Krylov Quantum Diagonalization
Diagonalizing large many-body Hamiltonians via shallow Trotter circuits and classical post-processing.
I develop and implement quantum algorithms tailored to compute quantities of interest within the coherence budget of noisy hardware. Topics range from simulations of quantum many-body systems, to high-energy physics phenomena, quantum thermodynamics and quantum optics. Highlights include Krylov-subspace diagonalisation of spin lattices, the first experimental measurement of conformal-field-theory central charge on a universal processor, and early demonstrations that qubit-level matched filtering can recover astrophysical signals at classical signal-to-noise ratios.
Diagonalizing large many-body Hamiltonians via shallow Trotter circuits and classical post-processing.
Reliable algorithmic results on noisy quantum processors require hardware-aware error suppression and mitigation. I build tools for: autonomous calibration of control pulses based on deep-reinforcement-learning and black-box optimization techniques; compiler techniques to reduce crosstalk via dynamical decoupling sequences; qubit selection protocols based on efficient sparse-noise tomography to execute the algorithm on the best performing qubits. These ideas extend into algorithmic space: I've recently started to explore tensor-network-assisted algorithm implementation, like multiproduct formulas, to improve performance without sacrificing circuit depth. Across all projects the aim is the same: suppress dominant errors in situ so mitigation overhead stays manageable.
Compressing time-evolution circuits using multiproduct formulas assisted by tensor networks.
Toolkit for modeling device noise and reconstructing unbiased expectation values using quasi-probability techniques.
Choosing the best qubits using sparse noise tomography or aggregated error metrics.
Mitigating correlated noise at compile time via targeted dynamical decoupling.
Using deep reinforcement learning and black-box optimization to autonomously calibrate high-fidelity gates.
At the heart of many superconducting devices is a tunable qubit-cavity coupling. My doctoral work focused on non-adiabatic effects, the dynamical Lamb and Casimir effects, arising during fast qubit driving. Modeling of superconducting circuits shows that these effects can be used to generate entanglement and perform ultra-fast quantum gates.
Entanglement generation in non-stationary cavity QED via the dynamical Lamb effect.
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