Quantum state tomography with disentanglement algorithm

TL;DR

This paper introduces a universal quantum state tomography method using a disentanglement algorithm with variational circuits, reducing measurement complexity and employing reinforcement learning for efficient quantum circuit design.

Contribution

It presents a novel disentanglement-based quantum state reconstruction approach that minimizes measurements and is adaptable to various quantum states without specific ansatz constraints.

Findings

Successfully reconstructs quantum states with fewer measurements.

Employs reinforcement learning for optimized circuit design.

Demonstrates effectiveness on random quantum states.

Abstract

In this work, we report on a novel quantum state reconstruction process based on the disentanglement algorithm. Using variational quantum circuits, we disentangle the quantum state to a product of computational zero states. Inverse evolution of the zero states reconstructs the quantum state up to an overall phase. By sequentially disentangling the qubit one by one, we reduce the required measurements with only single qubit measurement. Demonstrations with our proposal for the reconstruction of the random states are presented where variational quantum circuit is optimized by disentangling process. To facilitate experimental implementation, we also employ reinforcement learning for quantum circuit design with limited discrete quantum gates. Our method is universal and imposes no specific ansatz or constrain on the quantum state.