Quantum Is Moving From Research to Roadmap

Across finance, pharma, chemicals, automotive, aerospace, and energy, quantum computing has stopped being a physics story and become a business one. Leading enterprises are no longer asking if quantum matters. They're running structured programs, building internal teams, and shipping the software that carries their use cases forward as the technology advances.

Real Problems, Real Fits

Banks: portfolio optimization and risk simulation. Pharma and chemicals: molecular and materials simulation. Automotive and aerospace: materials science and structural design. Energy: grid optimization. The enterprises building now are the ones solving new problems, building IP, and staying ahead as the field matures.

Advantage Belongs to Those Building Now

Hardware alone doesn't create advantage. Teams, algorithms, and software do, and they take time to build. The enterprises building now are the ones ready to move first when the hardware is. Start today, and the advantage is already yours when it counts.

Build Your Quantum Capability in Three Phase

Industry Domain Experts

Turn your domain and algorithm expertise into working quantum applications and IP today. Classiq enables you to explore new approaches to hard problems and translate your ideas directly into functional quantum code, using the deep industry expertise and skills you already have.

Quantum Developers

If you know quantum, Classiq makes you faster and your code more efficient. Work at a higher level of abstraction than traditional SDKs, move quickly from algorithm to optimized hardware-ready output, and let the platform handle the implementation details that slow you down today.

Why quantum is hard for most AI tools.

Generic AI coding agents hit a ceiling quickly in quantum. Four reasons:

Limited training data

High-quality quantum code is scarce. Most models have almost nothing reliable to learn from, which means outputs that look plausible but frequently aren't.

Rapid Pace of Change

Quantum concepts, breakthroughs, and the ecosystem change rapidly, outpacing even the most advanced public models. Public examples that worked before are not usually backward compatible, requiring the most up to date syntax, research, and approaches.

Hardware dependencies

Optimized quantum code has to account for qubit topology, gate depth, and connectivity constraints. That requires reasoning about physical systems, not just syntax.

No repeatable patterns

Quantum algorithms don't follow templates. Each one requires genuine understanding of the underlying mechanics, not pattern recognition.

Classiq is built around these realities. The result is outputs you can trust rather than outputs you have to verify from scratch.

Calssiq’s AI agents.
Solving real-world problems

See how real-world problems and domain expertise can be turned into working quantum code and IP.

Ready to see what Quantum+AI can do for your team?

Get in touch with the experts now and discover how Classiq can bring your quantum ideas to life!