Artificial Intelligence and Quantum Information for Quantum Neural Networks.  Interesting research is being conducted which pulls together quantum information technologies and artificial neural networks. The two together have provided promising advances:

  • Enhanced machine-learning efficiency
  • Speed-up of classical tasks
  • Solving complex quantum problems (such as the quantum many-body problem)
  • Automating the control, and design, of quantum experiments

Qubit

npj | Quantum Information:

Quantum neural networks are emerging technologies that combine the features of artificial neural networks and quantum information technologies.  While neural networks are biologically inspired computing systems that learn from example to perform complex tasks in the area of “big data” and machine learning, quantum information technologies exploit quantum effects for practical applications like quantum computation, quantum cryptography, and long-distance quantum communications. 

The interaction between these two promising fields led to many advances. 

For instance, quantum effects in neural networks enhance learning efficiency and speed up solving many classical tasks.  Conversely, neural networks are used for solving complex quantum problems, the control, and design of quantum experiments, and considered as architectures, given a universal quantum computer or quantum annealer.

“Schematic representation of a quantum reservoir processor. A quantum state in the form of an optical field excites a fermionic lattice with random couplings Jij in an effective Fermi–Hubbard model. The occupation numbers of the fermionic sites are extracted and combined to give a final output. This generic architecture can perform various tasks, such as identifying separability of a quantum state and simultaneously estimating its various properties.”

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Source:  npj Quantum Information.  Sanjib Ghosh, Andrzej Opala, Michał Matuszewski, Tomasz Paterek & Timothy C. H. Liew, Quantum reservoir processing…