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This research presents a deterministic algorithm for simulating large open quantum systems by leveraging low-rank propagation, effectively overcoming the quadratic memory bottleneck associated with Hilbert-space dimension growth. By utilizing tridiagonal split-operator steps, the method achieves near-linear scaling in system size while maintaining high fidelity for complex models like driven nitrogen-vacancy-cavity systems. The approach offers a significant speedup over standard libraries such as QuTiP, making the simulation of larger quantum ensembles computationally feasible.
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Low-rank propagation for tridiagonalizable open quantum systems: near-linear scaling with system size

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arXiv:2609.25368v1 Announce Type: new Abstract: The quadratic growth of the density matrix with Hilbert-space dimension D is the central obstacle to simulating large open quantum systems. We introduce a deterministic algorithm that eliminates it for Lindblad dynamics whose Hamiltonian consists of a time-dependent diagonal part plus terms that are tridiagonal after reordering the basis. The state is a low-rank ensemble of vectors, propagated by tridiagonal split-operator steps and ensemble rank truncation of short-time Kraus branches, so that memory and cost per step are linear in D at fixed rank. For a driven nitrogen-vacancy-cavity model, rank 16 reproduces full density-matrix observables to relative error below $10^{-5}$, the method is up to two orders of magnitude faster than QuTiP already at $D \approx 500$, and its runtime scales nearly linearly.

Abstract:The quadratic growth of the density matrix with Hilbert-space dimension D is the central obstacle to simulating large open quantum systems. We introduce a deterministic algorithm that eliminates it for Lindblad dynamics whose Hamiltonian consists of a time-dependent diagonal part plus terms that are tridiagonal after reordering the basis. The state is a low-rank ensemble of vectors, propagated by tridiagonal split-operator steps and ensemble rank truncation of short-time Kraus branches, so that memory and cost per step are linear in D at fixed rank. For a driven nitrogen-vacancy-cavity model, rank 16 reproduces full density-matrix observables to relative error below $10^{-5}$, the method is up to two orders of magnitude faster than QuTiP already at $D \approx 500$, and its runtime scales nearly linearly.

arXiv:2609.25368 [quant-ph]

(or arXiv:2609.25368v1 [quant-ph] for this version)

https://doi.org/10.48550/arXiv.2609.25368 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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