Attention-based Quantum Tomography

TL;DR

This paper introduces Attention-based Quantum Tomography (AQT), a neural network model utilizing attention mechanisms to accurately reconstruct noisy quantum states and their density matrices, outperforming previous methods and demonstrating experimental success on IBMQ hardware.

Contribution

The paper presents a novel attention-based neural network approach for quantum state reconstruction that effectively models entanglement and correlations in noisy quantum systems.

Findings

AQT outperforms previous neural network methods in quantum state reconstruction.

AQT accurately reconstructs density matrices of noisy quantum states on IBMQ.

The attention mechanism effectively captures quantum entanglement and correlations.

Abstract

With rapid progress across platforms for quantum systems, the problem of many-body quantum state reconstruction for noisy quantum states becomes an important challenge. Recent works found promise in recasting the problem of quantum state reconstruction to learning the probability distribution of quantum state measurement vectors using generative neural network models. Here we propose the "Attention-based Quantum Tomography" (AQT), a quantum state reconstruction using an attention mechanism-based generative network that learns the mixed state density matrix of a noisy quantum state. The AQT is based on the model proposed in "Attention is all you need" by Vishwani et al (2017) that is designed to learn long-range correlations in natural language sentences and thereby outperform previous natural language processing models. We demonstrate not only that AQT outperforms earlier neural-network-based quantum state reconstruction on identical tasks but that AQT can accurately reconstruct the density matrix associated with a noisy quantum state experimentally realized in an IBMQ quantum computer. We speculate the success of the AQT stems from its ability to model quantum entanglement across the entire quantum system much as the attention model for natural language processing captures the correlations among words in a sentence.