Nerox
Preparing visual compute stack
Loading the optimization engine.
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QUBO
CUDA
SOLVER
Preparing visual compute stack
NEROX turns combinatorial problems — routing, scheduling, portfolio selection, graph partitioning — into QUBO instances and solves them with CUDA-accelerated quantum-inspired algorithms. No quantum hardware required.
Built on the modern GPU & compute stack
Submit TSP, portfolio, scheduling, MaxCut, or raw QUBO. Pick your solver — SA, GPU parallel, Tabu, QAOA, or VQE — and stream results back in real time.
Drag-and-drop workflow builder for quantum-inspired experiments. Chain solvers, compare parameters, and track every run with the built-in experiment tracker.
Head-to-head solver comparisons on standard datasets. A live leaderboard shows solution quality, runtime, and gap-to-optimal across every solver.
Automated AI-driven red teaming. Simulate complex attack vectors, audit model vulnerabilities, and stress-test your infrastructure with state-of-the-art adversarial simulations.
Every solver runs directly on CUDA — no CPU fallback. Submit via REST or Python SDK, stream results over WebSocket, and scale from a single GPU to distributed multi-GPU clusters. QAOA and VQE ship with the same API when quantum simulation is needed.
SA, parallel SA, Tabu Search, QAOA, and VQE — all written in CUDA. No wrapper layers, no CPU fallback.
Population-split annealing across up to 8 GPUs with automatic domain decomposition. Handles 50,000+ variable QUBO problems.
Per-iteration energy traces, best-so-far solutions, and convergence metrics stream back in real time as the solver runs.
Single Docker container on any NVIDIA GPU. Run fully on-premises — no data leaves your infrastructure.
Use the visual platform for exploration and benchmarking. Drop to the REST API or Python SDK when integrating into production pipelines.
Describe the problem in plain English. NEROX parses it, maps it to a QUBO, and runs GPU-accelerated annealing — all in under 2 seconds.
GPU-native quantum-inspired optimization for combinatorial problems at any scale.