IP Library › Granted Patent US 12,033,031
Granted Patent B2
US 12,033,031 · App. 18/458,783 · Granted Jul 9, 2024

Quantum circuit optimization

Inventors: Vandiver Chaplin (Washington, DC); Yunseong Nam (North Bethesda, MD)
Assignee: IonQ, Inc.
G06N10/00G06F8/443G06F9/3885
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Quick Facts
Patent No.
US 12,033,031
App. No.
18/458,783
Granted
Jul 9, 2024
Kind
B2
Abstract

A system and method is provided for optimizing an input quantum circuit. An exemplary method includes searching a library of templates to find, by compiling abstract gate operations into a set of hardware-specific operations that manipulate qubit states, a template of quantum circuit gates that performs a predetermined function and that matches a set of quantum circuit gates in the input quantum circuit that performs the predetermined function; and replacing the set of quantum circuit gates in the input quantum circuit with the template of quantum circuit gates when the template of quantum circuit gates has a lower quantum cost than the set of quantum circuit gates based on estimated execution times. Moreover, the method is executed in a pipeline in combination with at least quantum circuit compilation.

Claims (30)

1. A computer-implemented method of optimizing an input quantum circuit, the method comprising:

searching a library of templates to find, by compiling abstract gate operations into a set of hardware-specific operations that manipulate qubit states, a template of quantum circuit gates that performs a predetermined function and that matches a set of quantum circuit gates in the input quantum circuit that performs the predetermined function; and

replacing the set of quantum circuit gates in the input quantum circuit with the template of quantum circuit gates when the template of quantum circuit gates has a lower quantum cost than the set of quantum circuit gates based on estimated execution times,

wherein the method is executed in a pipeline in combination with at least quantum circuit compilation.

2. The computer-implemented method in accordance with claim 1 , further comprising selecting the template of quantum circuit gates to reduce an accrual of errors, including a likelihood complete state decoherence, that increase over time.

3. The computer-implemented method in accordance with claim 1 , further comprising determining the estimated execution times based on quantum circuit depth.

4. The computer-implemented method in accordance with claim 3 , further comprising computing the quantum circuit depth as a longest connected path from a beginning to an end of the input quantum circuit.

5. The computer-implemented method in accordance with claim 3 , further comprising computing the quantum circuit depth by determining a longest connected path in a Directed Acyclic Graph (DAG) representation of the input quantum circuit.

6. The computer-implemented method in accordance with claim 5 , further comprising computing a length of a path in the DAG to be equal to a number of gates encountered on the path.

7. The computer-implemented method in accordance with claim 5 , further comprising computing a length of a path in the DAG to be equal to a sum of quantum cost along the path.

8. The computer-implemented method in accordance with claim 1 , further comprising obtaining a quantum cost reduction by parallelizing two or more serial gates.

9. The computer-implemented method in accordance with claim 1 , wherein the method is a template-based quantum circuit optimization configured to reduce the estimated execution times and is used in tandem with quantum circuit optimizers that preserve a connectivity topology of the input quantum circuit to facilitate further circuit optimization in the lower quantum cost, including a number of gates, responsive to underlying target quantum hardware being configured to natively implement a newly-introduced connectivity.

10. The computer-implemented method in accordance with claim 1 , wherein the method is a template-based quantum circuit optimization configured to reduce the estimated execution times and is used in tandem with quantum circuit optimizers that preserve a connectivity topology of the input quantum circuit to facilitate further circuit optimization in the lower quantum cost, including a quantum circuit depth, responsive to underlying target quantum hardware being configured to natively implement a newly-introduced connectivity.

11. A quantum circuit optimizer for optimizing an input quantum circuit, comprising:

a quantum circuit analyzer configured to:

execute an algorithm to search a library of templates to find, by compiling abstract gate operations into a set of hardware-specific operations that manipulate qubit states, a template of quantum circuit gates that performs a predetermined function and that matches a set of quantum circuit gates in the input quantum circuit that performs the predetermined function; and

replace the set of quantum circuit gates in the input quantum circuit with the template of quantum circuit gates when the template of quantum circuit gates has a lower quantum cost than the set of quantum circuit gates based on estimated execution times,

wherein the algorithm is executed in a pipeline in combination with at least quantum circuit compilation.

12. The quantum circuit optimizer in accordance with claim 11 , wherein the quantum circuit analyzer is configured to select the template of quantum circuit gates to reduce an accrual of errors, including a likelihood complete state decoherence, that increase over time.

13. The quantum circuit optimizer in accordance with claim 11 , wherein the estimated execution times are based on quantum circuit depth.

14. The quantum circuit optimizer in accordance with claim 13 , wherein the quantum circuit analyzer is configured to compute the quantum circuit depth as a longest connected path from a beginning to an end of the input quantum circuit.

15. The quantum circuit optimizer in accordance with claim 13 , wherein the quantum circuit analyzer is configured to compute the quantum circuit depth by determining a longest connected path in a Directed Acyclic Graph (DAG) representation of the input quantum circuit.

16. The quantum circuit optimizer in accordance with claim 15 , wherein a length of a path in the DAG is equal to a number of gates encountered on the path.

17. The quantum circuit optimizer in accordance with claim 15 , wherein a length of a path in the DAG is equal to a sum of quantum cost along the path.

18. The quantum circuit optimizer in accordance with claim 11 , wherein a quantum cost reduction is obtained by parallelizing two or more serial gates.

19. The quantum circuit optimizer in accordance with claim 11 , wherein the optimizer is a template-based quantum circuit optimizer configured to reduce the estimated execution times and is used in tandem with quantum circuit optimizers that preserve a connectivity topology of the input quantum circuit to facilitate further circuit optimization in the lower quantum cost, including a number of gates, responsive to underlying target quantum hardware being configured to natively implement a newly-introduced connectivity.

20. A quantum computing system, comprising:

means for searching a library of templates to find, by compiling abstract gate operations into a set of hardware-specific operations that manipulate qubit states, a template of quantum circuit gates which performs a predetermined function and that matches a set of quantum circuit gates in an input quantum circuit that performs the predetermined function; and

means for replacing the set of quantum circuit gates in the input quantum circuit with the template of quantum circuit gates when the template of quantum circuit gates has a lower quantum cost than the set of quantum circuit gates based on estimated execution times,

wherein the means for searching and the means for replacing are implemented using a pipeline in combination with at least quantum circuit compilation.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 15, 2023
From: CHAPLIN, VANDIVER; NAM, YUNSEONG
To: IONQ, INC.
Reel/Frame 064916/0976 →
Continuity (2)
Continuation 16281241 · Feb 21, 2019
Related Publication 20240062087A1 · Feb 22, 2024
Cited By (1)
US 12,695,000