SINDy-Inspired Data-Driven Sparse Identification of Quantum Hamiltonian Dynamics Using Quantum Circuit Learning
Student Contest:
Yes
Affiliation Type:
Academia
Keywords:
Nonlinear dynamics, Quantum computing
Abstract:
Sparse identification of nonlinear dynamics (SINDy) is a data-driven methodology for reconstructing the governing equations of classical nonlinear systems using time-series data. It formulates system dynamics as a sparse linear combination of basis functions, with coefficients learned directly from observed time-series data. Motivated by the principles of SINDy, we introduce sparse identification of quantum Hamiltonian dynamics (SIQHDy), a quantum circuit learning approach for discovering quantum Hamiltonian dynamics from time-series measurement data. SIQHDy models quantum dynamics as a structured composition of parameterized quantum circuits and estimates a product of basis quantum circuits, with parameters inferred in a sparse manner from quantum measurement data. Through numerical simulations, we demonstrate that SIQHDy accurately captures the quantum dynamics of systems with three qubits.
Track ID:
5.1
Track Name:
Design and Analysis of Nonlinear Dynamics for Computing
