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Neuromorphic quantum computing.

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Neuromorphic computing can perform quantum operations by connecting spiking neurons to Ising spins. This approach enables learning quantum gates within neural network dynamics, advancing correlated computing.

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Area of Science:

  • Quantum Computing
  • Artificial Intelligence
  • Neuromorphic Computing

Background:

  • Correlations are fundamental to quantum computation.
  • Correlations may also be key for artificial intelligence, neuromorphic computing, and biological computing.
  • Exploring 'correlated computing' requires new frameworks.

Purpose of the Study:

  • To demonstrate that neuromorphic computing can perform quantum operations.
  • To explore the potential of correlated computing by linking neural networks and quantum systems.
  • To develop a novel probabilistic computing approach beyond traditional Markov chains.

Main Methods:

  • Representing spiking neuron states (active/silent) as Ising spin states (two states).
  • Constructing a quantum density matrix from Ising spin expectation values and correlations.
  • Learning quantum gates by adjusting neural network parameters while preserving quantum correlations.

Main Results:

  • Demonstrated that neuromorphic systems can execute quantum operations.
  • Showcased the ability to learn quantum gates in a two-qubit system through neural network parameter changes.
  • Established that learned parameter changes adhere to constraints ensuring quantum correlations.

Conclusions:

  • Neuromorphic computing offers a viable platform for performing quantum operations.
  • The proposed probabilistic computing model transcends Markov chains, utilizing classical probability distribution constraints akin to quantum entanglement.
  • This work provides a foundational step towards systematic exploration of correlated computing.