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As quantum technologies evolve from theoretical curiosities to real-world prototypes, it is worth asking: can we automate the design and calibration of these technologies using advanced computational resources? In this talk, I will discuss some of our recent work which aims to tackle this question using two separate approaches: (1) using near-term quantum computers, and (2) using Bayesian optimization algorithms running on classical computers. I will first introduce the concept of quantum inverse design as a potential application for near-term quantum computers. By defining information theoretic cost functions, such as the quantum and classical Fisher information matrices, I will present a two-step variational quantum algorithm that aims to solve the inverse design problem for quantum multi-parameter estimation. To illustrate the potential of this approach, I will show that it can be used to design difficult-to-simulate experiments in quantum metrology with applications in sensing, imaging, and spectroscopy. In the second part of the talk, I will show how Bayesian optimization algorithms (running on classical computers) can be used to automate the calibration of quantum optics experiments. Here, I will present the Bayesian optimization framework from scratch, highlighting the relevant cost functions, kernels, initial sampling schemes, and acquisition functions that help automate the experimental calibration process under noisy and photon-starved conditions. To validate our approach, I will also present a proof-of-concept demonstration of the optimization of an experimental Hong-Ou-Mandel measurement scheme. Finally, I will discuss the relevance of these results to optical-fibre-based quantum networks and conclude with a discussion on future directions.
Topic: INQNET seminar
Time: January 25, 2021 12:30 PM Pacific Time (US and Canada)
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https://caltech.zoom.us/j/93304584361
Meeting ID: 933 0458 4361
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Nikolai Lauk