Reconstructing quantum states with quantum reservoir networks

Abstract

Reconstructing quantum states is an important task for various emerging quantum technologies. The process of reconstructing the density matrix of a quantum state is known as quantum state tomography. Conventionally, tomography of arbitrary quantum states is challenging as the paradigm of efficient protocols has remained in applying specific techniques for different types of quantum states. Here we introduce a quantum state tomography platform based on the framework of reservoir computing. It forms a quantum neural network, and operates as a comprehensive device for reconstructing an arbitrary quantum state (finite dimensional or continuous variable). This is achieved with only measuring the average occupation numbers in a single physical setup, without the need of any knowledge of optimum measurement basis or correlation measurements.

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• Pitfalls when tackling the exponential concentration of parameterized quantum models
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• Polaritonic Machine Learning for Graph-based Data Analysis
• Pulsed Quantum Excitation
• Connection between memory performance and optical absorption in quantum reservoir computing