Variational quantum process tomography

TL;DR

This paper introduces a quantum machine learning algorithm that efficiently reconstructs unknown quantum processes with high fidelity using fewer measurements, significantly reducing the complexity compared to traditional methods.

Contribution

It proposes a novel variational quantum process tomography method that encodes quantum processes into shallow quantum circuits, enabling efficient reconstruction of processes up to 8 qubits.

Findings

High fidelity reconstruction of quantum processes up to 8 qubits.

Requires at least 100 times fewer input states than standard tomography.

Effective for Hamiltonian evolution and random quantum circuits.

Abstract

Quantum process tomography is an experimental technique to fully characterize an unknown quantum process. Standard quantum process tomography suffers from exponentially scaling of the number of measurements with the increasing system size. In this work, we put forward a quantum machine learning algorithm which approximately encodes the unknown unitary quantum process into a relatively shallow depth parametric quantum circuit. We demonstrate our method by reconstructing the unitary quantum processes resulting from the quantum Hamiltonian evolution and random quantum circuits up to $8$ qubits. Results show that those quantum processes could be reconstructed with high fidelity, while the number of input states required are at least $2$ orders of magnitude less than required by the standard quantum process tomography.