Reconstructing Quantum States With Quantum Reservoir Networks
| Autor: | Tomasz Paterek, Timothy Chi Hin Liew, Michał Matuszewski, Andrzej Opala, Sanjib Ghosh |
|---|---|
| Přispěvatelé: | School of Physical and Mathematical Sciences, Institute of Theoretical Physics and Astrophysics, University of Gda´nsk |
| Rok vydání: | 2020 |
| Předmět: |
Density matrix
Computer Networks and Communications Computer science FOS: Physical sciences 02 engineering and technology Physics [Science] Artificial Intelligence Quantum state 0202 electrical engineering electronic engineering information engineering Statistical physics Tomography Quantum Artificial Neural Networks Machine Intelligence Quantum computer Quantum Physics Reservoir computing Disordered Systems and Neural Networks (cond-mat.dis-nn) Quantum tomography Condensed Matter - Disordered Systems and Neural Networks Computer Science Applications Quantum technology Quantum neural network Quantum Computing 020201 artificial intelligence & image processing Quantum Physics (quant-ph) Software |
| Zdroj: | IEEE transactions on neural networks and learning systems. 32(7) |
| ISSN: | 2162-2388 |
| Popis: | 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. Ministry of Education (MOE) This work was supported in part by the Singapore Ministry of Education Academic Research Fund Tier 2 under Project MOE2015- T2-2-034, Project MOE2017-T2-1-001, and Project MOE2019-T2-1-004. The work of Andrzej Opala and Michał Matuszewski was supported by the National Science Center, Poland, under Grant 2016/22/E/ST3/00045. |
| Databáze: | OpenAIRE |
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