# Quantum Reservoir Computing: A Reservoir Approach Toward Quantum Machine Learning on Near-Term Quantum Devices

@article{Fujii2020QuantumRC, title={Quantum Reservoir Computing: A Reservoir Approach Toward Quantum Machine Learning on Near-Term Quantum Devices}, author={Keisuke Fujii and Kohei Nakajima}, journal={arXiv: Quantum Physics}, year={2020} }

Quantum systems have an exponentially large degree of freedom in the number of particles and hence provide a rich dynamics that could not be simulated on conventional computers. Quantum reservoir computing is an approach to use such a complex and rich dynamics on the quantum systems as it is for temporal machine learning. In this chapter, we explain quantum reservoir computing and related approaches, quantum extreme learning machine and quantum circuit learning, starting from a pedagogical…

## 8 Citations

Nonlinear input transformations are ubiquitous in quantum reservoir computing

- Physics
- 2021

The nascent computational paradigm of quantum reservoir computing presents an attractive use of near-term, noisy-intermediate-scale quantum processors. To understand the potential power and use cases…

Analytical evidence of nonlinearity in qubits and continuous-variable quantum reservoir computing

- PhysicsJournal of Physics: Complexity
- 2021

The natural dynamics of complex networks can be harnessed for information processing purposes. A paradigmatic example are artificial neural networks used for machine learning. In this context,…

Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spaces.

- Medicine, PhysicsPhysical review letters
- 2021

This work proves that the machine learning models induced from the quantum-enhanced feature space are universal approximators of continuous functions under typical quantum feature maps, and enables an important theoretical analysis to ensure that machine learning algorithms based on quantum feature Maps can handle a broad class of machine learning tasks.

Fock State-enhanced Expressivity of Quantum Machine Learning Models

- Physics, Mathematics2021 Conference on Lasers and Electro-Optics (CLEO)
- 2021

We propose quantum classifiers based on encoding classical data onto Fock states using tunable beam-splitter meshes, similar to the boson sampling architecture. We show that higher photon numbers…

The Reservoir Learning Power across Quantum Many-Boby Localization Transition

- Physics
- 2021

Wei Xia,1 Jie Zou,1 Xingze Qiu,1, 2, ∗ and Xiaopeng Li1, 3, † 1State Key Laboratory of Surface Physics, Institute of Nanoelectronics and Quantum Computing, and Department of Physics, Fudan…

Two-dimensional convective boundary layer: Numerical analysis and echo state network model

- Physics
- 2021

The numerical study of global atmospheric circulation processes requires the parametrization of turbulent buoyancy fluxes in the lower convective boundary layer which typically cannot be resolved by…

Learning Temporal Quantum Tomography

- Medicine, Computer SciencePhysical review letters
- 2021

This work develops a practical and approximate tomography method using a recurrent machine learning framework for near-term quantum devices with temporal processing, and demonstrates the algorithms for representative quantum learning tasks, followed by the proposal of a quantum memory capacity to evaluate the temporal processing ability of near- term quantum devices.

Quantum reservoir computing using arrays of Rydberg atoms

- Physics
- 2021

Quantum computing promises to provide machine learning with computational advantages. However, noisy intermediate-scale quantum (NISQ) devices pose engineering challenges to realizing quantum machine…

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