Neuromorphic Photonic Computing using Frequency Multiplexing
Artur Izmaylov | University of Toronto
Quantum chemistry is a prime target for demonstrating quantum advantage. Focusing on strongly correlated systems, I will discuss new algebraic techniques that complement quantum algorithms for the electronic-structure problem. Efficiently encoding the second-quantized Hamiltonian on quantum hardware is nontrivial and can become a bottleneck.
I will show how partitioning the Hamiltonian into fragments diagonalizable by rotations from small Lie groups or the Clifford group facilitates compilation and measurement. These algebraically tractable fragments enable tighter circuit synthesis, reduced measurement overhead, and cleaner error-mitigation pathways. I will illustrate how exploiting Hamiltonian algebraic structure improves the performance of several quantum algorithms across the pre- and post-error correction regimes.
About the speaker
Artur F. Izmaylov is a Professor in the Departments of Chemistry and Physical & Environmental Sciences at the University of Toronto. He received his Ph.D. from Rice University (2008) and completed postdoctoral research at Yale University (2008–2012).
His group develops quantum computing algorithms for quantum many-body problems that are challenging for classical methods, including (1) determining electronic states and properties of strongly correlated molecules and solids, (2) predicting molecular dynamics and vibrational spectra under strong anharmonicity and nonadiabaticity, and (3) modelling non-equilibrium stationary states of open quantum systems.
Notable contributions include efficient state-preparation techniques—such as qubit-coupled-cluster (QCC) theory—and advances in quantum measurements, recognized by the Google Quantum Research Award (2019) and several NSERC partnership grants. Current efforts focus on error-corrected quantum algorithms and quantum-inspired techniques for classical computation.
Location
QNC 0101, online