A Platform Independent Formula for Quantum State Benchmarking

Daniel Mark | Soonwon Choi | Joonhee Choi | Adam Shaw | Manuel Endres

Benchmarking a quantum device is a central task in quantum science and technology, but remains experimentally challenging. In particular, existing benchmarking methods require many samples and are not applicable to analog quantum simulators, due to their limited controllability. We propose a benchmarking formula that estimates the fidelity between an experimentally prepared state and a known target state, applicable to any analog quantum device. Our scheme does not require fine-tuned dynamics and initial states, and only requires experimental measurements in a fixed basis after a quench evolution. With increasing system size, our protocol has a small, constant sample complexity and improves in accuracy. The dominant cost of our protocol is the computational cost of the target state. We discuss possible applications, including Hamiltonian parameter estimation and quantum phase estimation.

 

Funding Sources: Institute for Quantum Information and Matter | Caltech (NSF Grant PHY-1733907) | NSF CAREER Award (1753386) | AFOSR Young Investigator Program (FA9550-19-1-0044) | DARPA ONIQS Program (W911NF2010021) | Army Research Office MURI Program (W911NF2010136) | NSF QLCI Program (2016245) | IQIM Postdoctoral Fellowship, Caltech | Eddleman Quantum Graduate Fellowship | Miller Institute for Basic Research in Science | MIT Department of Physics

Daniel Mark

Affiliation: MIT Department of Physics, Graduate Student

Areas of Research

    • Quantum Algorithms & Machine Learning

Open to

    • Internships