Harnessing disordered quantum dynamics for machine learning
This work introduces a new paradigm for quantum information processing that could benefit AI and quantum computing fields by leveraging existing quantum systems.
The authors tackled the challenge of realizing digital quantum computers by proposing quantum reservoir computing, which uses natural quantum dynamics for machine learning, achieving computational capabilities comparable to 500-node recurrent neural networks with just seven qubits.
Quantum computer has an amazing potential of fast information processing. However, realisation of a digital quantum computer is still a challenging problem requiring highly accurate controls and key application strategies. Here we propose a novel platform, quantum reservoir computing, to solve these issues successfully by exploiting natural quantum dynamics, which is ubiquitous in laboratories nowadays, for machine learning. In this framework, nonlinear dynamics including classical chaos can be universally emulated in quantum systems. A number of numerical experiments show that quantum systems consisting of at most seven qubits possess computational capabilities comparable to conventional recurrent neural networks of 500 nodes. This discovery opens up a new paradigm for information processing with artificial intelligence powered by quantum physics.