SEMIQLASSICAL
Introducing Toymodel
Complex computational workflows start with Toymodel. Toymodel is a human-inspectable computational workbench for industrial optimization, scientific machine learning, and physics-informed neural networks, with a built-in typed language, graph execution compiler, native tensor algebra, SQL-style tabular operations, and kernel-level acceleration.
What is semiqlassical
Semiqlassical builds high‑precision physics ML infrastructure for the regime between classical and quantum simulation.
Physics ML for complex systems
Machine learning for scientific systems where answers must stay numerical, inspectable, and grounded in the physics.
Simulation and generative modeling
Tools that learn from data, simulate scenarios, and surface structure in messy, multi‑scale systems.
Quantum-inspired optimization
Quantum and quantum‑inspired approaches to search, scheduling, and constrained optimization at scale.
Robust, measurable pipelines
End‑to‑end pipelines built with observability, performance, privacy, and safety in mind, for demanding compute and data workloads.
~100× faster tensor algebra
Toymodel is a compiled language and runtime for tensor algebra. Against plain Python, core kernels run roughly two orders of magnitude faster, in the performance class of C, Julia, and Rust.
Solver-native acceleration
The compiler reasons about precision, algebra, and optimization together, so proprietary solver paths outrun general‑purpose runtimes while staying inspectable.
Founders
Two builders lead semiqlassical: a computer scientist working across quantum computation, algorithms, and machine learning, and an engineer working across neuroscience, imaging, and ML systems. A quiet techno‑anarchic bent guides the work. We build where mathematics, compute, and measurement meet, and we hold three commitments for scientific ML systems: they stay understandable, they stay auditable, and they stay broadly useful. The work today covers high‑precision physics ML, simulation, and optimization for complex systems. It rests on three foundations: Monte Carlo methods, Markov‑chain methods classical and quantum, and large‑scale machine learning. A small circle of prominent scientists advises us, reviews our results, and collaborates quietly.
Avah Banerjee, PhD
Avah works across graph algorithms, high‑performance computing, and quantum computation. She has built compilers for quantum circuits, studied quantum walks, and designed resource‑efficient chaos generators. Her current research applies Markov chains and Monte Carlo methods, classical and quantum, to the modeling, the simulation, and the optimization of systems under real‑world constraints. She is former faculty, with a record of funded research and industry collaboration. At semiqlassical she directs the architecture, the numerics, and the mathematics behind our physics‑ML and quantum‑inspired tooling.
Emily Hsiang, PhD
Emily turns noisy signals into decisions. Trained in chemical engineering and neuroscience, she has built advanced optical imaging systems, developed ML pipelines that decode visual pathways, and shipped deep‑learning models that isolate signal from artifact. She works to three measures: operability, latency, and measurement, the three that carry a method from the laboratory into a running system. At semiqlassical she leads the data, the modeling, and the human factors of tools built for high‑stakes, everyday use.
Explore Toymodel
Start with Toymodel.
Toymodel is where the work begins. Open it, build a sector, and read every value on the way through.