Two-Unitary Decomposition Algorithm and Open Quantum System Simulation

Journal Article (2023)
Author(s)

Nishchay Suri (NASA Ames Research Center, USRA Research Institute for Advanced Computer Science, Mountain View, Carnegie Mellon University)

Joseph Barreto (TU Delft - Applied Sciences, TU Delft - QuTech Advanced Research Centre)

Stuart Hadfield (NASA Ames Research Center, USRA Research Institute for Advanced Computer Science, Mountain View)

Nathan Wiebe (Pacific Northwest National Laboratory, University of Toronto)

Filip Wudarski (NASA Ames Research Center, USRA Research Institute for Advanced Computer Science, Mountain View)

Jeffrey Marshall (USRA Research Institute for Advanced Computer Science, Mountain View, NASA Ames Research Center)

Research Institute
QuTech Advanced Research Centre
DOI related publication
https://doi.org/10.22331/Q-2023-05-15-1002 Final published version
More Info
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Publication Year
2023
Language
English
Research Institute
QuTech Advanced Research Centre
Volume number
7
Article number
1002
Downloads counter
270
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Abstract

Simulating general quantum processes that describe realistic interactions of quantum systems following a non-unitary evolution is challenging for conventional quantum computers that directly implement unitary gates. We analyze complexities for promising methods such as the Sz.-Nagy dilation and linear combination of unitaries that can simulate open systems by the probabilistic realization of non-unitary operators, requiring multiple calls to both the encoding and state preparation oracles. We propose a quantum two-unitary decomposition (TUD) algorithm to decompose a d-dimensional operator A with non-zero singular values as A = (U1 + U2)/2 using the quantum singular value transformation algorithm, avoiding classically expensive singular value decomposition (SVD) with an O(d3) overhead in time. The two unitaries can be deterministically implemented, thus requiring only a single call to the state preparation oracle for each. The calls to the encoding oracle can also be reduced significantly at the expense of an acceptable error in measurements. Since the TUD method can be used to implement non-unitary operators as only two unitaries, it also has potential applications in linear algebra and quantum machine learning.