<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Finding optimal measurement schemes in quantum state tomography is a fundamental problem in quantum computation. It is known that for non-degenerate operators the optimal measurement scheme is based on mutually unbiassed bases. This paper is a follow up from our previous work, where we use standard numerical approaches to look for optimal measurement schemes, where the measurement operators are projections on individual pure quantum states. In this paper we demonstrate the usefulness of several machine learning techniques – reinforcement learning and parallel machine learning approaches, to discover measurement schemes, which are significantly better than the ones discovered by standard numerical methods in our previous work. The high-performing quorums of projection operators we have discovered have complex structure and symmetries, which may imply that the optimal solution will possess such symmetries.</p></abstract>

Finding optimal measurement schemes in quantum state tomography is a fundamental problem in quantum computation. It is known that for non-degenerate operators the optimal measurement scheme is based on mutually unbiassed bases. This paper is a follow up from our previous work, where we use standard numerical approaches to look for optimal measurement schemes, where the measurement operators are projections on individual pure quantum states. In this paper we demonstrate the usefulness of several machine learning techniques – reinforcement learning and parallel machine learning approaches, to discover measurement schemes, which are significantly better than the ones discovered by standard numerical methods in our previous work. The high-performing quorums of projection operators we have discovered have complex structure and symmetries, which may imply that the optimal solution will possess such symmetries.

Evaluating Machine Learning Approaches for Discovering Optimal Sets of Projection Operators for Quantum State Tomography of Qubit Systems

Department of Mathematical Foundations of Computer Sciences

Cybernetics and Information Technologies

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Finding optimal measurement schemes in quantum state tomography is a fundamental problem in quantum computation. It is known that for non-degenerate operators...

Evaluating Machine Learning Approaches for Discovering Optimal Sets of Projection Operators for Quantum State Tomography of Qubit Systems

Online veröffentlicht: 31. Dez. 2020

Seitenbereich: 61 - 73

Eingereicht: 15. Sept. 2020

Akzeptiert: 04. Nov. 2020

DOI: https://doi.org/10.2478/cait-2020-0061

Schlüsselwörter
Quantum information, optimization problem, Widening, reinforcement learning

© 2020 Violeta N. Ivanova-Rohling et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Evaluating Machine Learning Approaches for Discovering Optimal Sets of Projection Operators for Quantum State Tomography of Qubit Systems

Online veröffentlicht: 31. Dez. 2020

Seitenbereich: 61 - 73

Eingereicht: 15. Sept. 2020

Akzeptiert: 04. Nov. 2020

DOI: https://doi.org/10.2478/cait-2020-0061

SchlüsselwörterQuantum information, optimization problem, Widening, reinforcement learning

© 2020 Violeta N. Ivanova-Rohling et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Quantum information, optimization problem, Widening, reinforcement learning