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.