Quantum reservoir processing

Abstract

The concurrent rise of artificial intelligence and quantum information poses opportunity for creating interdisciplinary technologies like quantum neural networks. Quantum reservoir processing, introduced here, is a platform for quantum information processing developed on the principle of reservoir computing that is a form of artificial neural network. A quantum reservoir processor can perform qualitative tasks like recognizing quantum states that are entangled as well as quantitative tasks like estimating a non-linear function of an input quantum state (e.g. entropy, purity or logarithmic negativity). In this way experimental schemes that require measurements of multiple observables can be simplified to measurement of one observable on a trained quantum reservoir processor.

Quantum Neuromorphic Platform for Quantum State Preparation

“…While the applications of reservoir computing largely focused on classical tasks (even with quantum reservoirs [16]), the idea was recently brought fully into the quantum world in the form of quantum reservoir processing [17]. It was shown as an efficient platform for quantum entanglement recognizing tasks and for performing complex quantum measurements (e.g., entropy, purity, and negativity).…”

Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

“…The dynamical evolution of the density matrix of the coupled quantum substrate and the quantum input modes enables different tasks. [ 46,50 ] For instance, quantum input states can be classified when encoded into an ancilla interacting with a fermionic network. [ 46 ] Further, the input quantum state, either in finite dimension or in continuous variable, can be reconstructed.…”

“…[ 46,50 ] For instance, quantum input states can be classified when encoded into an ancilla interacting with a fermionic network. [ 46 ] Further, the input quantum state, either in finite dimension or in continuous variable, can be reconstructed. [ 50 ] Recently, a quantum input has been also considered in classical RC.…”

“…In particular, the spin‐based implementation is the most analyzed platform for QRC at the moment, [ 34–42 ] with the continuous‐variable systems becoming another promising option. [ 43–45 ] In the context of QELM, both fermionic and bosonic setups have been proposed for instance in entanglement detection [ 46 ] or light field phase estimation. [ 47 ] Inspired by these neuromorphic approaches other quantum tasks have been reported such as quantum states preparation [ 48,49 ] or reconstruction.…”

Realising and Compressing Quantum Circuits With Quantum Reservoir Computing

“…As it is easier to engineer a fixed and random network than a well controlled one, reservoir computing has been successfully implemented in a variety of physical systems [20][21][22][23]. Recently, the reservoir computing concept was brought to the quantum domain [24], using networks of quantum nodes [25,26] and the performance of specific non-classical tasks [27] including quantum state preparation [28] and tomography [29]. While these examples operate with quantum systems, they work with classical data either in the input or output and are far from being quantum computers, which should be able to implement unitary transformations (at least approximately) of a quantum state.…”