Variational quantum process tomography

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.

Classical Shadows for Quantum Process Tomography on Near-term Quantum Computers

“…Earlier attempts at QPT used the linear inversion method [7,8]. Later various statistical methods were developed including maximum likelihood methods [9][10][11][12][13], Bayesian methods [14][15][16], compressed sensing methods [17], tensor network methods [18] and other optimization techniques [19][20][21][22][23][24][25]. Theoretically, quantum process tomography can be related to quantum state tomography through the Jamio lkowski process-state isomorphism [26,27].…”