Error mitigation with Clifford quantum-circuit data

Quantum 5, 592 (2021).

https://doi.org/10.22331/q-2021-11-26-592

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. The method generates training data ${X_i^{text{noisy}},X_i^{text{exact}}}$ via quantum circuits composed largely of Clifford gates, which can be efficiently simulated classically, where $X_i^{text{noisy}}$ and $X_i^{text{exact}}$ 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.