Mitigating crosstalk errors by randomized compiling: Simulation of the BCS model on a superconducting quantum computer

Abstract

We develop and apply an extension of the randomized compiling (RC) protocol that includes a special treatment of neighboring qubits and dramatically reduces crosstalk effects caused by the application of faulty gates on superconducting qubits in IBMQ quantum computers (\texttt{ibm\_lagos} and \texttt{ibmq\_ehningen}). Crosstalk errors, stemming from CNOT two-qubit gates, are a crucial source of errors on numerous quantum computing platforms. For the IBMQ machines, their magnitude is often overlooked-9. Our RC protocol turns coherent noise due to crosstalk into a depolarising noise channel that can then be treated using established error mitigation schemes, such as noise estimation circuits. We apply our approach to the quantum simulation of the non-equilibrium dynamics of the Bardeen-Cooper-Schrieffer (BCS) Hamiltonian for superconductivity, a particularly challenging model to simulate on quantum hardware because of the long-range interaction of Cooper pairs. With 135 CNOT gates, we work in a regime where crosstalk, as opposed to either trotterization or qubit decoherence, dominates the error. Our twirling of neighboring qubits is shown to dramatically improve the noise estimation protocol without the need to add new qubits or circuits and allows for a quantitative simulation of the BCS model.

27 pages, 15 figures

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Cited by in corpus (9)

• Combining Matrix Product States and Noisy Quantum Computers for Quantum Simulation
• Machine learning of quantum channels on NISQ devices
• Optimized Noise Suppression for Quantum Circuits
• Quantum error mitigation in the regime of high noise using deep neural network: Trotterized dynamics
• Echo-evolution data generation for quantum error mitigation via neural networks
• Demonstration of system-bath physics on a gate-based quantum computer
• Dynamic thermalization on noisy quantum hardware
• Feasibility of performing quantum chemistry calculations on quantum computers
• Bayesian inference of general noise-model parameters from the syndrome statistics of surface codes