Deep Learning with Quantum State Fidelity and Beyond

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Deep Learning with Quantum State Fidelity and Beyond

Remarkable progress has been achieved in deep learning-related systems
and applications. In the post-Moore’s Law era, however, the limit of
semiconductor fabrication technology and the increasing data size have
slowed down the development of learning algorithms. In parallel, the
rapid development of quantum computing has pushed classical system
designs into a new era. We propose novel architectures with
quantum-state-fidelity-based evaluation functions to train deep
learning models. The new designs encode the data with a reduced number
of qubits and generate the quantum circuit, pushing it to the quantum
machines for the best states iteratively. We conduct intensive
experiments on both quantum simulators and the IBM-Q quantum platform.
Additionally, we compare IBM-Q results with IonQ as they are the two
leading qubit implementations for quantum information processing at
scale and have complementary characteristics. Finally, we will discuss
the challenges and opportunities to develop quantum-based systems and
applications further.

Date and Time

  • Date: 12 Oct 2023
  • Time: 11:00 AM to 01:00 PM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
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Join Zoom Meeting


  • 323 Dr Martin Luther King Jr Blvd
  • New Jersey Institute of Technology
  • Newark, New Jersey
  • United States 07102
  • Building: ECEC Building
  • Room Number: 202



  • Starts 30 September 2023 11:57 AM
  • Ends 12 October 2023 11:00 AM
  • All times are (UTC-05:00) Eastern Time (US & Canada)
  • No Admission Charge


Dr. Ying Mao

Dr. Ying Mao of Fordham University



Deep Learning with Quantum State Fidelity and Beyond