TY - GEN
T1 - Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels
AU - Suthan Chittoor, Hari Hara
AU - Simeone, Osvaldo
AU - Banchi, Leonardo
AU - Pirandola, Stefano
N1 - Funding Information:
the European Union's Horizon 2020 Research and Innovation Programme (Grant Agreement No. 725731), and Osvaldo Simeone has also been supported by an Open Fellowship of the EPSRC (EP/W024101/1). For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. The authors acknowledge use of the research computing facility at King's College London, Rosalind (https://rosalind.kcl.ac.uk).
Funding Information:
Hari Hara Suthan Chittoor and Osvaldo Simeone are with King’s Communications, Learning, and Information Processing (KCLIP) lab at the Department of Engineering of Kings College London, UK (emails: hari.hara@kcl.ac.uk, osvaldo.simeone@kcl.ac.uk). Their work has been supported by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (Grant Agreement No. 725731), and Osvaldo Simeone has also been supported by an Open Fellowship of the EPSRC (EP/W024101/1). For the purpose of open access, the author has applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising. The authors acknowledge use of the research computing facility at King’s College London, Rosalind (https://rosalind.kcl.ac.uk).
Funding Information:
Leonardo Banchi is with the Department of Physics and Astronomy, University of Florence & INFN sezione di Firenze, via G. Sansone 1, I-50019 Sesto Fiorentino (FI), Italy (email: leonardo.banchi@unifi.it). His work is supported by the U.S. Department of Energy, Office of Science, National Quantum Information Science Research Centers, Superconducting Quantum Materials and Systems Center (SQMS) under the contract No. DE-AC02-07CH11359.
Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Simulating quantum channels is a fundamental primitive in quantum computing, since quantum channels define general (trace-preserving) quantum operations. An arbitrary quantum channel cannot be exactly simulated using a finite-dimensional programmable quantum processor, making it important to develop optimal approximate simulation techniques. In this paper, we study the challenging setting in which the channel to be simulated varies adversarially with time. We propose the use of matrix exponentiated gradient descent (MEGD), an online convex optimization method, and analytically show that it achieves a sublinear regret in time. Through experiments, we validate the main results for time-varying dephasing channels using a programmable generalized teleportation processor.
AB - Simulating quantum channels is a fundamental primitive in quantum computing, since quantum channels define general (trace-preserving) quantum operations. An arbitrary quantum channel cannot be exactly simulated using a finite-dimensional programmable quantum processor, making it important to develop optimal approximate simulation techniques. In this paper, we study the challenging setting in which the channel to be simulated varies adversarially with time. We propose the use of matrix exponentiated gradient descent (MEGD), an online convex optimization method, and analytically show that it achieves a sublinear regret in time. Through experiments, we validate the main results for time-varying dephasing channels using a programmable generalized teleportation processor.
KW - Programmable quantum computing
KW - convex optimization
KW - online learning
KW - quantum channel simulation
UR - http://www.scopus.com/inward/record.url?scp=85165029729&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85165029729&partnerID=8YFLogxK
U2 - 10.1109/ITW55543.2023.10161641
DO - 10.1109/ITW55543.2023.10161641
M3 - Conference contribution
AN - SCOPUS:85165029729
T3 - 2023 IEEE Information Theory Workshop, ITW 2023
SP - 175
EP - 180
BT - 2023 IEEE Information Theory Workshop, ITW 2023
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE Information Theory Workshop, ITW 2023
Y2 - 23 April 2023 through 28 April 2023
ER -