Frontier models,
quantum-refined training.
Qubit Field Labs trains and serves state-of-the-art language models for reasoning, code, and scientific discovery. We build Field Harness, the coding harness that runs our models with full repository context. Selectively, we use cryogenic quantum processors to improve gradient refinement, sampling efficiency, and scientific reasoning — quantum is our accelerator, not our product.
Two frontier models, each purpose-built.
Both models are served through Field Harness with full tool integration. Quantum-assisted optimization improves training convergence; inference runs on classical accelerators at production throughput.
Designed for complex multi-step reasoning, large-scale code generation, and structured problem-solving. Quantum-assisted gradient refinement during training yields sharper loss convergence on math and logic benchmarks.
Fine-tuned on crystallographic databases, molecular dynamics trajectories, and materials science literature. Uses quantum-sampled embeddings for conformer ranking and property prediction tasks.
Field Harness: the coding harness for QF models.
Field Harness connects QF Reasoner and QF Matter directly to your repository. It indexes your codebase, executes tools, runs tests, and streams model outputs — all from a single CLI.
✓ Model loaded: QF Reasoner 32B (256K context)
✓ Repository context indexed: 847 files across 62 modules
✓ Tool registry: 12 tools available
$ field-harness run "Optimize attention mechanism for batched inference"
▸ Analyzing codebase structure...
▸ Identifying attention modules: 3 candidates
▸ Generating implementation plan...
▸ Executing: 2 files modified, 1 file created
▸ Running test suite: attention/tests/
✓ All 142 tests passing
✓ Changes staged on branch feature/optimize-attention
Concrete Integration Benefits
- Full repository AST indexing — the model understands your codebase structure before generating a single token.
- Tool execution sandbox — models can invoke linters, type checkers, and test runners; results feed back into the generation context.
- Model-aware context budgeting — Field Harness automatically allocates the 256K or 128K context window across code, tool output, and conversation history.
- Quantum-assisted sampling mode — for QF Matter, optionally route conformer-ranking subtasks through the cryogenic QPU for diversity-weighted sampling.
- Deterministic replay — every model interaction is logged with its full context for auditing and reproducibility.
Install via pip:
pip install field-harness[quantum]
Adjust qubit rotation angles and observe measurement probabilities.
This is a simplified two-qubit model. Each qubit's rotation angle determines its measurement probability distribution. The four joint outcome probabilities always sum to approximately 100%. Move the sliders to explore the state space.
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Three tracks, all AI-centered.
Quantum computing supports each program — it does not lead them. Our research agenda is driven by model capability, not hardware novelty.
Quantum-Assisted Model Optimization
Using cryogenic quantum processors to refine gradient estimates during large-scale transformer training. We target specific optimization subproblems — attention head pruning, embedding compression — where quantum sampling provides provably sharper convergence bounds. Classical infrastructure handles the remaining 98% of the training pipeline.
Scientific Reasoning & Simulation
QF Matter 14B integrates with quantum-sampled embeddings for molecular conformer ranking, crystal structure prediction, and reaction pathway scoring. The model proposes hypotheses; quantum processors evaluate energetically favorable configurations when classical force fields reach their precision limits.
Hybrid Quantum-Classical Training
Developing training protocols where select parameter subgroups are optimized on QPUs while the bulk of model weights are updated classically. Current focus: using variational quantum circuits as differentiable auxiliary modules within transformer attention blocks, with gradient routing determined by loss curvature analysis.
We demonstrate that gradient estimates for attention-head importance scoring can be refined using a 72-qubit cryogenic quantum processor, yielding a 18.3% reduction in training steps to convergence on a 32B-parameter mixture-of-experts model. The quantum subroutine targets a non-convex subproblem within the gradient computation; all other operations remain classical. We provide convergence guarantees under bounded noise assumptions and validate empirically on mathematics and code generation benchmarks.
Full-stack, from models to cryogenic processors.
Classical accelerators handle the vast majority of computation. Cryogenic QPUs are scheduled selectively for optimization subtasks where they provide a measurable advantage.
QF Matter 14B
CLI & SDK
A100 / H100
Gradient Routing
72 qubits · Selective
Researchers, engineers, and quantum physicists.
A multidisciplinary team spanning AI research, high-performance systems engineering, and quantum information science. All names and roles are fictional.
Join the lab.
All positions are fictional — this is a demonstration page. In a real deployment, these would link to an application portal.
Research Scientist, Quantum-AI Systems
Design hybrid training protocols; collaborate with quantum physicists and ML engineers. PhD required.
Systems Engineer, High-Performance Inference
Optimize model serving across GPU clusters; build tooling for Field Harness. 5+ years experience.
Developer Advocate, AI Tools & SDKs
Write documentation, build demos, and support the developer community around Field Harness.
Cryogenic Hardware Technician
Maintain and operate dilution refrigerator systems for the 72-qubit QPU. 3+ years in low-temperature physics.