or more arithmetic functions into a series of Fourier components that are executable using the quantum computer by using one or more quantum circuits utilizing rotation gates acting on qubits representing the Fourier components, and to process outputs from the one or more quantum circuits to generate results of the one or more arithmetic functions, wherein the results are used to generate the corresponding output data.
Corcoles-Gonzalez et al. U.S. Publication No. 2019/0378025
Techniques and a system to facilitate quantum computation are provided. In one example, a system includes a processor that executes computer executable components stored in a memory; a quantum feature map circuit component that estimates a kernel associated with a feature map; and a support vector machine component that performs a classification using the estimated kernel.
Cao U.S. Publication No. 2020/0104740
A hybrid quantum-classical (HQC) computer system, which includes a classical computer and a quantum computer, solves linear systems. The HQC computer system splits the linear system to be solved into subsystems that are small enough to be solved by the quantum computer, under control of the classical computer. The classical computer synthesizes the outputs of the quantum computer to generate the complete solution to the linear system.
Cao et al. U.S. Publication No. 2020/0327441
A hybrid quantum-classical computing method for solving optimization problems though applications of non-unitary transformations. An initial state is prepared, a transformation is applied, and the state is updated to provide an improved answer. This update procedure is iterated until convergence to an approximately optimal solution.
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/KRIS E MACKES/Primary Examiner, Art Unit 2153
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