Quantum error mitigation with diffusion models

A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising:

• performing, on a quantum circuit, a circuit cutting process to generate a set of subcircuits of the quantum circuit;
• for each of the subcircuits:
• running the subcircuit on a target backend quantum hardware configuration, and measuring a noisy output resulting from the running of the subcircuit;
• using an ML (machine learning) model to obtain an estimate of a noise-free output of the subcircuit;
• and
• replacing noisy output of the subcircuits with the estimated noise-free output generated by the ML model;
• running a circuit knitting process, using the estimates, to knit the subcircuits together to form the quantum circuit;
• and
• executing, on the backend quantum hardware configuration, the quantum circuit that resulted from the circuit knitting process.

A system, comprising:

• one or more hardware processors;
• and
• a non-transitory storage medium having stored therein instructions that are executable by the one or more hardware processors to perform operations comprising:
• performing, on a quantum circuit, a circuit cutting process to generate a set of subcircuits of the quantum circuit;
• for each of the subcircuits:
• running the subcircuit on a target backend quantum hardware configuration, and measuring a noisy output resulting from the running of the subcircuit;
• using an ML (machine learning) model to obtain an estimate of a noise-free output of the subcircuit;
• and
• replacing noisy output of the subcircuits with the estimated noise-free output generated by the ML model;
• running a circuit knitting process, using the estimates, to knit the subcircuits together to form the quantum circuit;
• and
• executing, on the backend quantum hardware configuration, the quantum circuit that resulted from the circuit knitting process.