• Quantum reservoir computing with Susanne Yelin

  • Aug 15 2024
  • Length: 26 mins
  • Podcast

Quantum reservoir computing with Susanne Yelin

  • Summary

  • Sebastian is joined by Susanne Yelin, Professor of Physics in Residence at Harvard University and the University of Connecticut.
    Susanne's Background:

    • Fellow at the American Physical Society and Optica (formerly the American Optics Society)
    • Background in theoretical AMO (Atomic, Molecular, and Optical) physics and quantum optics
    • Transition to quantum machine learning and quantum computing applications

    Quantum Machine Learning Challenges

    • Limited to simulating small systems (6-10 qubits) due to lack of working quantum computers
    • Barren plateau problem: the more quantum and entangled the system, the worse the problem
    • Moved towards analog systems and away from universal quantum computers

    Quantum Reservoir Computing

    • Subclass of recurrent neural networks where connections between nodes are fixed
    • Learning occurs through a filter function on the outputs
    • Suitable for analog quantum systems like ensembles of atoms with interactions
    • Advantages: redundancy in learning, quantum effects (interference, non-commuting bases, true randomness)
    • Potential for fault tolerance and automatic error correction

    Quantum Chemistry Application

    • Goal: leverage classical chemistry knowledge and identify problems hard for classical computers
    • Collaboration with quantum chemists Anna Krylov (USC) and Martin Head-Gordon (UC Berkeley)
    • Focused on effective input-output between classical and quantum computers
    • Simulating a biochemical catalyst molecule with high spin correlation using a combination of analog time evolution and logical gates
    • Demonstrating higher fidelity simulation at low energy scales compared to classical methods

    Future Directions

    • Exploring fault-tolerant and robust approaches as an alternative to full error correction
    • Optimizing pulses tailored for specific quantum chemistry calculations
    • Investigating dynamics of chemical reactions
    • Calculating potential energy surfaces for molecules
    • Implementing multi-qubit analog ideas on the Rydberg atom array machine at Harvard
    • Dr. Yelin's work combines the strengths of analog quantum systems and avoids some limitations of purely digital approaches, aiming to advance quantum chemistry simulations beyond current classical capabilities.
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