Quantum Techniques in Machine Learning

Normalizing Flows for Classical Simulation of Quantum Circuits in Phase Space

Not scheduled

20m

Jan Mouton Learning Centre (Stellenbosch University)

Poster

Speaker

Edward Jiang (ICFO)

Description

We present a novel framework to learn a representation of quantum circuits based on the phase
space formulation of many-qubit systems. Given that N-qubit states can be represented as quasiprobability 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.

Presentation materials

QTML_abstract.pdf