Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels

Hari Hara Suthan Chittoor; Osvaldo Simeone; Leonardo Banchi; Stefano Pirandola

doi:10.1109/ITW55543.2023.10161641

Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels

Hari Hara Suthan Chittoor, Osvaldo Simeone, Leonardo Banchi, Stefano Pirandola

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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.

Original language	English (US)
Title of host publication	2023 IEEE Information Theory Workshop, ITW 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	175-180
Number of pages	6
ISBN (Electronic)	9798350301496
DOIs	https://doi.org/10.1109/ITW55543.2023.10161641
State	Published - 2023
Externally published	Yes
Event	2023 IEEE Information Theory Workshop, ITW 2023 - Saint-Malo, France Duration: Apr 23 2023 → Apr 28 2023

Publication series

Name	2023 IEEE Information Theory Workshop, ITW 2023

Conference

Conference	2023 IEEE Information Theory Workshop, ITW 2023
Country/Territory	France
City	Saint-Malo
Period	4/23/23 → 4/28/23

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Computational Theory and Mathematics
Computer Networks and Communications
Signal Processing
Control and Optimization

Keywords

Programmable quantum computing
convex optimization
online learning
quantum channel simulation

Access to Document

10.1109/ITW55543.2023.10161641

Cite this

Suthan Chittoor, H. H., Simeone, O., Banchi, L., & Pirandola, S. (2023). Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels. In 2023 IEEE Information Theory Workshop, ITW 2023 (pp. 175-180). (2023 IEEE Information Theory Workshop, ITW 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITW55543.2023.10161641

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title = "Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels",

abstract = "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.",

keywords = "Programmable quantum computing, convex optimization, online learning, quantum channel simulation",

author = "{Suthan Chittoor}, {Hari Hara} and Osvaldo Simeone and Leonardo Banchi and Stefano Pirandola",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE Information Theory Workshop, ITW 2023 ; Conference date: 23-04-2023 Through 28-04-2023",

year = "2023",

doi = "10.1109/ITW55543.2023.10161641",

language = "English (US)",

series = "2023 IEEE Information Theory Workshop, ITW 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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booktitle = "2023 IEEE Information Theory Workshop, ITW 2023",

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Suthan Chittoor, HH, Simeone, O, Banchi, L & Pirandola, S 2023, Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels. in 2023 IEEE Information Theory Workshop, ITW 2023. 2023 IEEE Information Theory Workshop, ITW 2023, Institute of Electrical and Electronics Engineers Inc., pp. 175-180, 2023 IEEE Information Theory Workshop, ITW 2023, Saint-Malo, France, 4/23/23. https://doi.org/10.1109/ITW55543.2023.10161641

Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels. / Suthan Chittoor, Hari Hara; Simeone, Osvaldo; Banchi, Leonardo et al.
2023 IEEE Information Theory Workshop, ITW 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 175-180 (2023 IEEE Information Theory Workshop, ITW 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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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

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

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Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels

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