Computing platform with heterogenous quantum processors
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.
1. A hybrid quantum-classical computing platform comprising:
a first quantum processor unit (QPU) comprising a first classical local memory;
a second QPU comprising a second classical local memory; 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,
wherein 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.
2. The hybrid quantum-classical computing platform of claim 1 , wherein the first QPU has higher fidelity than the second QPU.
3. The hybrid quantum-classical computing platform of claim 2 , wherein 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.
4. The hybrid quantum-classical computing platform of claim 3 , wherein the fault tolerant threshold for quantum error correction is one percent gate infidelity for the surface code.
5. The hybrid quantum-classical computing platform of claim 3 , wherein the fault tolerant threshold for quantum error correction is one percent error for the surface code.
6. The hybrid quantum-classical computing platform of claim 1 , wherein the first QPU operates at a higher clock rate than the second QPU.
7. The hybrid quantum-classical computing platform of claim 1 , wherein the first QPU operates at a higher logical clock rate than the second QPU.
8. The hybrid quantum-classical computing platform of claim 1 , wherein the first QPU operates at a higher repetition rate than the second QPU.
9. The hybrid quantum-classical computing platform of claim 1 , wherein the shared classical memory is configured to refresh with data from the first classical local memory and the second classical local memory.
10. The hybrid quantum-classical computing platform of claim 1 , wherein the shared classical memory comprises a first shared classical local memory and a second shared classical local memory, the first shared classical local memory being connected to the second shared classical local memory for sharing data between the first QPU and the second QPU.
11. The hybrid quantum-classical computing platform of claim 10 , wherein the first shared classical local memory and the second shared classical local memory are configured to refresh with data from the first classical local memory and the second classical local memory, respectively.
12. The hybrid quantum-classical computing platform of claim 1 , wherein the shared classical memory is partitioned into a first portion, a second portion and a shared portion, wherein the first portion is dedicated to the first QPU, the second portion is dedicated to the second QPU, and the shared portion coordinates the flow of data through the shared classical memory.
13. The hybrid quantum-classical computing platform of claim 1 , further comprising a quantum communication link between the first QPU and the second QPU for teleportation of quantum states.
14. A method of operating the hybrid quantum-classical computing platform of claim 1 , the method comprising:
collecting a first set of data from the first QPU in the first classical local memory;
transferring the first set of data from the first classical local memory to the shared classical memory; and
transferring the first set of data from the shared classical memory to the second classical local memory.
15. The method of claim 14 , wherein the first set of data is calibration data for the second QPU.
16. The method of claim 14 , wherein the first set of data is error syndrome data for the second QPU.
17. The method of claim 14 , wherein the first set of data is optimization data for a quantum approximate optimization algorithm (QAOA) iteration being run on the second QPU.
18. The method of claim 14 , wherein the first set of data is optimization data for a variational-quantum-eigensolver (VQE) algorithm iteration being run on the second QPU.
19. The method of claim 14 , wherein the first set of data is optimization data for a machine learning (ML) classification algorithm iteration being run on the second QPU.
20. The method of claim 14 , wherein the first set of data is a reduced data set derived from a classical data set autoencoded on the first QPU.
21. The method of claim 14 , wherein the first QPU operates at a higher clock rate than the second QPU.
22. The method of claim 14 , wherein the first QPU operates at a higher logical clock rate than the second QPU.
23. The method of claim 14 , wherein the first QPU operates at a higher repetition rate than the second QPU.
24. The method of claim 14 , wherein the second QPU has higher fidelity than the first QPU.