Error-mitigated fermionic classical shadows on noisy quantum devices

Wu，Bujiao1,2,3; Koh，Dax Enshan4

2024-12-01

10.1038/s41534-024-00836-7

npj Quantum Information   影响因子和分区

2056-6387

Efficiently estimating fermionic Hamiltonian expectation values is vital for simulating various physical systems. Classical shadow (CS) algorithms offer a solution by reducing the number of quantum state copies needed, but noise in quantum devices poses challenges. We propose an error-mitigated CS algorithm assuming gate-independent, time-stationary, and Markovian (GTM) noise. For n-qubit systems, our algorithm, which employs the easily prepared initial state 00 assumed to be noiseless, efficiently estimates k-RDMs with Õ(kn) state copies and Õ(n) calibration measurements for GTM noise with constant fidelities. We show that our algorithm is robust against noise types like depolarizing, damping, and X-rotation noise with constant strengths, showing scalings akin to prior CS algorithms for fermions but with better noise resilience. Numerical simulations confirm our algorithm’s efficacy in noisy settings, suggesting its viability for near-term quantum devices.

SCI

英语

通讯

2-s2.0-85190586377

Scopus

1.Dahlem Center for Complex Quantum Systems,Freie Universität Berlin,Berlin,14195,Germany
2.Shenzhen Institute for Quantum Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Center on Frontiers of Computing Studies,Peking University,Beijing,100871,China
4.Institute of High Performance Computing (IHPC),Agency for Science,Technology and Research (A*STAR),Singapore,1 Fusionopolis Way, #16-16 Connexis,138632,Singapore

Wu，Bujiao,Koh，Dax Enshan. Error-mitigated fermionic classical shadows on noisy quantum devices[J]. npj Quantum Information,2024,10(1).

Wu，Bujiao,&Koh，Dax Enshan.(2024).Error-mitigated fermionic classical shadows on noisy quantum devices.npj Quantum Information,10(1).

Wu，Bujiao,et al."Error-mitigated fermionic classical shadows on noisy quantum devices".npj Quantum Information 10.1(2024).