Error mitigation with Clifford quantum-circuit data

Achieving near-term quantum advantage will require accurate estimation of quantum observables despite significant hardware noise. For this purpose, we propose a novel, scalable error-mitigation method that applies to gatebased quantum computers. The method generates training data {X-i(noisy), X-i(exact)} via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where X-i(noisy) and X-i(noisy) are noisy and noiseless observables respectively. Fitting a linear ansatz to this data then allows for the prediction of noise-free observables for arbitrary circuits. We analyze the performance of our method versus the number of qubits, circuit depth, and number of non-Clifford gates. We obtain an order-of-magnitude error reduction for a ground-state energy problem on 16 qubits in an IBMQ quantum computer and on a 64-qubit noisy simulator.

Automated summary

A novel error-mitigation method for gate-based quantum computers is proposed in this study, which generates training data in quantum circuits and fits a linear ansatz to predict noise-free observables for arbitrary circuits. The method achieves an order-of-magnitude error reduction under various conditions.