Dimension-adaptive machine-learning-based quantum state reconstruction

TL;DR

This paper presents a machine learning approach for quantum state reconstruction that can adapt to different system sizes without retraining, significantly reducing resource requirements.

Contribution

It introduces a dimension-adaptive machine learning method that reconstructs quantum states across varying system sizes using a single trained model.

Findings

Successfully reconstructed states of 1-3 qubits using a model trained on larger systems.

Reconstruction time scales more favorably than training time, saving resources.

Eliminates need for training separate models for each system dimension.

Abstract

We introduce an approach for performing quantum state reconstruction on systems of $n$ qubits using a machine-learning-based reconstruction system trained exclusively on $m$ qubits, where $m\geq n$. This approach removes the necessity of exactly matching the dimensionality of a system under consideration with the dimension of a model used for training. We demonstrate our technique by performing quantum state reconstruction on randomly sampled systems of one, two, and three qubits using machine-learning-based methods trained exclusively on systems containing at least one additional qubit. The reconstruction time required for machine-learning-based methods scales significantly more favorably than the training time; hence this technique can offer an overall savings of resources by leveraging a single neural network for dimension-variable state reconstruction, obviating the need to train dedicated machine-learning systems for each Hilbert space.