Quantum reservoir computing with Susanne Yelin

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 opticsTransition to quantum machine learning and quantum computing applicationsQuantum Machine Learning ChallengesLimited to simulating small systems (6-10 qubits) due to lack of working quantum computersBarren plateau problem: the more quantum and entangled the system, the worse the problemMoved towards analog systems and away from universal quantum computersQuantum Reservoir ComputingSubclass of recurrent neural networks where connections between nodes are fixedLearning occurs through a filter function on the outputsSuitable for analog quantum systems like ensembles of atoms with interactionsAdvantages: redundancy in learning, quantum effects (interference, non-commuting bases, true randomness)Potential for fault tolerance and automatic error correctionQuantum Chemistry ApplicationGoal: leverage classical chemistry knowledge and identify problems hard for classical computersCollaboration with quantum chemists Anna Krylov (USC) and Martin Head-Gordon (UC Berkeley)Focused on effective input-output between classical and quantum computersSimulating a biochemical catalyst molecule with high spin correlation using a combination of analog time evolution and logical gatesDemonstrating higher fidelity simulation at low energy scales compared to classical methodsFuture DirectionsExploring fault-tolerant and robust approaches as an alternative to full error correctionOptimizing pulses tailored for specific quantum chemistry calculationsInvestigating dynamics of chemical reactionsCalculating potential energy surfaces for moleculesImplementing multi-qubit analog ideas on the Rydberg atom array machine at HarvardDr. 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.