Speaker
Grecia Castelazo
(UCSB)
Description
In this work, we explore the potential of existing quantum systems such as cold atoms in optical
cavities or quantum dots to perform complex computational tasks through the Quantum Reservoir
Computing (QRC) paradigm under some of the key constraints of the Noisy Intermediate Scale
Quantum (NISQ) era, such as: moderate hardware size, and non-negligible levels of noise. QRC
is a relatively novel machine learning framework and a candidate for showing quantum advantage
with respect to its classical counterparts; and at the same time is known to circumvent the cost and
training bottlenecks of conventional QML based on variational quantum circuits.