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Deep quantum neural networks on a superconducting processor.

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Researchers demonstrated training deep quantum neural networks using backpropagation on a superconducting processor. This quantum machine learning advance efficiently learns quantum channels and molecular energies, guiding future quantum device applications.

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Area of Science:

  • Quantum Computing
  • Machine Learning
  • Quantum Machine Learning

Background:

  • Deep learning and quantum computing have seen significant recent advancements.
  • The intersection of these fields creates the emerging area of quantum machine learning (QML).

Purpose of the Study:

  • To experimentally demonstrate the training of deep quantum neural networks (DQNNs) using the backpropagation algorithm.
  • To assess the efficiency and fidelity of DQNNs in learning quantum tasks.

Main Methods:

  • Utilized a six-qubit programmable superconducting processor for experimental demonstration.
  • Performed the forward pass of backpropagation experimentally.
  • Classically simulated the backward pass of the backpropagation algorithm.

Main Results:

  • Successfully trained three-layer DQNNs to learn two-qubit quantum channels with up to 96.0% mean fidelity.
  • Achieved 93.3% accuracy in learning the ground state energy of molecular hydrogen.
  • Trained six-layer DQNNs for single-qubit quantum channels with up to 94.8% mean fidelity.

Conclusions:

  • The number of coherent qubits needed does not increase with DQNN depth.
  • This finding offers valuable guidance for QML applications on current and future quantum hardware.