Quantum Reservoir Computing for Speckle Disorder Potentials

Abstract

Quantum reservoir computing is a machine learning approach designed to exploit the dynamics of quantum systems with memory to process information. As an advantage, it presents the possibility to benefit from the quantum resources provided by the reservoir combined with a simple and fast training strategy. In this work, this technique is introduced with a quantum reservoir of spins and it is applied to find the ground state energy of an additional quantum system. The quantum reservoir computer is trained with a linear model to predict the lowest energy of a particle in the presence of different speckle disorder potentials. The performance of the task is analyzed with a focus on the observable quantities extracted from the reservoir and it is shown to be enhanced when two-qubit correlations are employed.

Time-series quantum reservoir computing with weak and projective measurements

“…Recently, reservoir computing has been extended to the quantum regime considering several ideal models ranging from qubits to fermions and bosons, in photonic, atomic and solid-state platforms 48,49 . Theoretical proposals display successful performances of quantum reservoir computing (QRC) in genuine temporal tasks [50][51][52][53][54][55] and in generalization and classification ones 49,[56][57][58][59][60][61] , an approach known as extreme learning machine. QRC is indeed a burgeoning alternative approach in quantum machine learning, but continuous monitoring for sequential time series processing poses a major challenge.…”

Tomographic completeness and robustness of quantum reservoir networks

“…Granted that extreme controls over quantum systems are expensive resources, an alternative that relaxes this condition is worth exploring. Inspired by a CNN architecture known as reservoir computing [7,8], where one does not require controls over the network itself, quantum versions have been recently proposed for solving both classical tasks [4], such as time series prediction [9] or pattern prediction [10], and quantum tasks [5], such as quantum state tomography [11][12][13], quantum process tomography [14], quantum state preparation [15,16], quantum operations [17], and quantum metrology [18] (see Ref. [19] for a review).…”

Time Series Quantum Reservoir Computing with Weak and Projective Measurements

“…Recently, reservoir computing has been extended to the quantum regime considering several models ranging from qubits to fermions and bosons, modeling photonic, atomic and solid-state platforms [48,49]. Theoretical proposals display successful performances of quantum reservoir computing (QRC) in genuine temporal tasks [50][51][52][53][54][55] and in generalization and classification ones [49,[56][57][58][59][60][61], an approach known as extreme learning machine. A first experimental implementation of a static classification task has been realized with NMR of a nuclear spin ensemble in a solid [62], while the first exploration of temporal series processing with QRC was recently reported on a quantum digital computer [52].…”