Quantum reservoir computing with Susanne Yelin

The New Quantum Era

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.