Coincidence-based QELMs outperform intensity-based ones on a 0/1 MNIST task, and simulations predict that indistinguishable photons give a growing expressivity advantage as photon number increases, though the 2-photon experiment shows no accuracy advantage.
Harnessing disordered quantum dynamics for machine learning
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abstract
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.
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Harnessing Photon Indistinguishability in Quantum Extreme Learning Machines
Coincidence-based QELMs outperform intensity-based ones on a 0/1 MNIST task, and simulations predict that indistinguishable photons give a growing expressivity advantage as photon number increases, though the 2-photon experiment shows no accuracy advantage.