Online Convex Optimization of Programmable Quantum Computers to Simulate Time-Varying Quantum Channels
This addresses the challenge of approximating adversarial time-varying quantum operations for quantum computing applications, representing an incremental advance in simulation techniques.
The paper tackled the problem of simulating time-varying quantum channels with a programmable quantum processor, proposing matrix exponentiated gradient descent (MEGD) and showing it achieves sublinear regret in time, validated through experiments on dephasing channels.
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.