On the experimental feasibility of quantum state reconstruction via machine learning

Abstract

We determine the resource scaling of machine learning-based quantum state reconstruction methods, in terms of inference and training, for systems of up to four qubits when constrained to pure states. Further, we examine system performance in the low-count regime, likely to be encountered in the tomography of high-dimensional systems. Finally, we implement our quantum state reconstruction method on an IBM Q quantum computer, and compare against both unconstrained and constrained MLE state reconstruction.

9 pages

References in corpus (5)

• Scalable multi-particle entanglement of trapped ions
• Synthesis of Quantum Logic Circuits
• Choice of Measurement Sets in Qubit Tomography
• Knowledge and ignorance in incomplete quantum state tomography
• Quantum State Tomography with Conditional Generative Adversarial Networks

Cited by in corpus (6)

• Quantum Machine Learning: from physics to software engineering
• Regression of high dimensional angular momentum states of light
• Improving application performance with biased distributions of quantum states
• Data-Centric Machine Learning in Quantum Information Science
• Neural networks for quantum state tomography with constrained measurements
• Demonstration of machine-learning-enhanced Bayesian quantum state estimation