Portfolio optimization with digitized counterdiabatic quantum algorithms

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Date
2022-12
Author
Hegade, Narendra N.
Chandarana, Pranav
Paul, Koushik
Chen, Xi
Albarrán Arriagada, Francisco
Solano Villanueva, Enrique Leónidas ORCID
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Physical Review Research 4 : (2022) // Article ID 043204
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http://hdl.handle.net/10810/59605
Abstract
We consider digitized-counterdiabatic quantum computing as an advanced paradigm to approach quantum advantage for industrial applications in the NISQ era. We apply this concept to investigate a discrete meanvariance portfolio optimization problem, showing its usefulness in a key finance application. Our analysis shows a drastic improvement in the success probabilities of the resulting digital quantum algorithm when approximate counterdiabatic techniques are introduced. Along these lines, we discuss the enhanced performance of our methods over variational quantum algorithms like QAOA and DC-QAOA.
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.
Except where otherwise noted, this item's license is described as Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.