Configured Quantum Reservoir Computing for Multi-Task Machine Learning

Understand

Amidst the rapid advancements in experimental technology, noise-intermediate-scale quantum (NISQ) devices have become increasingly programmable, offering versatile opportunities to leverage quantum computational advantage.

• Here we explore the intricate dynamics of programmable NISQ devices for quantum reservoir computing.
• Using a genetic algorithm to configure the quantum reservoir dynamics, we systematically enhance the learning performance.
• Remarkably, a single configured quantum reservoir can simultaneously learn multiple tasks, including a synthetic oscillatory network of transcriptional regulators, chaotic motifs in gene regulatory networks, and the fractional-order Chua's circuit.