Can a machine infer quantum mechanics?
by Prof. Shay Hacohen-Gourgy
at Physics Colloquium
Tue, 07 Jan 2020, 15:30
Ilse Katz Institute for Nanoscale Science & Technology (51), room 015
In this talk I will introduce circuit-QED, a platform based on superconducting qubits coupled to resonators. I will discuss the exquisite control and improvement in coherence times that has pushed circuit-QED to become one of the leading technologies for realizing quantum computers. One remarkable experimental feat is the ability to probe these circuits near the quantum limit. These precision measurements, together with advanced control, allow to faithfully track the quantum trajectories, simultaneously probe several observables, entangle remote qubits, probe them with squeezed light, and in general constitute an ideal quantum optics playground. The standard method of tracking quantum evolution requires knowledge of the system Hamiltonian, yet quantum mechanics can be formulated as an extended information theory, where the physical system is treated as a black box in which preparation and measurement combine to give the probabilities of experimental outcomes. This suggests that advanced tools such as neural networks may be trained to predict stochastic quantum evolution without a priori specifying the rules of quantum theory. I will demonstrate that a recurrent neural network can be trained to infer the individual quantum trajectories associated with the evolution of a superconducting qubit from raw observations only.
Created on 11-11-2019 by Schechter, Moshe (smoshe)
Updaded on 01-01-2020 by Schechter, Moshe (smoshe)