An agentic layer that finds hard problems and solves them — without asking you to change how you work.
Lightbridge sits between your existing workload and a hardware-agnostic quantum routing layer.
Your workload runs as normal — inside CUDA-Q, an Omniverse digital twin, or a model training pipeline.
Lightbridge's agentic layer watches for quantum-favorable structure — combinatorial optimization, simulation, or search patterns that are expensive to solve classically.
The identified subproblem is formulated automatically into a form quantum backends can solve.
A hardware-agnostic routing layer sends it to the right backend — whether that's a specific quantum processor, a quantum-inspired solver, or a third-party synthesis/compiler engine.
The result returns to exactly where you were working — no separate console, no new interface to check.
Lightbridge integrates with NVIDIA NVQLink, made available through the cudaq-realtime API, which provides the real-time, low-latency interconnect between GPUs and quantum processors. This is the infrastructure that makes it possible for a quantum computation to function as a genuine co-processor step inside a GPU-accelerated workload, rather than a separate system requiring its own integration project. We build directly on top of it rather than reinventing it.
Lightbridge doesn't build a proprietary circuit-synthesis engine, and we don't ask customers to commit to one quantum hardware modality. Our routing layer can call quantum hardware directly, or route through third-party compiler and synthesis platforms as interchangeable backends — whichever combination best fits the specific problem being solved. As the underlying hardware and compiler landscape evolves, our customers' integration with Lightbridge doesn't have to.
Not every hard problem benefits from quantum computation today. Lightbridge's agentic layer is built specifically to recognize the patterns that do — primarily combinatorial optimization problems (where the number of possible solutions grows explosively with problem size), certain classes of simulation, and structured search problems. Examples include routing and scheduling optimization, portfolio and resource allocation, digital-twin-surfaced layout problems, and neural network pruning formulated as a quadratic unconstrained binary optimization (QUBO) problem — an approach with a growing body of published research behind it. Our detection layer is built to find these patterns inside workloads that were never explicitly framed as quantum problems in the first place.
The same underlying agentic pattern — watch the workload, detect the hard problem, formulate it, route it, return the result — is designed to work inside any environment where problem-solving happens, not just NVIDIA's. Databricks and Snowflake are next.