Quantum reservoir computation utilising scale-free networks

Abstract

Today's quantum processors composed of fifty or more qubits have allowed us to enter a computational era where the output results are not easily simulatable on the world's biggest supercomputers. What we have not seen yet, however, is whether or not such quantum complexity can be ever useful for any practical applications. A fundamental question behind this lies in the non-trivial relation between the complexity and its computational power. If we find a clue for how and what quantum complexity could boost the computational power, we might be able to directly utilize the quantum complexity to design quantum computation even with the presence of noise and errors. In this work we introduce a new reservoir computational model for pattern recognition showing a quantum advantage utilizing scale-free networks. This new scheme allows us to utilize the complexity inherent in the scale-free networks, meaning we do not require programing nor optimization of the quantum layer even for other computational tasks. The simplicity in our approach illustrates the computational power in quantum complexity as well as provide new applications for such processors.

arXiv comment8 pages, 5 figures

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• Feedback-driven quantum reservoir computing for time-series analysis
• On fundamental aspects of quantum extreme learning machines
• Reduced-order modeling of two-dimensional turbulent Rayleigh-Bénard flow by hybrid quantum-classical reservoir computing
• Harnessing Quantum Extreme Learning Machines for image classification
• Role of coherence in many-body Quantum Reservoir Computing
• Impact of the form of weighted networks on the quantum extreme reservoir computation
• Non-Markovian to Markovian decay in structured environments with correlated disorder
• Quantum reservoir computing on random regular graphs
• Entanglement estimation of Werner states with a quantum extreme learning machine
• Simple Hamiltonian dynamics is a powerful quantum processing resource
• Time crystal embodies chimeralike state in periodically driven quantum spin system
• Quantum Transport Reservoir Computing
• Edge of Many-Body Quantum Chaos in Quantum Reservoir Computing
• Neural networks with quantum states of light