Qubit Field Labs builds advanced AI models and developer tools, leveraging selective quantum-assisted computation to push the boundaries of reasoning, science, and code. Our models run on classical infrastructure; quantum methods augment training and inference where they provide a measurable advantage.
Our models are trained on classical GPU clusters with selective quantum-assisted optimization for specific scientific and reasoning tasks. Both are available via API and through the Field Harness.
Dense transformer with Mixture-of-Experts routing. Trained on 8.5T tokens of code, mathematics, and scientific text. State-of-the-art on coding benchmarks and multi-step reasoning tasks. Ideal for developers, researchers, and engineers.
Specialized foundation model for chemistry, drug discovery, and materials design. Pre-trained on 4.2B compounds and 2.1M crystal structures. Quantum-assisted fine-tuning improves conformational sampling and reaction pathway prediction.
QF Reasoner 32B produces production-ready code with docstrings, type hints, and unit tests. It leverages its 128K context to reference repository conventions.
A terminal-native coding harness optimized for QF models. Select your model, load repository context, execute tools, and get test feedback — all without leaving the command line.
Field Harness connects directly to your repository, indexes your codebase, and gives your team a shared model context. No more copying snippets into a chat UI.
Model computation flows through classical attention layers while quantum-assisted optimization iteratively refines parameter updates. Move your pointer to influence the optimization landscape.
Adjust two qubit angles and observe the resulting measurement probabilities. This is a simplified model of how quantum-assisted optimization selects candidate parameter updates.
We run targeted research programs where quantum methods can improve AI training, scientific reasoning, and model efficiency. All results are published and open.
Using variational quantum circuits to identify promising parameter update directions during training, reducing the number of classical gradient steps. Demonstrated 2.3× reduction in training steps for chemical foundation models.
Hybrid models that combine classical transformers with quantum kernels for molecular property prediction, reaction pathway search, and crystal structure generation. Published at NeurIPS 2024 and ICLR 2025.
Co-training framework where a classical model and a quantum model share representations through a learned projection. Achieves lower loss on scientific benchmarks with 40% fewer parameters.
All figures are fictional and represent internal evaluations on our test suites. They are not claims of real-world performance.
Our platform spans AI models, developer tools, classical accelerators, and selectively used cryogenic quantum processors.
QF Reasoner 32B, QF Matter 14B, and fine-tuned variants
CoreDeveloper CLI with model-aware code generation and testing
ProductNVIDIA H100 & A100 clusters for training and inference
WorkhorseSelective quantum-assisted optimization (16–64 qubits)
SpecializedCore contributors and scientific advisors.
We're looking for people who build at the intersection of AI and quantum.