We Are Hiring!
We are seeking motivated PhD or postdoctoral researchers to join our team in the exciting field of quantum reservoir computing. Applicants can focus on numerical, theoretical, or experimental aspects of the research. To apply, send your CV to Dr. Bojan Žunkovič with the subject line "QRC position".
Practical quantum advantage of reservoir computing on NISQ devices
Description
This project explores the intersection of quantum computing and reservoir computing (RC) to address key challenges in energy efficiency and the simulation of many-body quantum systems. The rapid growth in data and machine learning model complexity has increased the demand for more efficient computational methods. Quantum computing, particularly through noisy intermediate-scale quantum (NISQ) devices, offers the potential for practical energy efficiency benefits, even without the promise of exponential speedups. Quantum machine learning is seen as a promising route to realize these benefits.
The project focuses on two main objectives:
1) Quantum Reservoir Computing (QRC): Investigating the potential of quantum reservoir computing to achieve practical quantum advantages in terms of energy efficiency and computational performance. This involves conducting proof-of-principle experiments in cold atom and muon systems to explore how quantum resources can enhance reservoir computing's capabilities.
2) Classical Reservoir Computing for Many-Body Quantum Systems: Applying classical reservoir computing to overcome the entanglement barrier in simulating the long-time evolution of many-body quantum systems. By leveraging the simplicity of RC's linear optimization, the project aims to improve the description of quantum dynamics, particularly where traditional neural network approaches face challenges.
The outcomes of this research will contribute to the understanding of quantum reservoir computing, both in its quantum and classical forms, and its potential to provide tangible advantages in quantum simulation and machine learning tasks.