Quantum Machine Learning for State Tomography Using Classical Data

TL;DR

This paper introduces a quantum machine learning protocol for quantum state tomography that operates solely on classical measurement data, is compatible with NISQ devices, and achieves high-fidelity reconstructions.

Contribution

It presents the first QML-based tomography scheme implemented on real quantum hardware using only classical measurement data, demonstrating scalability and practicality.

Findings

Achieved high-fidelity state reconstructions (90%+) in simulations.

Validated the protocol on IBM and IonQ quantum hardware.

Showed accurate tomography with incomplete measurement bases.

Abstract

Reconstructing quantum states from measurement data represents a formidable challenge in quantum information science, especially as system sizes grow beyond the reach of traditional tomography methods. While recent studies have explored quantum machine learning (QML) for quantum state tomography (QST), nearly all rely on idealized assumptions, such as direct access to the unknown quantum state as quantum data input, which are incompatible with current hardware constraints. In this work, we present a QML-based tomography protocol that operates entirely on classical measurement data and is fully executable on noisy intermediate-scale quantum (NISQ) devices. Our approach employs a variational quantum circuit trained to reconstruct quantum states based solely on measurement outcomes. We test the method in simulation, achieving high-fidelity reconstructions of diverse quantum states, including GHZ states, spin chain ground states, and states generated by random circuits. The protocol is then validated on quantum hardware from IBM and IonQ. Additionally, we demonstrate accurate tomography is possible using incomplete measurement bases, a crucial step towards scaling up our protocol. Our results in various scenarios illustrate successful state reconstruction with fidelity reaching 90% or higher. To our knowledge, this is the first QML-based tomography scheme that has been implemented on real quantum processors using exclusively classical measurements. This work establishes the feasibility of QML-based tomography on current quantum platforms and offers a scalable pathway for practical quantum state reconstruction.