IP Library Granted Patent US 11,049,035
Granted Patent B2
US 11,049,035 · App. 15/983,916 · Granted Jun 29, 2021

Meta-level short-depth quantum computation of k-eigenpairs

Inventors: Lior Horesh (North Salem, NY); Giacomo Nannicini (New York, NY)
Assignee: INTERNATIONAL BUSINESS MACHINES CORPORATION
G06N10/00G06F17/16G06N7/00
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Quick Facts
Patent No.
US 11,049,035
App. No.
15/983,916
Granted
Jun 29, 2021
Kind
B2
Abstract

Techniques and a system to facilitate meta-level quantum computation are provided. In one example, a system includes a quantum processor and a classical processor. The quantum processor can perform an expectation computation process to compute an expected value of a deflated operator and a quantum state associated with a quantum circuit description. The classical processor can execute computer executable components stored in a memory, where the computer executable components comprise a meta-level variational optimization component. The meta-level variational optimization component can perform a meta-level optimization process associated with a k-eigenvalue decomposition process to iteratively determine an inflation parameter and a variational parameterization for an eigenpair based on samples of the expectation computation process.

Claims (35)

1. A system, comprising:

a quantum processor that performs an expectation computation process to compute an expected value of a deflated operator and a quantum state associated with a quantum circuit description; and

a classical processor that executes computer executable components stored in a memory, wherein the computer executable components comprise:

a meta-level variational optimization component that iteratively performs a meta-level optimization process associated with a k-eigenvalue decomposition process to iteratively determine an inflation parameter and a variational parameterization for an eigenpair based on samples of the expectation computation process, wherein respective iterations of the meta-level optimization process enforce a constraint that an eigenvalue of the eigenpair is greater than previous eigenvalues of previous eigenpairs of previous iterations of the meta-level optimization process.

2. The system of claim 1 , wherein the meta-level variational optimization component transmits the quantum circuit description to the quantum processor, and wherein the quantum circuit description includes a trial deflated operator and a trial state variational parameterization.

3. The system of claim 2 , wherein the quantum processor performs the expectation computation process to generate quantum measurement data indicative of a set of quantum measurements.

4. The system of claim 3 , wherein the computer executable components further comprise:

an expectation computation component that computes an expectation of the quantum measurement data.

5. The system of claim 4 , wherein the meta-level variational optimization component utilizes output from the expectation of the quantum measurement data to iteratively determine the eigenvalue and an eigenstate.

6. The system of claim 1 , wherein the respective iterations of the meta-level optimization process employ a penalty that provides a preference to a smallest discoverable eigenvalue.

7. The system of claim 4 , wherein the expectation computation component formats the quantum measurement data based on a modulo summation process.

8. The system of claim 1 , wherein the meta-level variational optimization component transmits the quantum circuit description to the quantum processor, and wherein the quantum circuit description includes an operator represented as a weighted sum of tensor products of Pauli and identity operators.

9. The system of claim 1 , wherein the meta-level variational optimization component performs the meta-level optimization process to facilitate reduced error associated with the quantum processor.

10. A computer-implemented method, comprising:

receiving, by a system operatively coupled to a processor, from a quantum processor, samples of an expectation computation process performed by the quantum processor to compute an expected value of a deflated operator and a quantum state associated with a quantum circuit description; and

iteratively performing, by the system, a meta-level optimization process associated with a k-eigenvalue decomposition process to iteratively determine an inflation parameter and a variational parameterization for an eigenpair based on samples of the expectation computation process, wherein respective iterations of the meta-level optimization process enforces a constraint that an eigenvalue of the eigenpair is greater than previous eigenvalues of previous eigenpairs of previous iterations of the meta-level optimization process.

11. The computer-implemented method of claim 10 , further comprising:

transmitting, by the system, the quantum circuit description to the quantum processor, wherein the quantum circuit description includes a trial deflated operator and a trial state variational parameterization.

12. The computer-implemented method of claim 11 , wherein the performing the meta-level optimization process comprises performing the expectation computation process to generate quantum measurement data indicative of a set of quantum measurement.

13. The computer-implemented method of claim 12 , further comprising:

computing, by the system, an expectation of the quantum measurement data.

14. The computer-implemented method of claim 13 , further comprising:

utilizing, by the system, output from the expectation of the quantum measurement data to iteratively determine the eigenvalue and an eigenstate.

15. The computer-implemented method of claim 10 , wherein the performing the meta-level optimization process comprises reducing error associated with the quantum processor.

16. A computer program product for meta-level quantum computation, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to:

receive, by the processor, from a quantum processor, samples of an expectation computation process performed by the quantum processor to compute an expected value of a deflated operator and a quantum state associated with a quantum circuit description; and

iteratively perform, by the processor, a meta-level optimization process associated with a k-eigenvalue decomposition process to determine an inflation parameter and a variational parameterization for an eigenpair based on samples of the expectation computation process, wherein respective iterations of the meta-level optimization process enforces a constraint that an eigenvalue of the eigenpair is greater than previous eigenvalues of previous eigenpairs of previous iterations of the meta-level optimization process.

17. The computer program product of claim 16 , wherein the program instructions are further executable by the processor to cause the processor to:

transmit, by the processor, the quantum circuit description to the quantum processor, wherein the quantum circuit description includes a trial deflated operator and a trial state variational parameterization.

18. The computer program product of claim 17 , wherein the program instructions are further executable by the processor to cause the processor to:

perform, by the processor, the expectation computation process to generate quantum measurement data indicative of a set of quantum measurement.

19. The computer program product of claim 18 , wherein the program instructions are further executable by the processor to cause the processor to:

compute, by the system, an expectation of the quantum measurement data.

20. The computer program product of claim 18 , wherein the program instructions are further executable by the processor to cause the processor to:

format, by the processor, the quantum measurement data based on a modulo summation process.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded May 18, 2018
From: HORESH, LIOR; NANNICINI, GIACOMO
To: INTERNATIONAL BUSINESS MACHINES CORPORATION
Reel/Frame 045848/0160 →
Continuity (1)
Related Publication 20190354897A1 · Nov 21, 2019