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Q-Farm Quantum Seminar Series presents "Learning Adaptive Quantum State Tomography with Neural Networks and Differentiable Programming"

Learning Adaptive Quantum State Tomography with Neural Networks and Differentiable Programming
Wednesday, February 19, 2020 - 12:00pm
Physics/Astrophysics (Varian II) Building, Room 102/103
Stanislav Fort (PhD Candidate, Stanford University)
Abstract / Description: 

Quantum State Tomography is the task of determining an unknown quantum state by making measurements on identical copies of the state. Current algorithms are costly both on the experimental front -- requiring vast numbers of measurements -- as well as in terms of the computational time to analyze those measurements. In this talk, we address the problem of analysis speed and flexibility, introducing Neural Adaptive Quantum State Tomography (NA-QST), a machine learning based algorithm for quantum state tomography that adapts measurements and provides orders of magnitude faster processing while retaining state-of-the-art reconstruction accuracy. Our algorithm is inspired by particle swarm optimization and Bayesian particle-filter based adaptive methods, which we extend and enhance using neural networks. The resampling step, in which a bank of candidate solutions -- particles -- is refined, is in our case learned directly from data, removing the computational bottleneck of standard methods. We successfully replace the Bayesian calculation that requires computational time of O(poly(n)) with a learned heuristic whose time complexity empirically scales as O(log(n)) with the number of copies measured n, while retaining very comparable reconstruction accuracy. This corresponds to a factor of a million speedup for 10^7 copies measured. We demonstrate that our algorithm learns to work with basis, symmetric informationally complete (SIC), as well as other types of POVMs. We discuss the value of measurement adaptivity for each POVM type, demonstrating that its effect is significant only for basis POVMs. Our algorithm can be retrained within hours on a single laptop for a two-qubit situation, which suggests a feasible time-cost when extended to larger systems. It can also adapt to a subset of possible states, a choice of the type of measurement, and other experimental details.

Paper link:


Stanislav Fort is a PhD student at Stanford University working on 1) understanding deep learning and 2) applying it to challenging problems in physics, with a special focus on quantum. He received his BA and MSci degrees from the University of Cambridge, and spent the last year as a full time AI researcher at Google Research, working primarily with Google Brain and DeepMind.