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Statistical Phase Estimation and Error Mitigation on a Superconducting Quantum Processor
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DOI：10.1103/PRXQuantum.4.040341.png)
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Quantum phase estimation (QPE) is a key quantum algorithm, which has been widely studied as a method to perform chemistry and solid-state calculations on future fault-tolerant quantum computers. Recently, several authors have proposed statistical alternatives to QPE that have benefits on early faulttolerant devices, including shorter circuits and better suitability for error-mitigation techniques. However, experimental investigations of the algorithm on real quantum processors are lacking. Here, we implement statistical phase estimation on Rigetti's superconducting processors. Specifically, we use a modification of the Lin and Tong [PRX Quantum 3, 010318 (2022)] algorithm with the improved Fourier approximation of Wan et al. [Phys. Rev. Lett. 129, 030503 (2022)] and apply a variational-compilation technique to reduce the circuit depth. We then incorporate error-mitigation strategies including zero-noise extrapolation and readout-error mitigation with bit-flip averaging. We propose a new method to estimate energies from the statistical phase estimation data, which is found to improve the accuracy in the final energy estimates by 1-2 orders of magnitude with respect to prior theoretical bounds, reducing the cost of performing accurate phase-estimation calculations. We apply these methods to chemistry problems for active spaces up to four electrons in four orbitals, including the application of a quantum embedding method, and use them to correctly estimate energies within chemical precision. Our work demonstrates that statistical phase estimation has a natural resilience to noise, particularly after mitigating coherent errors, and can achieve far higher accuracy than suggested by previous analysis, demonstrating its potential as a valuable quantum algorithm for early fault-tolerant devices.
Keyword:
CORRELATED MOLECULAR CALCULATIONS
GAUSSIAN-BASIS SETS
ENTANGLEMENT
ALGORITHMS
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11
论文数:
919
被引数:
9.0K
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