Author
Listed:
- Joonhee Choi
(California Institute of Technology)
- Adam L. Shaw
(California Institute of Technology)
- Ivaylo S. Madjarov
(California Institute of Technology)
- Xin Xie
(California Institute of Technology)
- Ran Finkelstein
(California Institute of Technology)
- Jacob P. Covey
(California Institute of Technology
The University of Illinois at Urbana-Champaign)
- Jordan S. Cotler
(Harvard University)
- Daniel K. Mark
(Massachusetts Institute of Technology)
- Hsin-Yuan Huang
(California Institute of Technology)
- Anant Kale
(Harvard University)
- Hannes Pichler
(University of Innsbruck
Institute for Quantum Optics and Quantum Information, Austrian Academy of Sciences)
- Fernando G. S. L. Brandão
(California Institute of Technology)
- Soonwon Choi
(Massachusetts Institute of Technology
University of California)
- Manuel Endres
(California Institute of Technology)
Abstract
Producing quantum states at random has become increasingly important in modern quantum science, with applications being both theoretical and practical. In particular, ensembles of such randomly distributed, but pure, quantum states underlie our understanding of complexity in quantum circuits1 and black holes2, and have been used for benchmarking quantum devices3,4 in tests of quantum advantage5,6. However, creating random ensembles has necessitated a high degree of spatio-temporal control7–12 placing such studies out of reach for a wide class of quantum systems. Here we solve this problem by predicting and experimentally observing the emergence of random state ensembles naturally under time-independent Hamiltonian dynamics, which we use to implement an efficient, widely applicable benchmarking protocol. The observed random ensembles emerge from projective measurements and are intimately linked to universal correlations built up between subsystems of a larger quantum system, offering new insights into quantum thermalization13. Predicated on this discovery, we develop a fidelity estimation scheme, which we demonstrate for a Rydberg quantum simulator with up to 25 atoms using fewer than 104 experimental samples. This method has broad applicability, as we demonstrate for Hamiltonian parameter estimation, target-state generation benchmarking, and comparison of analogue and digital quantum devices. Our work has implications for understanding randomness in quantum dynamics14 and enables applications of this concept in a much wider context4,5,9,10,15–20.
Suggested Citation
Joonhee Choi & Adam L. Shaw & Ivaylo S. Madjarov & Xin Xie & Ran Finkelstein & Jacob P. Covey & Jordan S. Cotler & Daniel K. Mark & Hsin-Yuan Huang & Anant Kale & Hannes Pichler & Fernando G. S. L. Br, 2023.
"Preparing random states and benchmarking with many-body quantum chaos,"
Nature, Nature, vol. 613(7944), pages 468-473, January.
Handle:
RePEc:nat:nature:v:613:y:2023:i:7944:d:10.1038_s41586-022-05442-1
DOI: 10.1038/s41586-022-05442-1
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Citations
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Cited by:
- Lih-King Lim & Cunzhong Lou & Chushun Tian, 2024.
"Mesoscopic fluctuations in entanglement dynamics,"
Nature Communications, Nature, vol. 15(1), pages 1-8, December.
- Yen-Jui Chang & Wei-Ting Wang & Hao-Yuan Chen & Shih-Wei Liao & Ching-Ray Chang, 2023.
"A novel approach for quantum financial simulation and quantum state preparation,"
Papers
2308.01844, arXiv.org, revised Apr 2024.
- Fangjun Hu & Saeed A. Khan & Nicholas T. Bronn & Gerasimos Angelatos & Graham E. Rowlands & Guilhem J. Ribeill & Hakan E. Türeci, 2024.
"Overcoming the coherence time barrier in quantum machine learning on temporal data,"
Nature Communications, Nature, vol. 15(1), pages 1-12, December.
- Yen-Jui Chang & Wei-Ting Wang & Hao-Yuan Chen & Shih-Wei Liao & Ching-Ray Chang, 2023.
"Preparing random state for quantum financing with quantum walks,"
Papers
2302.12500, arXiv.org, revised Mar 2023.
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