IP Library › Granted Patent US 12,299,593
Granted Patent B2
US 12,299,593 · App. 17/154,210 · Granted May 13, 2025

Systems and methods for optimizing annealing parameters

Inventor: Mohammad H. Amin (Coquitlam, CA)
Assignee: D-WAVE SYSTEMS INC.
G06N5/04G06N10/60G06N20/00
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Quick Facts
Patent No.
US 12,299,593
App. No.
17/154,210
Granted
May 13, 2025
Kind
B2
Abstract

A quantum annealing schedule for a computational problem can be adjusted by methods and systems involving one or more processors. The one or more processors proceed by receiving a representation of the computation problem, the representation including a plurality of problem values. These problem values are transformed based on a plurality of trained parameters of a machine learning model to generate at least a portion of an annealing schedule including at least one annealing parameter. Instructions are transmitted to the quantum processor to cause the quantum processor to evolve from an initial state to a final state based on the computational problem and the at least a portion of an annealing schedule, the final state producing a result for the computational problem.

Claims (26)

1. A method of adjusting a quantum annealing schedule for a computational problem, the method being performed by one or more processors, the method comprising:

receiving a representation of the computational problem, the representation comprising a plurality of problem values;

transforming the plurality of problem values based on a plurality of trained parameters of a machine learning model to generate at least a portion of an annealing schedule comprising at least one annealing offset parameter and at least one annealing offset compensation coefficient; and

transmitting instructions comprising the at least one annealing offset compensation coefficient to a quantum processor to cause the quantum processor to update the computational problem with the at least one annealing offset compensation coefficient to generate an updated computational problem and evolve from an initial state to a final state based on the updated computational problem and the at least a portion of an annealing schedule, wherein the final state provides a result for the computational problem.

2. The method of claim 1 , wherein transforming the plurality of problem values based on a plurality of trained parameters of a machine learning model to generate at least a portion of an annealing schedule comprises generating one or more of: annealing speed, and timing and duration of annealing pauses.

3. The method of claim 1 , wherein transforming the plurality of problem values based on a plurality of trained parameters of a machine learning model to generate at least a portion of an annealing schedule comprising at least one annealing offset parameter comprises generating multiple annealing parameters to form an annealing schedule.

4. The method of claim 1 , wherein receiving a representation of the computational problem, the representation comprising a plurality of problem values, comprises receiving problem values comprising qubit and coupler parameters corresponding to the computational problem.

5. The method of claim 4 , wherein transforming the plurality of problem values based on a plurality of trained parameters of a machine learning model to generate at least a portion of an annealing schedule comprising at least one annealing offset parameter comprises generating an annealing parameter that determines the annealing schedule for at least one qubit.

6. The method of claim 1 , wherein receiving a representation of the computational problem comprises receiving a representation of a computational problem comprising a problem Hamiltonian and wherein transmitting instructions to the quantum processor to cause the quantum processor to evolve from an initial state to a final state based on the computational problem and the at least a portion of an annealing schedule further comprises transmitting instructions to the quantum processor to cause the quantum processor to evolve from a problem Hamiltonian state to a result Hamiltonian state.

7. The method of claim 1 , further comprising receiving a default annealing schedule, and wherein transforming the plurality of problem values based on a plurality of trained parameters of a machine learning model to generate at least a portion of an annealing schedule comprising at least one annealing offset parameter comprises modifying the default annealing schedule based on the at least one annealing offset parameter to generate the at least a portion of an annealing schedule.

8. The method of claim 7 , wherein receiving a default annealing schedule comprises receiving one or more default annealing parameters comprising one or more of: default annealing offsets, default annealing speed, and default timing and duration of annealing pauses.

9. The method of claim 7 , further comprising receiving result data generated by causing the quantum processor to evolve from an initial state to a final state based on the computational problem and the default annealing schedule, and wherein transforming the plurality of problem values based on a plurality of trained parameters of a machine learning model to generate at least a portion of an annealing schedule comprising at least one annealing offset parameter comprises transforming the plurality of problem values and the result data to generate at least a portion of an annealing schedule comprising at least one annealing parameter.

10. A system for use in quantum processing, comprising:

at least one non-transitory processor-readable medium that stores at least one of processor executable instructions and data; and

at least one processor communicatively coupled to the at least one non-transitory processor-readable medium and a quantum processor, and which, in response to execution of the at least one of processor executable instructions and data:

receives a representation of a computational problem, the representation comprising a plurality of problem values;

transforms the plurality of problem values based on a plurality of trained parameters of a machine learning model to generate at least a portion of an annealing schedule comprising at least one annealing offset parameter and at least one annealing offset compensation coefficient; and

transmits instructions to the quantum processor that cause the quantum processor to update the computational problem with the annealing offset compensation coefficients to generate an updated computational problem and evolve from an initial state to a final state based on the updated computational problem and the at least a portion of an annealing schedule, wherein the final state provides a result for the computational problem.

11. The system of claim 10 , wherein the at least a portion of an annealing schedule comprises one or more of: annealing speed, and timing and duration of annealing pauses.

12. The system of claim 10 , wherein the machine learning model is trained to generate multiple annealing parameters to form an annealing schedule.

13. The system of claim 10 , wherein the plurality of problem values comprises qubit and coupler parameters corresponding to the computational problem.

14. The system of claim 13 , wherein the at least one annealing offset parameter comprises an annealing parameter that determines the annealing schedule for at least one qubit.

15. The system of claim 10 , wherein the representation of a computational problem comprises a representation of a problem Hamiltonian and the result for the computational problem comprises a result Hamiltonian.

16. The system of claim 10 , wherein, in response to execution of the at least one of processor executable instructions and data, the at least one processor receives a default annealing schedule, and transmits instructions to the quantum processor to modify the default annealing schedule based on the at least one annealing offset parameter to generate the at least a portion of an annealing schedule.

17. The system of claim 16 , wherein the default annealing schedule comprises one or more default annealing parameters comprising one or more of: default annealing speed, and default timing and duration of annealing pauses.

18. The system of claim 16 , wherein, in response to execution of the at least one of processor executable instructions and data, the at least one processor receives result data generated by the quantum processor evolving from an initial state to a final state based on the computational problem and the default annealing schedule, and wherein the processor transforms the plurality of problem values and the result data to generate the at least a portion of an annealing schedule comprising the at least one annealing offset parameter.

Assignments (5)
RELEASE OF SECURITY INTEREST Recorded Mar 11, 2025
From: PSPIB UNITAS INVESTMENTS II INC.
To: D-WAVE SYSTEMS INC.; 1372934 B.C. LTD.
Reel/Frame 070470/0098 →
INTELLECTUAL PROPERTY SECURITY AGREEMENT Recorded Apr 14, 2023
From: D-WAVE SYSTEMS INC.; 1372934 B.C. LTD.
To: PSPIB UNITAS INVESTMENTS II INC., AS COLLATERAL AGENT
Reel/Frame 063340/0888 →
RELEASE OF SECURITY INTEREST Recorded Sep 20, 2022
From: PSPIB UNITAS INVESTMENTS II INC., IN ITS CAPACITY AS COLLATERAL AGENT
To: D-WAVE SYSTEMS INC.
Reel/Frame 061493/0694 →
SECURITY INTEREST Recorded Mar 3, 2022
From: D-WAVE SYSTEMS INC.
To: PSPIB UNITAS INVESTMENTS II INC.
Reel/Frame 059317/0871 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 28, 2021
From: AMIN, MOHAMMAD H.
To: D-WAVE SYSTEMS INC.
Reel/Frame 055060/0098 →
Continuity (2)
Provisional Application 62970217 · Feb 5, 2020
Related Publication 20210241143A1 · Aug 5, 2021
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