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 § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim 12 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim does not fall within at least one of the four categories of patent eligible subject matter because a program per se does not have physical or tangible form. The Examiner suggests adding the requisite language regarding storage and execution at least as disclosed in paragraph [0057]. See MPEP sections 2106, 2106.03.
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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(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.
Claims 1 and 11-12 are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Nam et al. [US 2020/0184024 A1].
Taking claim 1 as exemplary of the similarly recited method, as per claims 11-12, a quantum circuit generation device [FIGS. 18-20] comprising:
a setting unit [FIG. 18 elements 1854, 1850, paragraph 0130 lines 1-2, FIG. 19 element 1905] which sets a reference quantum circuit [FIG. 2] including a plurality of quantum operations [210a-230c] to be performed on a plurality of qubits [x,y]; and
a generation unit [FIG. 18 elements 1840, 1830] which generates a shortened quantum circuit [FIG. 5 right circuit, paragraphs 0070-0071 these rotations can be combined into a single rotation … the simplification; see also reduction of quantum circuits from 200 in FIG. 2 to 500 in FIG. 5 to 900 in FIG. 9] including quantum operations [510a-530c in FIG. 5] smaller [FIG. 19 element 1910 the second list of quantum gates being smaller than … the first list of quantum gates] in number than the quantum operations [210a-230c in FIG. 5] included in the reference quantum circuit [note: FIG. 2 is Fig. 5 left circuit], where the shortened quantum circuit [FIG. 5 right circuit] is so evaluated that a probability distribution obtained when quantum operations [510a-530c in FIG. 5] by the shortened quantum circuit [FIG. 5 right circuit] are performed on the plurality of qubits [x,y] approximates to a probability distribution [FIG. 19 step 1910, functional equivalence: would produce approximate probability distribution] obtained when quantum operations [210a-230c on FIG. 5 left circuit] by the reference quantum circuit [FIG. 2 or Fig. 5 left circuit] are performed on the plurality of qubits [x,y].
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Nam et al. (US 20200184024) in view of Kelly et al. (US12443872).
As per claims 1 and 11-12, Nam shows a quantum circuit generation device (figs. 18-20) comprising:
a setting unit (fig 18: 1854, 1850, ¶0130, lines 1-2; fig 19: 1905) which sets a reference quantum circuit (fig. 2) including a plurality of quantum operations (210a - 230c) to be performed on a plurality of qubits (x, y); and
a generation unit (fig. 18: 1840, 1830) which generates a shortened quantum circuit (fig. 5, right circuit; ¶s 0070, 0071: these rotations can be combined into a single rotation … the simplification …; see also reduction of quantum circuits from 200 in fig. 2 to 500 in fig. 5 to 900 in fig. 9) including quantum operations (510a – 530c in fig. 5) smaller (fig. 19: 1910: the second list of quantum gates being smaller than … the first list of quantum gates) in number than the quantum operations (210a – 230c in fig. 5) included in the reference quantum circuit (note: fig. 2 is fig. 5 left circuit), where the shortened quantum circuit (fig. 5, right circuit) is so evaluated that a probability distribution obtained when quantum operations (510a – 530c in fig. 5) by the shortened quantum circuit (fig. 5, right circuit) are performed on the plurality of qubits (x, y) approximates to a probability distribution (fig. 19: 1910; functional equivalence: would produce approximate probability distribution) obtained when quantum operations (210a – 230c on fig. 5 left circuit ) by the reference quantum circuit (fig. 2; or fig. 5, left circuit), are performed on the plurality of qubits (x, y).
If Nam is found to be unclear regarding illustrating the probability distribution of results of the two quantum circuits, Kelly’s fig. 3 shows that the shading of the point represents the respective probability (Kelly col. 10, lines 50-59) of the measured results of the quantum circuits (Kelly fig. 3a-c; col. 10, lines 44-67; col. 11, lines 1-41). Kelly additional shows a cumulative distribution (col. 11, lines 3-7) and purity of the quantum states (col. 11, lines 23-25) of the quantum circuits. Noted that Nam’s intent is to generate a shortened (second) quantum circuit which is functional equivalent to the reference (first) quantum circuit (Nam fig. 19, element 1910), one skilled in the art would recognize that in order for two quantum circuits to be “functional equivalent”, the performance of the circuits or the fidelity of the quantum operations (Nam ¶0145) from the first and second quantum circuits would have similar probability distribution. Hence, it would have been obvious for one skilled in the art before the effective filing date of the present application to demonstrate such fidelity of the quantum circuits shown by Kelly in order to verify that the two quantum circuits are functional equivalent called by Nam (fig. 19, element 1910).
Claims 1-2 and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Gambetta et al. [US 11,321,625 B2].
Taking claim 1 as exemplary of claims 1 and 11-12, Gambetta et al. teach the following interpretation of a quantum circuit generation device [FIGS. 3 and 7] comprising:
a setting unit [FIG 3. element 312] which sets a reference quantum circuit [FIG. 7 element 710/720] including a plurality of quantum operations to be performed on a plurality of qubits [column 14, lines 40-42]; and
a generation unit [FIG. 3 element 314] which generates a shortened quantum circuit [FIG. 7 element 740] including quantum operations smaller in number than the quantum operations included in the reference quantum circuit [column 5, lines 20-23, column 6, lines 21-25], where the shortened quantum circuit is so evaluated that a probability distribution obtained when quantum operations by the shortened quantum circuit are performed on the plurality of qubits approximates to a probability distribution obtained when quantum operations by the reference quantum circuit are performed on the plurality of qubits [column 4, lines 10-14 probability distribution, column 8, lines 55-60 correctness is determined by comparing outputs which are represented as probability distribution given column 4, column 14, lines 42-51, column 16, lines 25-30 evaluating quantum circuit optimizations, lines 33-42 executing the quantum circuits to determine correctness which compares results/outputs, 51-53 evaluation indicates that the result/output (element 742 in FIG. 7) is equivalent to the original result 712 and includes fewer gates, column 17, lines 1-3].
However, Gambetta et al. only appear to mention probability distribution at column 4 and do not appear to concisely recite where a probability distribution approximates. Rather the reference is interpreted to teach the combination of features cited above result in a broadest reasonable interpretation of the shortened quantum circuit is so evaluated that a probability distribution obtained when quantum operations by the shortened quantum circuit are performed on the plurality of qubits approximates to a probability distribution obtained when quantum operations by the reference quantum circuit are performed on the plurality of qubits. Thus, 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 because the disclosed optimizing incurs lower cost while maintaining accuracy, by using a configuration of fewer qubits, accuracy refers to consistency with previous results, synonymous with correctness [column 3, lines 54-column 4, line 3, lines 10-14].
As per claim 2, the quantum circuit generation device according to claim 1, wherein the evaluation in the generation unit is made by
generating a plurality of intermediate quantum circuits including quantum operations smaller in number than the quantum operations included in the reference quantum circuit [column 4, lines 57-64 modularization by definition, column 16, lines 31-33 sections are interpreted as intermediate quantum circuits, see FIG. 7], and
exploring the shortened quantum circuit from among the plurality of intermediate quantum circuits based on an evaluation function in which a probability distribution obtained when quantum operations by the plurality of intermediate quantum circuits are performed on the plurality of qubits, and the number of quantum operations included in the plurality of intermediate quantum circuits are set as variables [following the reasoning provided above for the generation unit, FIG. 7 depicts exploring the sections accordingly, see, also, FIG. 9 and column 17, lines 14-30 which describes score to be further interpreted as a form exploring for evaluating and correctness].
Claims 3-10 are rejected under 35 U.S.C. 103 as being unpatentable over Gambetta et al. [US 11,321,625 B2] as applied to claim 1 above, and further in view of T. Fosel et al. [“Reinforcement Learning with Neural Networks for Quantum Feedback”]
As per claim 3, Gambetta et al. teach the quantum circuit generation device according to claim 1 (see above), further comprising
machine learning [column 1, lines 6-10] to output one or more quantum operations to be performed on the plurality of qubits so as to obtain a probability distribution that approximates to a probability distribution obtained when quantum operations by the reference quantum circuit are performed on the plurality of qubits [FIG. 3 using machine learning and executing, FIG. 7 evaluating quantum circuit optimizations using machine learning, column 4, lines 10-14 probability distribution, column 8, lines 55-60 correctness is determined by comparing outputs which are represented as probability distribution given column 4, column 14, lines 42-51, column 16, lines 25-30 evaluating quantum circuit optimizations, lines 33-42 executing the quantum circuits to determine correctness which compares results/outputs, 51-53 evaluation indicates that the result/output (element 742 in FIG. 7) is equivalent to the original result 712 and includes fewer gates, column 17, lines 1-3], wherein
the generation unit generates the shortened quantum circuit [FIG. 7 element 740] based on the one or more quantum operations output [column 14, lines 30-32 using machine learning, lines 45-51], and
the machine learning generates [column 16, lines 25-53 evaluating quantum circuit optimizations using machine learning] by
setting one or more quantum operations performed on the plurality of qubits as state [column 1, lines 30-44, column 4, lines 10-14 the program state and the output, column 14, lines 45-51],
setting quantum operations to be performed on the plurality of qubits as action [column 1, line 50-column 2, line 26, column 4, lines 10-14 the program state, possible outcomes, and the output, column 14, lines 45-51], and
calculating a reward based on a degree of coincidence between a probability distribution obtained when quantum operations by the reference quantum circuit are performed on the plurality of qubits, and a probability distribution obtained when the one or more quantum operations selected as the action are performed on the plurality of qubits [column 7, lines 58-60 reward is interpreted as measured by the efficiency score, column 8, lines 55-60 correctness is interpreted as a degree of coincidence by comparing outputs which are represented as probability distribution given column 4 if the execution outputs match by more than a threshold amount, column 16, lines 25-30 evaluating quantum circuit optimizations using machine learning, lines 33-42 executing the quantum circuits based on the setting identified above to determine correctness which compares results/outputs, 51-53 evaluation indicates that the result/output (element 742 in FIG. 7) is equivalent to the original result 712 and includes fewer gates, column 17, lines 1-3 both equivalent and more efficient, FIG. 9 and column 17, lines 14-30 which describes score to be further interpreted as a form of calculating a reward based on a degree of coincidence accordingly].
However, Gambetta et al. do not specify a reinforcement learning unit per se which generates an agent by reinforcement learning. Rather, Gambetta et al. describe using machine learning as cited for the features identified above. T. Fosel et al. is provided as exemplary of using a specific form of machine learning comprising a reinforcement learning unit which generates an agent by reinforcement learning (see the entire document which describes reinforcement learning in terms of quantum error correction strategies, applicable to correctness identified in Gambetta). Thus, 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 because reinforcement learning has an immediate impact on quantum computation [Abstract], on the machine-learning side, RL promises to be a flexible and general tool of wide-ranging applicability for exploring feedback-based control of quantum and classical systems in physics [VI. Conclusion second paragraph].
As per claim 4, the quantum circuit generation device according to claim 3, wherein the generation unit determines whether or not to end the selection of the action by the agent based on the reward calculated by comparing the degree of coincidence with a threshold value [both Gambetta et al. as above and T. Fosel et al.].
As per claim 5, the quantum circuit generation device according to claim 3, wherein the degree of coincidence is given as a function in which a probability distribution obtained when quantum operations by the reference quantum circuit are performed on the plurality of qubits, and a probability distribution obtained when the one or more quantum operation selected as the action are performed on the plurality of qubits are set as variables [see Gambetta et al. as interpreted above].
As per claim 6, the quantum circuit generation device according to claim 5, wherein the function includes Kolmogorov distance or Bhattacharyya coefficient [the Examiner takes official notice that these functions are old and well known, see, for example, C. A. Fuchs et al. cited on PTO-892].
As per claim 7, the quantum circuit generation device according to claim 3, wherein the agent selects the action using Monte Carlo Tree Search [the Examiner takes official notice that using Monte Carlo Tree Search is old and well known as particularly suited for reinforcement learning, see, for example, Wikipedia].
As per claim 8, the quantum circuit generation device according to claim 7, wherein the agent performs the Monte Carlo Tree Search based on output values obtained by inputting the state to a neural network [the Examiner takes official notice that performing Monte Carlo Tree Search is old and well known as particularly suited for reinforcement learning, see, for example, Wikipedia].
As per claim 9, the quantum circuit generation device according to claim 8, wherein the generation unit updates parameters of the neural network based on learning data obtained by simulating selection of the action using the Monte Carlo Tree Search and calculation of the reward a plurality of times [the Examiner takes official notice that using Monte Carlo Tree Search is old and well known as particularly suited for reinforcement learning, see, for example, Wikipedia].
As per claim 10, the quantum circuit generation device according to claim 3, wherein the generation unit calculates the reward based on the degree of coincidence and the number of one or more quantum operations included in the state [see Gambetta et al. as interpreted above].
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Gambetta et al [US 10,977,546 B2] disclose short depth quantum circuits, including machine learning (entire document). Barenco et al. disclose “Approximate quantum Fourier transform and decoherence.” C.A. Fuchs et al. ["Cryptographic Distinguishability Measures for Quantum-Mechanical States"] disclose Bhattacharyya coefficient and Kolmogorov distance (Abstract). K. Mitarai et al. disclose “Quantum circuit learning” (entire document). K. Temme et al. disclose "Error mitigation for short-depth quantum circuits" (entire document). Y. Nam et al. disclose "Approximate quantum Fourier transform with O(n log(n)) T gates.” L. P. Kaelbling et al. disclose “Reinforcement Learning: A Survey” and K. Arulkumaran et al. “Deep Reinforcement Learning”.
Conclusion
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/JACK CHIANG/ Supervisory Patent Examiner, Art Unit 2851