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

arXiv: https://arxiv.org/abs/2212.05145 · HTML: https://mlsanity.com/paper/2212.05145/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 21 references that are in the map.
- First posted on arXiv 9 Dec 2022.

## Builds on (most cited first, 3 of 3)

- [Online Learning: A Modern Introduction Using Convex Optimization](https://mlsanity.com/paper/1912.13213/) (2019)
- [Online Learning of Quantum States](https://mlsanity.com/paper/1802.09025/) (2018)
- [An Introduction to Quantum Machine Learning for Engineers](https://mlsanity.com/paper/2205.09510/) (2022)

## Built on by (most cited first, 1 of 1)

- [Sequential Capacity of Quantum Processes with Finite Memory](https://mlsanity.com/paper/2610.02068/) (2026)

## Abstract (arXiv)

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.