Shadow Hamiltonian Simulation
Rolando Somma, Google Quantum AI
Shadow Hamiltonian simulation is a framework for simulating quantum dynamics using a compressed quantum state, which we call the “shadow state,” whose amplitudes are proportional to the expectations of certain operators of interest. The shadow state evolves according to its own Schrödinger equation that can be simulated on a quantum computer efficiently. Shadow Hamiltonian simulation enables the efficient solution to many problems in quantum simulation including simulating dynamics of exponentially large quantum systems of free fermions or bosons, and simulating the dynamics of exponentially large systems of classical oscillators. In this presentation, I will introduce the shadow Hamiltonian simulation framework, highlight its key advantages over traditional quantum simulation methods, and also explore applications and quantum advantage in practical quantum simulation problems.
About the speaker
Dr. Rolando D. Somma is a theoretical physicist and research scientist at Google Quantum AI, recognized for his pioneering contributions to quantum computing and quantum information theory. Before joining Google, he built a distinguished career as a Staff Scientist at Los Alamos National Laboratory, where he led the quantum computing team (2010-2022). Somma's current research focuses on designing advanced quantum algorithms, particularly for quantum simulation and linear algebra, that achieve significant quantum speedups and lower computational complexity than other traditional methods. In recognition of his scientific contributions, Somma was elected a Fellow of the American Physical Society (APS), Division of Quantum Information, in 2022.
Location
QNC 0101
Light refreshments will be provided.
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