Floquet Engineering with Particle Swarm Optimization: Maximizing Topological Invariants
Shikun Zhang, Jiangbin Gong

TL;DR
This paper demonstrates how particle swarm optimization can effectively maximize topological invariants in Floquet systems, enabling the design of exotic topological phases with limited resources and stable large invariants.
Contribution
It introduces PSO as a novel optimization method for maximizing topological invariants in Floquet engineering, overcoming limitations of gradient-based approaches.
Findings
PSO effectively maximizes Floquet topological invariants.
Low-frequency driving can increase topological invariants without extra energy.
Maximized invariants correspond with physical phenomena like Thouless pumping.
Abstract
It is of theoretical and experimental interest to engineer topological phases with very large topological invariants via periodic driving. As advocated by this work, such Floquet engineering can be elegantly achieved by the particle swarm optimization (PSO) technique from the swarm intelligence family. With the recognition that conventional gradient-based optimization approaches are not suitable for directly optimizing topological invariants as integers, the highly effective PSO route yields new promises in the search for exotic topological phases, requiring limited physical resource. Our results are especially timely in view of two important insights from literature: low-frequency driving may be beneficial in creating large topological invariants, but an open-ended low-frequency driving often leads to drastic fluctuations in the obtained topological invariants. Indeed, using a simple…
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