Quantum Algorithm for Petz Recovery Channels and Pretty Good Measurements
Abstract
The Petz recovery channel plays an important role in quantum information science as an operation that approximately reverses the effect of a quantum channel. The pretty good measurement is a special case of the Petz recovery channel, and it allows for near-optimal state discrimination. A hurdle to the experimental realization of these vaunted theoretical tools is the lack of a systematic and efficient method to implement them. This Letter sets out to rectify this lack: Using the recently developed tools of quantum singular value transformation and oblivious amplitude amplification, we provide a quantum algorithm to implement the Petz recovery channel when given the ability to perform the channel that one wishes to reverse. Moreover, we prove that, in some sense, our quantum algorithm's usage of the channel implementation cannot be improved by more than a quadratic factor. Our quantum algorithm also provides a procedure to perform pretty good measurements when given multiple copies of the states that one is trying to distinguish.
Additional Information
© 2022 American Physical Society. (Received 21 July 2020; revised 14 March 2022; accepted 7 April 2022; published 1 June 2022) We gratefully acknowledge the Simons Institute for the Theory of Computing, where part of this work was conducted. A. G. acknowledges funding provided by Samsung Electronics Co., Ltd., for the project "The Computational Power of Sampling on Quantum Computers." Additional support was provided by the Institute for Quantum Information and Matter, an NSF Physics Frontiers Center (National Science Foundation Grant No. PHY-1733907). S. L. was funded by ARO, AFOSR, and IARPA. I. M. acknowledges support from the U.S. National Science Foundation under Grant No. 1910571. Y. Q. acknowledges support from a Stanford QFARM fellowship and from an NUS Overseas Graduate Scholarship. M. M. W. acknowledges support from the U.S. National Science Foundation under Grant No. 1714215, from Stanford QFARM, and from AFOSR under Grant No. FA9550-19-1-0369.Attached Files
Published - PhysRevLett.128.220502.pdf
Accepted Version - 2006.16924.pdf
Supplemental Material - PRL-appendix.pdf
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Additional details
- Eprint ID
- 115082
- Resolver ID
- CaltechAUTHORS:20220608-849402000
- Samsung
- PHY-1733907
- NSF
- Army Research Office (ARO)
- FA9550-19-1-0369
- Air Force Office of Scientific Research (AFOSR)
- Intelligence Advanced Research Projects Activity (IARPA)
- CCF-1910571
- NSF
- Stanford University
- National University of Singapore
- CCF-1714215
- NSF
- Created
-
2022-06-14Created from EPrint's datestamp field
- Updated
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2022-07-25Created from EPrint's last_modified field
- Caltech groups
- Institute for Quantum Information and Matter