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 gate-based quantum computers [1]. 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^exact 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.
[1] P. Czarnik, A. Arrasmith, P. J. Coles, L. Cincio, arXiv:2005.10189.