Presents structure-preserving quantum method-of-lines algorithms for parabolic and hyperbolic PDEs with mixed BCs, using Coons interpolation, similarity transforms, and explicit quantum circuit constructions with complexity and error bounds.
Quantum Koopman Algorithms
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abstract
We define an observable-space framework of Quantum Koopman Algorithms (QKAs) for simulating the dynamics of both linear quantum and nonlinear classical systems, based on approximately closed sets of observables and efficient coherent encodings of their Koopman-driven evolution. QKAs have two strands: Dynamic-QKA for the initial-value problem of observables dynamics, and Spectral-QKA for the eigenvalue analysis of the Koopman operator. We demonstrate the scope of the framework through several applications. First, for classes of $N$ free fermions linearly coupled to a bath, we construct quantum algorithms with gate cost $O(\mathrm{polylog}(N))$, an exponential improvement over classical methods, and use them to reconstruct heat flows and decay rates. Second, for nonlinear classical dynamics, we introduce a novel nonlinear interaction-picture quantum algorithm that enables perturbative expansions around solvable nonlinear reference flows, going beyond existing approaches that only apply to weakly nonlinear systems. Third, we develop spectral methods for extracting eigen-frequencies of late-time nonlinear dynamics, introducing a windowed quantum ODE-solver. Our results identify the Koopman-quantum interface as a natural setting in which quantum algorithms can exploit observable-space structure to simulate both classical and quantum dynamics.
fields
quant-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Structure-Preserving Quantum Method of Lines for Evolutionary PDEs with Mixed Boundary Conditions
Presents structure-preserving quantum method-of-lines algorithms for parabolic and hyperbolic PDEs with mixed BCs, using Coons interpolation, similarity transforms, and explicit quantum circuit constructions with complexity and error bounds.