Error mitigation with Clifford quantum-circuit data

Abstract

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.

16 pages, 14 figures. New numerical results added. A version accepted by Quantum

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Cited by in corpus (12)

• The Variational Quantum Eigensolver: a review of methods and best practices
• Mitiq: A software package for error mitigation on noisy quantum computers
• Towards understanding the power of quantum kernels in the NISQ era
• Extending quantum probabilistic error cancellation by noise scaling
• Quantum Error Mitigation Relying on Permutation Filtering
• Error mitigation by training with fermionic linear optics
• Convergence of reconstructed density matrix to a pure state using maximal entropy approach
• Quantum error reduction with deep neural network applied at the post-processing stage
• Engineering analog quantum chemistry Hamiltonians using cold atoms in optical lattices
• Scalable evaluation of quantum-circuit error loss using Clifford sampling
• A Practical Framework for Quantum Error Mitigation
• Wildcard error: Quantifying unmodeled errors in quantum processors