Author

Matthew L. Sims-Goh

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Preprint Oct 2026

Breaking the chain: geometry-native state preparation with ASPIRE

Loading data into quantum computers is a key bottleneck that threatens end-to-end quantum advantage. Quantum-circuit compilers for structured states like matrix product states (MPS) are a critical enabler to manage this in quantum chemistry, finance, and many-body quantum simulation. Approximate preparation of MPS typi...

Fredrik Hasselgren, Matthew L. Sims-Goh · 0 citations

Preprint Aug 2026

Quantum-Inspired Computational Fluid Dynamics for Transient Turbulent Compressible Flows

Quantum-inspired algorithms are an emerging class of algorithms for computational fluid dynamics (CFD) with potentially favourable scaling for large problems compared to classical methods. However, their applications have been limited to incompressible flows due to arithmetic limitations, which are addressed in this wo...

S. Lee, M. Kozul, Muhammad Usman et al. · 1 citation

Preprint Sep 2026

Multivariate quantum state preparation with optimized tensor networks

SCENT (Spectral Clustering for Entanglement miNimizing Trees) is a protocol that utilizes efficiently-computable pairwise entanglement metrics to determine a suitable tree tensor network (TTN) structure, which is efficiently approximated using tensor cross-interpolation (TCI) and substantially outperforms MPS methods a...

Matthew L. Sims-Goh, L. Cincio, Annina Z. Lieberherr et al. · 2 citations