Simulating quench dynamics on a digital quantum computer with data-driven error mitigation

Abstract

Error mitigation is likely to be key in obtaining near term quantum advantage. In this work we present one of the first implementations of several Clifford data regression based methods which are used to mitigate the effect of noise in real quantum data. We explore the dynamics of the 1-D Ising model with transverse and longitudinal magnetic fields, highlighting signatures of confinement. We find in general Clifford data regression based techniques are advantageous in comparison with zero-noise extrapolation and obtain quantitative agreement with exact results for systems of 9 qubits with circuit depths of up to 176, involving hundreds of CNOT gates. This is the largest systems investigated so far in a study of this type. We also investigate the two-point correlation function and find the effect of noise on this more complicated observable can be mitigated using Clifford quantum circuit data highlighting the utility of these methods.

arXiv comment16 pages, 13 figures

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• Algebraic Bethe Circuits
• Confinement and Kink Entanglement Asymmetry on a Quantum Ising Chain
• Mitigating algorithmic errors in quantum optimization through energy extrapolation
• Qibolab: an open-source hybrid quantum operating system
• Towards the real-time evolution of gauge-invariant ${\mathbb{Z}}_2$ and $U(1)$ quantum link models on NISQ Hardware with error-mitigation
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• Comparison of encoding schemes for quantum computing of $S>1/2$ spin chains
• Improving the efficiency of learning-based error mitigation
• Quantum noise modeling through Reinforcement Learning
• Extracting the spin excitation spectrum of a fermionic system using a quantum processor
• Efficient Eigenstate Preparation in an Integrable Model with Hilbert Space Fragmentation