Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

Abstract

Quantum reservoir computing and quantum extreme learning machines are two emerging approaches that have demonstrated their potential both in classical and quantum machine learning tasks. They exploit the quantumness of physical systems combined with an easy training strategy, achieving an excellent performance. The increasing interest in these unconventional computing approaches is fueled by the availability of diverse quantum platforms suitable for implementation and the theoretical progresses in the study of complex quantum systems. In this review article, recent proposals and first experiments displaying a broad range of possibilities are reviewed when quantum inputs, quantum physical substrates and quantum tasks are considered. The main focus is the performance of these approaches, on the advantages with respect to classical counterparts and opportunities.

Nonlinear input transformations are ubiquitous in quantum reservoir computing

“…Quantum reservoir computing (QRC) is an emerging area in the field of quantum neuromorphic computing [1,2] that promises the potential application of near-term quantum technology to real-world problems. It generalizes the classical computing paradigm of reservoir computing [3][4][5] to consider quantum systems as the computational resource, or reservoir.…”

Experimental Quantum End-to-End Learning on a Superconducting Processor

“…It should be noted that no quantum advantage is claimed here with a small-size NISQ processor. However, we notice that the end-to-end learning is very similar to quantum reservoir computing (QRC) [44,45] in that both schemes exploit complex natural quantum dynamics for hard computing tasks, and QRC has been proven to have universal approximation property [46] and higher information processing capacity [47]. It is conjectured that similar conclusions can be made for quantum end-to-end learning, and these will be explored in our future studies.…”

Quantum Reservoir Computing for Speckle Disorder Potentials

“…Additionally, many efforts are devoted to developing machine learning algorithms that exploit quantum resources, aiming to find a quantum advantage in performing tasks. Quantum reservoir computing (QRC) and related approaches belong to this last category [22,23].…”

“…In second place, the presence of entanglement can also contribute to achieving a quantum advantage when quantum correlations are exploited [39]. Finally, there exist several proposals suitable to be implemented in a wide variety of experimental platforms to realize not only classical tasks but also quantum ones [22], for instance, entanglement detection [40], quantum state tomography [41], and quantum state preparation [42,43].…”