SymBasis.jl
Exact diagonalization and related methods scale poorly with system size unless symmetries are exploited to shrink the effective Hilbert space. SymBasis.jl automates the construction of symmetry-resolved bases for systems with a discrete number of degrees of freedom, so that step doesn’t have to be rebuilt by hand for every new model.
Useful for: researchers and practitioners working on exact diagonalization or generally needing symmetry-adapted basis construction in condensed matter and quantum many-body physics.
Demonstrates: Julia package design, performance-aware combinatorics and linear algebra, testing, and open-source maintenance.
Why it matters: the same reduction pattern — using symmetry to shrink a Hilbert space to a workable one — shows up throughout quantum simulation and quantum computing, not just in this one project.
I work on the numerical simulation of quantum many-body systems — nonequilibrium dynamics, quantum chaos, and disordered systems — using methods from exact diagonalization to neural-network quantum states. Alongside the physics, I write and maintain open-source scientific software in Julia and Python. I’m a Ph.D. candidate at the University of Göttingen.
In practice, that means building and validating exact-diagonalization codes, constructing symmetry-resolved Hilbert spaces, implementing iterative eigensolvers, and exploring neural-network quantum states as an alternative to standard variational methods — with benchmarking and reproducibility built into the process rather than treated as an afterthought.
I’m increasingly interested in applying this background to problems in quantum simulation, quantum algorithms, and scientific computing more broadly — areas where physics-informed modeling and larger-scale computation meet.
Outside of physics, I spend time with photography, travel, and cinema — particularly auteur and festival films — and I play piano, strictly as an amateur.