Reconstructing quantum states with quantum reservoir networks
arXiv:2008.06378 · doi:10.1109/TNNLS.2020.3009716
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.
References in corpus (7)
- Scalable multi-particle entanglement of trapped ions
- Efficient quantum state tomography
- Experimental entanglement of six photons in graph states
- Quantum-enhanced machine learning
- Self-guided quantum tomography
- Quantum pattern recognition with liquid-state nuclear magnetic resonance
- A comparative study of estimation methods in quantum tomography