Convex optimization of programmable quantum computers
Name
s41534-020-0268-2.pdf
Description
Published version
Size
1.08 MB
Format
Unknown
Checksum (MD5)
0fe809596d7e544dea43a13901a19495
Author(s)
Banchi, Leonardo
Pereira, Jason
Lloyd, Seth
Pirandola, Stefano
Date Issued
2020
Journal
npj Quantum Information
Publisher
Springer Science and Business Media LLC
Version
Final published version
Abstract
© 2020, The Author(s). A fundamental model of quantum computation is the programmable quantum gate array. This is a quantum processor that is fed by a program state that induces a corresponding quantum operation on input states. While being programmable, any finite-dimensional design of this model is known to be nonuniversal, meaning that the processor cannot perfectly simulate an arbitrary quantum channel over the input. Characterizing how close the simulation is and finding the optimal program state have been open questions for the past 20 years. Here, we answer these questions by showing that the search for the optimal program state is a convex optimization problem that can be solved via semidefinite programming and gradient-based methods commonly employed for machine learning. We apply this general result to different types of processors, from a shallow design based on quantum teleportation, to deeper schemes relying on port-based teleportation and parametric quantum circuits.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Research Laboratory of Electronics
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://dx.doi.org/10.1038/s41534-020-0268-2
