Quantum Ensemble Classification: A Sampling-based Learning Control Approach

Abstract

Quantum ensemble classification has significant applications in discrimination of atoms (or molecules), separation of isotopic molecules and quantum information extraction. However, quantum mechanics forbids deterministic discrimination among nonorthogonal states. The classification of inhomogeneous quantum ensembles is very challenging since there exist variations in the parameters characterizing the members within different classes. In this paper, we recast quantum ensemble classification as a supervised quantum learning problem. A systematic classification methodology is presented by using a sampling-based learning control (SLC) approach for quantum discrimination. The classification task is accomplished via simultaneously steering members belonging to different classes to their corresponding target states (e.g., mutually orthogonal states). Firstly a new discrimination method is proposed for two similar quantum systems. Then an SLC method is presented for quantum ensemble classification. Numerical results demonstrate the effectiveness of the proposed approach for the binary classification of two-level quantum ensembles and the multiclass classification of multilevel quantum ensembles.

32 pages, 15 figures, 2 tables

References in corpus (7)

• Modeling and Control of Quantum Systems: An Introduction
• Fidelity-based Probabilistic Q-learning for Control of Quantum Systems
• Sampling-based Learning Control for Quantum Systems with Uncertainties
• Classification of quantum phases and topology of logical operators in an exactly solved model of quantum codes
• Quantum learning: optimal classification of qubit states
• Quantum Pareto Optimal Control
• Sampling-based Learning Control for Quantum Systems with Hamiltonian Uncertainties

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• Robust Learning Control Design for Quantum Unitary Transformations
• Quantum Language Model with Entanglement Embedding for Question Answering
• Learning control of quantum systems using frequency-domain optimization algorithms
• An agnostic-Dolinar receiver for coherent states classification
• Robust controllability of two-qubit Hamiltonian dynamics