Inverted-circuit zero-noise extrapolation for quantum gate error mitigation (2403.01608v3)
Abstract: A common approach to deal with gate errors in modern quantum-computing hardware is zero-noise extrapolation. By artificially amplifying errors and extrapolating the expectation values obtained with different error strengths towards the zero-error (zero-noise) limit, the technique aims at rectifying errors in noisy quantum computing systems. For an accurate extrapolation, it is essential to know the exact factors of the noise amplification. In this article, we propose a simple method for estimating the strength of errors occurring in a quantum circuit and demonstrate improved extrapolation results. The method determines the error strength for a circuit by appending to it the inverted circuit and measuring the probability of the initial state. The estimation of error strengths is easy to implement for arbitrary circuits and does not require a previous characterisation of noise properties. We compare this method with the conventional zero-noise extrapolation method and show that the novel method leads to a more accurate calculation of expectation values. Our method proves to be particularly effective on current hardware, showcasing its suitability for near-term quantum computing applications.
- J. Preskill, Quantum computing in the NISQ era and beyond, Quantum 2, 79 (2018).
- K. Temme, S. Bravyi, and J. M. Gambetta, Error mitigation for short-depth quantum circuits, Phys. Rev. Lett. 119, 180509 (2017).
- Y. Li and S. C. Benjamin, Efficient variational quantum simulator incorporating active error minimization, Phys. Rev. X 7, 021050 (2017).
- M. Krebsbach, B. Trauzettel, and A. Calzona, Optimization of richardson extrapolation for quantum error mitigation, Phys. Rev. A 106, 062436 (2022).
- A. Mari, N. Shammah, and W. J. Zeng, Extending quantum probabilistic error cancellation by noise scaling, Phys. Rev. A 104, 052607 (2021).
- S. Endo, S. C. Benjamin, and Y. Li, Practical quantum error mitigation for near-future applications, Phys. Rev. X 8, 031027 (2018).
- E. Magesan, J. M. Gambetta, and J. Emerson, Scalable and robust randomized benchmarking of quantum processes, Phys. Rev. Lett. 106, 180504 (2011).
- E. Magesan, J. M. Gambetta, and J. Emerson, Characterizing quantum gates via randomized benchmarking, Phys. Rev. A 85, 042311 (2012).
- Z. Cai, Multi-exponential error extrapolation and combining error mitigation techniques for NISQ applications, npj Quantum Information 7, 80 (2021).
- Z. Cai, A practical framework for quantum error mitigation (2023), arXiv:2110.05389 [quant-ph] .
- J. J. Wallman and J. Emerson, Noise tailoring for scalable quantum computation via randomized compiling, Phys. Rev. A 94, 052325 (2016).
- D. Gottesman, Theory of fault-tolerant quantum computation, Phys. Rev. A 57, 127 (1998).
- A. Ketterer and T. Wellens, Characterizing crosstalk of superconducting transmon processors, Phys. Rev. Appl. 20, 034065 (2023).
- A. W. Harrow, A. Hassidim, and S. Lloyd, Quantum algorithm for linear systems of equations, Phys. Rev. Lett. 103, 150502 (2009).
- A. Zaman, H. J. Morrell, and H. Y. Wong, A step-by-step HHL algorithm walkthrough to enhance understanding of critical quantum computing concepts, IEEE Access 11, 77117 (2023).