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 .
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 line 18, claim 2 line 3, claim 11 line 16, and claim 12 line 3 recite “the unitary matrix”. This limitation lacks antecedent basis. It is unclear whether “the unitary matrix” refers to the estimated unitary matrix recited in line 5 or the current-iteration estimate of the unitary matrix recited in lines 10-11, or other. For purposes of examination, Examiner interprets as “a unitary matrix of the quantum computing device”. Claims 2-10 inherit the same deficiency as claim 1 based on dependence. Claims 3-4 inherit the same deficiency as claim 2 based on dependence. Claims 12-15 inherit the same deficiency as claim 11 based on dependence.
Claim 16 line 5, line 6, lines 10-11, line 16, and claim 17 lines 1- 2recite “the unitary matrix”. This limitation lacks antecedent basis. It is unclear to what unitary matrix this refers. Examiner interprets as “a unitary matrix”. Claims 17-20 inherit the same deficiency as claim 16 based on dependence.
Claim 16 lines 22-25 recite “at the quantum computing device and the processor, computing a plurality of estimated phase differences between the eigenvectors and the uniformly random eigenvector at least in part by performing iterative quantum phase estimated on the first register”. It is unclear which functions of this clause are performed by the quantum computing device versus which functions of this clause are performed by the processor, which renders the bounds of the claim unclear. Claims 17-20 inherit the same deficiency as claim 16 based on dependence.
Claim 18 recites “when computing the estimated phase difference associated with an eigenvector of the plurality of eigenvectors, the computing system further: computes a plurality of sample estimated phase differences via the iterative quantum phase estimation; and computes the estimated phase difference as a median of the plurality of sample estimated phase differences”. It is unclear which functions of this clause are performed by the quantum computing device versus which functions of this clause are performed by the processor, which renders the bounds of the claim unclear.
Claim 20 recites “wherein the estimated eigenvalues are output to a quantum phase estimation process”. It is unclear which functions of this clause are performed by the quantum computing device versus which functions of this clause are performed by the processor, which renders the bounds of the claim unclear.
Allowable Subject Matter
Claims 1-20 would be allowable if rewritten to overcome the rejections under 35 USC 112(b).
The following is a statement of reasons for the indication of allowable subject matter. Applicant claims apparatus, and methods for a computing system wherein the apparatus as in claim 1 comprises:
a quantum computing device; and
a classical computing device including a processor,
wherein the computing system:
computes an estimated unitary matrix over a plurality of iterations that each include:
at the processor:
computing a current-iteration exponent and a current- iteration error parameter for a current iteration of the plurality of iterations;
computing a conjugate transpose of a current-iteration estimate of the unitary matrix; and
transmitting the current-iteration exponent, the current-iteration error parameter, and the conjugate transpose of the current-iteration estimate to the quantum computing device;
at the quantum computing device:
computing a process tomography result based at least in part on the current-iteration exponent, the current-iteration error parameter, the conjugate transpose of the current-iteration estimate, and the unitary matrix; and
outputting the process tomography result to the classical computing device; and
at the processor:
computing a distance measure between the current- iteration estimate and the process tomography result; and
when the distance measure is below a predefined constant, updating the current-iteration estimate based at least in part on the process tomography result; and
outputs, as the estimated unitary matrix, the updated current-iteration estimate computed in a final iteration of the plurality of iterations.
The apparatus as in claim 16 comprises:
a quantum computing device; and a classical computing device including a processor,
wherein the computing system:
estimates a plurality of eigenvalues of the unitary matrix at least in part by, for each of a plurality of eigenvectors of the unitary matrix:
at the quantum computing device, in each of a plurality of iterations:
receiving an ancillary qubit state at a first register;
receiving a tensor product of the eigenvector of the unitary matrix and a uniformly random eigenvector of the unitary matrix at a second register and a third register of the quantum computing device;
performing a controlled-SWAP operation on the second register and the third register, wherein the controlled-SWAP operation is controlled on the first register;
applying the unitary matrix to the second register a number of times determined based at least in part on an iteration number of a current iteration;
repeating the controlled-SWAP operation; and
detaching the first register from the second register and the third register;
at the quantum computing device and the processor, computing a plurality of estimated phase differences between the eigenvectors and the uniformly random eigenvector at least in part by performing iterative quantum phase estimation on the first register; and
at the processor, computing a plurality of estimated eigenvalues based at least in part on the plurality of estimated phase differences; and
outputs the estimated eigenvalues.
M. Clouatre, et al., Model-Predictive Quantum Control via Hamiltonian Learning, Quantum Internet, IEEE Transactions on Quantum Engineering, 23 May 2022, (hereinafter “Clouatre”) discloses a framework for control of quantum systems using tomography, which involves optimization of a unitary group. Clouatre further discloses measuring a distance between two matrices and minimizing a squared distance to produce an estimate of unitary dynamics (section B). Clouatre does not, however, the specific tomographic steps as in claim 1, or the steps of estimating a plurality of eigenvalues of the unitary matrix as in claim 16.
Wang, et al., Efficient identification of unitary quantum processes, Australian and New Zealand Control Conference (ANZCC), IEEE, p. 196-201, 2017 (hereinafter “Wang”) discloses an iterative algorithm for identifying and estimating unitary processes (abstract, fig 1). Wang does not, however, the specific tomographic steps as in claim 1, or the steps of estimating a plurality of eigenvalues of the unitary matrix as in claim 16.
K. Zhang et al., An online optimization algorithm for real-time quantum state tomography, Quantum Information Processing, 19:361, 2020 (hereinafter “Zhang”) discloses an online iterative algorithm for quantum tomography (abstract, introduction, fig 1). Zhang further discloses solving a singular value decomposition using the unitary similarity matrix ( p. 360 bottom – 361 top, algorithm 1). Zhang does not, however disclose the specific tomographic steps as in claim 1, or the steps of estimating a plurality of eigenvalues of the unitary matrix as in claim 16.
US 20200279185 A1 Weibe et al., (hereinafter “Wiebe”) discloses methods to train a quantum Boltzmann machine, and tomography (abstract, fig 1). Wiebe further discloses initializing registers, applying a unitary transformation, and a fourier transform ([0187-0193). Weibe does not, however disclose the specific tomographic steps as in claim 1, or the steps of estimating a plurality of eigenvalues of the unitary matrix as in claim 16.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to EMILY E LAROCQUE whose telephone number is (469)295-9289. The examiner can normally be reached on 10:00am - 1200pm, 2:00pm - 8pm ET M-F.
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/EMILY E LAROCQUE/Primary Examiner, Art Unit 2182