Neural networks that train faster, cost less, and scale further
QuantumForge builds NeuralSpeedQ, a quantum-inspired acceleration platform that speeds up neural network training and inference on the infrastructure you already run — with a clear path to hybrid quantum hardware as it matures.
Speed
Quantum-inspired routines cut neural-network training and inference time on the hardware you already run.
Benchmark-driven
Every method is validated against traditional implementations before it ships, not just tested in theory.
Ease of integration
Drops into existing ML frameworks and pipelines, no quantum hardware or expertise required to start.
Built to scale
Handles industrial-scale workloads and keeps a compatible path to hybrid quantum-classical hardware.
“Nobody buys quantum. Businesses buy faster training, lower infrastructure cost, and a measurable edge on the workloads that matter.”QuantumForge — Go-to-Market Principle
NeuralSpeedQ runs today, and is built for tomorrow
Rather than waiting on quantum hardware to mature, NeuralSpeedQ ships value now through quantum-inspired methods on classical infrastructure, while staying compatible with hybrid quantum-classical execution as cloud quantum access matures.
Track 1 — Quantum-inspired classical computing
Deployable today on the hardware your team already uses, no quantum access required.
- Quantum-inspired optimization routines
- Advanced matrix and tensor acceleration
- Hybrid optimization methods
- Full classical hardware compatibility
- Enterprise-ready deployment
Track 2 — Hybrid quantum-classical computing
A forward path onto cloud quantum infrastructure as it becomes commercially viable.
- IBM Quantum & Amazon Braket compatibility
- IonQ and emerging hardware targets
- Quantum cloud infrastructure integration
- Hybrid algorithm execution
- Built to absorb hardware gains as they arrive
NeuralSpeedQ™
QuantumForge's commercial acceleration library applies quantum and quantum-inspired algorithms to speed up neural-network training and inference. It integrates with the machine-learning frameworks your team already uses, and runs on classical hardware today while staying compatible with hybrid quantum-classical execution on IBM Quantum, Amazon Braket, and IonQ as it matures.
Three ways to put quantum AI acceleration to work
From licensed software to hands-on deployment support, QuantumForge meets teams wherever they are on the quantum AI curve.
NeuralSpeedQ software
A commercial acceleration library that integrates with your existing ML frameworks to cut training time, inference latency, and GPU spend.
Quantum AI training
Practical, non-academic education that gets engineering and data science teams fluent in quantum-inspired methods without a physics background.
Consulting & deployment
Hands-on engagements that identify the right workloads, benchmark against your current stack, and deploy acceleration into production.
Built for workloads where speed and cost actually matter
NeuralSpeedQ is developed around real industrial bottlenecks, not generic benchmarks.
Manufacturing
Defect detection, process control, predictive maintenance.
Healthcare AI
Faster training on large imaging datasets, lower GPU cost.
Finance & risk
More portfolio simulations and risk models per compute dollar.
Logistics
Supply chain and routing optimization at production scale.
Cybersecurity
Faster anomaly detection models trained on evolving threat data.
Autonomous systems
Lower-latency inference for real-time decision making.
Predictive maintenance
Continuous retraining without runaway infrastructure cost.
Process control
Tighter feedback loops for real-time industrial systems.
A benchmark-driven path from research to deployment
NeuralSpeedQ is developed in three disciplined phases, each with clear deliverables and success criteria before moving forward.
Research, assessment & technical foundation
Evaluate quantum and quantum-inspired approaches against real neural-network bottlenecks, and select the highest-potential methods with a defined benchmark methodology.
Prototype development & validation
Build working prototypes, integrate them into neural-network architectures, and validate measurable gains in training time, inference time, and computational efficiency.
Commercialization & market launch
Package NeuralSpeedQ into a deployable product with APIs, documentation, and pilot programs, backed by a go-to-market strategy for target industries.
Ready to see what quantum-inspired acceleration does for your workloads?
Tell us about your current training and inference bottlenecks. We will walk you through where NeuralSpeedQ can help first.