We train and serve state-of-the-art language models for reasoning and science. Quantum computing accelerates key bottlenecks in training, inference, and molecular simulation — giving our models a structural advantage without replacing the classical stack that does the heavy lifting.
Trained on classical accelerators · Optimized with quantum · Served at scale
Two models designed for distinct domains. Both trained with quantum-assisted optimization on our internal cluster.
A general-purpose reasoning and coding model. Excels at multi-step logic, code generation and debugging, mathematical proof, and structured tool use. Ideal as a backbone for developer-facing applications.
A specialist model for computational chemistry and materials science. Trained on curated scientific corpora and fine-tuned with quantum simulation feedback for molecular property prediction and reaction pathway analysis.
Select a model to view a realistic Field Harness session with that model's strengths.
Classical neural computation identifies optimization targets; quantum sampling explores the solution landscape. Move your cursor across the visualization to see the interplay.
Model gradient flow → quantum-enhanced optimization → updated weights
Adjust θ and φ to rotate each qubit on the Bloch sphere. The measurement probabilities update in real time and sum to 100%.
We focus on where quantum computation measurably improves AI capabilities.
Using variational quantum circuits and quantum approximate optimization (QAOA) to navigate high-dimensional loss landscapes. Targets weight initialization, learning rate schedules, and architecture search — not replacing gradient descent, but seeding it with better starting points.
Training models that can formulate hypotheses, plan experiments, and interpret results in chemistry and materials science. Integrates classical simulation data with quantum chemistry benchmarks for grounded, verifiable scientific outputs.
Co-designing classical and quantum components that execute in the same training loop. Focus on reducing communication overhead, handling qubit decoherence gracefully, and ensuring graceful degradation when quantum resources are unavailable.
A coding harness optimized for QF models. Understands repository context, executes tools, manages multi-file edits, and streams structured feedback — all running against our inference endpoints or your own deployment.
$ pip install qf-harness
Automatically indexes your codebase, dependency graph, and test structure. Passes only relevant context to the model, reducing token waste and improving edit quality.
Runs linters, formatters, tests, and custom scripts within a sandboxed environment. Model sees real output and iterates — no more guessing at compiler errors.
Generates minimal, reviewable diffs rather than rewriting entire files. Integrates with git for atomic commits, branch-aware context, and conflict detection.
Quantum processors are one layer in the stack — used where they help, bypassed where they don't.
Reasoner 32B, Matter 14B — and the developer platform that makes them usable.
vLLM-based serving with speculative decoding, continuous batching, and model-parallel sharding.
H100 / A100 nodes for training and inference. Handles the vast majority of computation.
Selectively invoked for optimization subroutines, molecular simulation, and architecture search.
Our quantum processors operate at 15 mK in dilution refrigerators and are accessed via a cloud API. They are used for approximately 8–12% of total training compute — targeted at stages where quantum sampling provides measurable advantage. When quantum resources are unavailable, the system degrades gracefully to classical-only baselines.
Previously led ML systems at a national lab. PhD in quantum information from MIT. Focused on making quantum optimization practical for large-scale training.
Systems architect with deep experience in GPU clusters and distributed training. Builds the infrastructure that connects classical and quantum compute.
Former core contributor at a major code intelligence platform. Designs the developer experience that makes QF models accessible in real workflows.
We're hiring across research, engineering, and product. Remote-friendly with offices in San Francisco and Tokyo.
Design and optimize quantum-classical training loops. You'll work at the intersection of variational quantum circuits and large-scale model training.
Build the tool execution runtime, repository indexing pipeline, and multi-model orchestration layer for our developer platform.
Advance scientific reasoning capabilities in QF Matter. Develop training recipes, evaluation suites, and benchmark datasets for chemistry and materials science.