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US-20260127473-A1 - Computing Platform with Heterogenous Quantum Processors

US20260127473A1US 20260127473 A1US20260127473 A1US 20260127473A1US-20260127473-A1

Abstract

In some aspects, a hybrid quantum-classical computing platform may comprise: a first quantum processor unit (QPU); a second QPU; and a shared classical memory, the shared classical memory being connected to both the first QPU and the second QPU, wherein the shared classical memory is configured to share data between the first QPU and the second QPU. In some embodiments, the first QPU operates at a higher repetition rate and/or clock rate than the second QPU and the second QPU operates with a higher fidelity than the first QPU.

Inventors

  • Chad Tyler Rigetti
  • William J. Zeng
  • Blake Robert Johnson
  • Nikolas Anton Tezak

Assignees

  • RIGETTI & CO, LLC

Dates

Publication Date
20260507
Application Date
20241120

Claims (20)

  1. 1 - 53 . (canceled)
  2. 54 . A method of using a first quantum processor unit (QPU) to calibrate a second QPU in a hybrid quantum-classical computing platform, the method comprising: obtaining control parameters for the second QPU as an initial calibration; generating sampled data based on operating the second QPU with the control parameters; storing the sampled data in shared classical memory; using the sampled data in a quantum Hamiltonian learning process, wherein the quantum Hamiltonian learning process produces a learned Hamiltonian of the second QPU based on operating the first QPU as a trusted simulator; and updating the control parameters for the second QPU based on evaluating the learned Hamiltonian against a calibration objective function.
  3. 55 . The method of claim 54 , wherein the quantum Hamiltonian learning process uses the first QPU to simulate subsystems of qubits of the second QPU.
  4. 56 . The method of claim 54 , further comprising performing an iterative process until a termination criterion is reached, each iteration of the iterative process comprising: generating sampled data for the iteration based on operating the second QPU with the updated control parameters; storing the sampled data for the iteration in the shared classical memory; using the sampled data for the iteration in the quantum Hamiltonian learning process, wherein the quantum Hamiltonian learning process produces a learned Hamiltonian for the iteration; and updating the control parameters based on evaluating the learned Hamiltonian for the iteration against the calibration objective function.
  5. 57 . The method of claim 56 , wherein the termination criterion is a target calibration accuracy.
  6. 58 . The method of claim 54 , wherein the calibration objective function is the average distance from a set of random sequences to the identity.
  7. 59 . The method of claim 54 , wherein the first QPU operates at a higher clock rate than the second QPU.
  8. 60 . The method of claim 54 , wherein the first QPU operates at a higher logical clock rate than the second QPU.
  9. 61 . The method of claim 54 , wherein the first QPU operates at a higher repetition rate than the second QPU.
  10. 62 . The method of claim 54 , wherein the second QPU has higher fidelity than the first QPU.
  11. 63 . The method of claim 54 , wherein the shared classical memory is connected to both the first QPU and the second QPU, and the shared classical memory is configured to share data between the first QPU and the second QPU.
  12. 64 . The method of claim 54 , wherein the first QPU comprises a first classical local memory, the second QPU comprises a second classical local memory, and the first classical local memory and the second classical local memory are connected to the shared classical memory for transferring data between the first QPU and the second QPU.
  13. 65 . The method of claim 54 , wherein the hybrid quantum-classical computing platform comprises a quantum communication link between the first QPU and the second QPU for teleportation of quantum states.
  14. 66 . The method of claim 54 , wherein generating the sampled data comprises sampling Pr(DIH), wherein a likelihood of the data D being from a given Hamiltonian H is given by Pr ⁡ ( H | D ) = Pr ⁡ ( D | H ) ⁢ Pr ⁡ ( H ) Pr ⁡ ( D ) .
  15. 67 . A hybrid quantum-classical computing platform comprising: a first quantum processor unit (QPU); a second QPU; shared classical memory; and a classical computing resource configured to: obtain control parameters for the second QPU as an initial calibration; generate sampled data based on operating the second QPU with the control parameters; store the sampled data in the shared classical memory; use the sampled data in a quantum Hamiltonian learning process, wherein the quantum Hamiltonian learning process produces a learned Hamiltonian of the second QPU based on operating the first QPU as a trusted simulator; and update the control parameters for the second QPU based on evaluating the learned Hamiltonian against a calibration objective function.
  16. 68 . The platform of claim 67 , wherein the quantum Hamiltonian learning process uses the first QPU to simulate subsystems of qubits of the second QPU.
  17. 69 . The platform of claim 67 , wherein the classical computing resource is configured to perform an iterative process until a termination criterion is reached, each iteration of the iterative process comprising: generating sampled data for the iteration based on operating the second QPU with the updated control parameters; storing the sampled data for the iteration in the shared classical memory; using the sampled data for the iteration in the quantum Hamiltonian learning process, wherein the quantum Hamiltonian learning process produces a learned Hamiltonian for the iteration; and updating the control parameters based on evaluating the learned Hamiltonian for the iteration against the calibration objective function.
  18. 70 . The platform of claim 67 , wherein the termination criterion is a target calibration accuracy.
  19. 71 . The platform of claim 67 , wherein the calibration objective function is the average distance from a set of random sequences to the identity.
  20. 72 . The platform of claim 67 , wherein the shared classical memory is connected to both the first QPU and the second QPU, and the shared classical memory is configured to share data between the first QPU and the second QPU.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 62/673,658 filed May 18, 2018 and entitled “Computing Platform with Heterogenous Quantum Processors.” The entire contents of the above-referenced priority application, including all text and drawings, are hereby incorporated by reference. BACKGROUND The following description relates to computing platforms with heterogenous quantum processors. Quantum computing is an emerging and fast-growing field that aims to harness quantum effects to perform information processing. There is a need for quantum computing systems with improved performance. BRIEF DESCRIPTION OF THE DRAWINGS FIG. 1 shows an example quantum computer system, according to some embodiments. FIG. 2 shows a schematic representation of a computing platform with heterogenous quantum processors and a shared classical memory, according to a first embodiment. FIG. 3 shows a schematic representation of a computing platform with heterogenous quantum processors and a shared classical memory, according to a second embodiment. FIG. 4 shows a schematic representation of a computing platform with heterogenous quantum processors and a shared classical memory, according to a third embodiment. FIG. 5 shows a schematic representation of a computing platform with heterogenous quantum processors and a shared classical memory, according to a fourth embodiment. FIG. 6 shows a flow diagram for running an optimized QAOA on a computing platform with heterogenous quantum processors, according to some embodiments. FIG. 7 shows a flow diagram for running error correction on a computing platform with heterogenous quantum processors, according to some embodiments. FIGS. 8A & 8B show a flow diagram for increasing the effective clock rate of an error-corrected quantum processor on a computing platform with heterogenous quantum processors, according to some embodiments. FIG. 9 shows a flow diagram for running model reduction on a computing platform with heterogenous quantum processors, according to some embodiments. FIG. 10 shows a flow diagram for running calibration on a computing platform with heterogenous quantum processors, according to some embodiments. DETAILED DESCRIPTION In some aspects of what is described here, a hybrid quantum-classical computing platform may comprise: a multiplicity of QPUs, a shared classical memory, wherein the shared classical memory is configured to share data between each of the multiplicity of QPUs. In some embodiments, one or more of the multiplicity of QPUs operates at a higher repetition rate and/or clock rate than other ones of the multiplicity of QPUs; in some embodiments, one or more of the multiplicity of QPUs operates with a higher fidelity than other ones of the multiplicity of QPUs. In some embodiments, the first QPU is above the fault tolerant threshold for quantum error correction and the second QPU is below the fault tolerant threshold for quantum error correction; in some embodiments the fault tolerant threshold for quantum error correction is one percent gate infidelity for the surface code. These hybrid quantum-classical computing platforms may achieve higher performance than any one single component quantum processor—examples of applications are provided herein. The multiplicity of QPUs comprise one or more QPUs chosen from a nuclear spin QPU, an electron spin QPU, an ion trap QPU, a photonic QPU, a topological QPU, a superconducting circuit QPU, etc. Herein the following terms are used and have the following meanings. Clock rate is the rate at which logic gates (i.e. instructions) are executed; note that this is a physical clock rate, for gates acting directly on physical qubits. Another term is logical clock rate which is understood herein to encompass several rounds of an error syndrome and correction cycle, for example. Repetition rate is the rate at which entire quantum circuits (programs) are executed. Repetition rates are relevant to certain quantum algorithms containing circuits whose output is non-deterministic, and thus several executions of the circuit must be completed to collect statistics on the distribution of outputs. For instance, an algorithm may need to sample several times from the output distribution. Gate speed is a synonym for the (physical) clock rate. According to some embodiments, a method of operating a hybrid quantum-classical computing platform, may comprise: collecting a first set of data from a first QPU in a first classical local memory; transferring the first set of data from the first classical local memory to a shared classical memory; transferring the first set of data from the shared classical memory to a second classical local memory; and transferring the first set of data from the second classical local memory to a second QPU. In some embodiments, the first set of data is calibration data for the second QPU. In some embodiments, the first set of data is error syndrome data for the