II Machine Learning Methods for Complex and Quantum Systems

Normalizing Flows for Classical Simulation of Quantum Circuits in Phase Space

Not scheduled

20m

Aula Magna (Facultat de Física, Universitat de Barcelona)

Poster

Description

We present a novel framework to learn a representation of quantum circuits based on the phase space formulation of many-body systems. Given that N-qubit states can be represented as quasi-probability distributions on a 2N-dimensional manifold, we show how unitary gates can be parameterized as normalizing flows between these distributions, opening a new avenue for AI-assisted quantum circuit simulation. We evaluate our method on two common families of quantum circuits, and show that it scales better than the current state-of-the-art to large numbers of qubits.