Quantum Neuromorphic Computing with Reservoir Computing Networks

Abstract

Quantum reservoir networks combine the intelligence of neural networks with the potential of quantum computing in a single platform. This platform operates on the architecture of reservoir computing, which can function even with random connections between neural nodes. This is a major advantage for hardware implementation. Herein is described how reservoir computing is brought into the quantum domain to perform various tasks, including characterization of quantum states, quantum estimation, quantum state preparation, and quantum computing. It shows quantum enhancement in classical data processing, and creates the opportunity for quantum information processing within the robust paradigm of neural networks. It is friendly for implementation in a wide range of physical systems, including quantum dots, superconductors, trapped ions, cold atoms, and exciton‐polaritons.

Performance of reservoir computing in a random network of single-walled carbon nanotubes complexed with polyoxometalate

“…Many types of PRCs have recently been proposed. Their physical resources are widely diverse, varying from photonics [6] and spintronics [7], nanomaterials, [8] quantum [9,10], and solid-state devices [11] to mechanics [12] and biological materials [13]. Among the others, nanomaterials have the ability to form complex networks and are expected to realize highly integrated RC chips.…”

Microcavity exciton polaritons at room temperature

“…In contrast, a quantum reservoir computer, which is a form of quantum neural network, can operate even with uncontrolled connections [389] . This is a huge advantage for physical implementation [390,391] . Here, exciton polaritons can be an excellent platform to realize a quantum reservoir computer as a hardware.…”

Realizing quantum convolutional neural networks on a superconducting quantum processor to recognize quantum phases

“…Quantum computers are beginning to reach a level at which their output states are too complex to be analyzed by classical means 1 , suggesting that machine learning techniques which directly process quantum data are expected to become an increasingly important tool to efficiently characterize and benchmark quantum hardware. Examples of specific applications thereof include the principal component analysis of density matrices 21 , quantum autoencoders 22 – 24 , the certification of Hamiltonian dynamics 25 , 26 , and the detection of entanglement correlations in quantum many-body states 10 , 11 , 27 – 29 .…”