165 citations · 369 across the 14 of their papers we have counts for
7 papers · 1 filter
Measurement-enabled online quantum processing with amplitude encoding
Giacomo Franceschetto, Pere Mujal, Rodrigo Martínez-Peña
We introduce a quantum reservoir computing online protocol that realizes amplitude encoding on quantum hardware. Our scheme combines mid-circuit measurement and reset operations to…
Quantum reservoir computing in atomic lattices
Guillem Llodrà, Pere Mujal, Roberta Zambrini +1
Quantum reservoir computing (QRC) exploits the dynamical properties of quantum systems to perform machine learning tasks. We demonstrate that optimal performance in QRC can be achi…
Harnessing quantum back-action for time-series processing
Giacomo Franceschetto, Marcin Płodzień, Maciej Lewenstein +2
Quantum measurements affect the state of the observed systems via back-action. While projective measurements extract maximal classical information, they drastically alter the syste…
Certifying steady-state properties of open quantum systems
Luke Mortimer, Donato Farina, Grazia Di Bello +4
Estimating the steady-state properties of open many-body quantum systems is a fundamental challenge in quantum science and technologies. In this work, we present a scalable approac…
Time Series Quantum Reservoir Computing with Weak and Projective Measurements
Pere Mujal, Rodrigo Martínez-Peña, Gian Luca Giorgi +2
Quantum machine learning represents a promising avenue for data processing, also for purposes of sequential temporal data analysis, as recently proposed in quantum reservoir comput…
Quantum Reservoir Computing for Speckle-Disorder Potentials
Pere Mujal
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…