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Powerful, hardware-agnostic quantum code development for derivatives, portfolios, risk, and more.
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Our Clients

Our clients trust Classiq to enable their quantum initiatives, upskill their workforce, and deploy efficient quantum programs

Case Studies

Electric Vehicle Cooling Simulation With The BMW Group

Thermal management is a critical challenge in electric vehicle (EV) design, directly impacting performance, safety, and battery longevity. Simulating cooling systems such as heat flow through or between components requires solving complex equations which can become computationally expensive with classical tools.

In a joint use-case with the BMW Group, Classiq explored how quantum computing could support the simulation of EV cooling systems. The project focused on modeling and optimizing heat transfer in tightly coupled mechanical and electrical subsystems using quantum algorithms designed to reduce computational overhead.

By expressing high-level thermal models and automatically generating optimized quantum circuits, the PoC demonstrated the potential for more scalable simulation workflows. Quantum-enhanced approaches were assessed for their ability to improve modeling fidelity and accelerate numerical solutions.
This collaboration demostrated how quantum computing can complement classical simulation techniques in automotive R&D, exploring a pathway to more efficient thermal design and accelerated innovation in next-generation electric vehicles.

Battery Material Optimization

Designing advanced battery materials such as solid electrolytes, high-capacity cathodes, or fast-charging anodes requires accurate modeling of atomic-scale properties like ion mobility, phase stability, and electron transport. Classical methods often struggle with the quantum complexity of these systems, especially when dealing with strongly correlated materials.

A quantum computing project in battery material optimization explores how quantum algorithms, particularly the Variational Quantum Eigensolver (VQE) or Quantum Phase Estimation (QPE), can simulate the electronic structure of novel compounds with higher fidelity. This enables better screening of candidate materials for properties like conductivity, voltage stability, or degradation resistance.
The use-case typically focuses on representative molecular fragments or crystalline structures, benchmarking quantum-derived insights against classical computational chemistry methods such as DFT. By modeling energetics, charge distribution, or diffusion pathways, the study may inform which materials might offer superior performance.

For energy storage innovators, this approach offers a potential leap forward in accelerating the discovery of safer, more efficient, and longer-lasting battery chemistries vital to the future of electric vehicles, grid storage, and portable electronics.

Quantum Applications For Automotive

Battery Design
  • Explore anode/cathode chemistries with scalable quantum software
  • Predict ion diffusion and stability
  • Shorten lab cycles
Autonomous Driving Optimization
  • Quantum enhancement for sensor fusion and decision making
  • Reduce edge compute latency
  • Integrates with ADAS stack
Vehicle Routing
  • Optimize fleet routing under dynamic constraints
  • Optimized quantum circuits outperform classical heuristics on large instances
  • Cut energy and time costs

Enable Your Quantum Initiatives

Quantum Team Building

If you and your team are getting started with quantum computer programming, Classiq’s hands-on quantum training program is built for technical professionals. You’ll begin with a focused introduction to quantum computing fundamentals: qubits, quantum gates, and circuit models. Next, you’ll explore key quantum algorithms such as QAOA, VQE, and Grover’s, with an emphasis on practical implementation. The core of the training is onboarding to the Classiq platform, where you’ll learn high-level quantum algorithm development, resource-aware quantum circuit design, and hardware-aware optimization. This program equips developers, engineers, and researchers with the skills to build scalable quantum applications from day one.

Quantum Use-Case Implementation

Classiq’s Use-Case Scoping and Implementation Program is designed to guide teams through the full lifecycle of quantum application development. Whether you're exploring quantum for the first time or scaling an R&D initiative, our experts work closely with you to identify high-impact quantum use cases, define algorithmic approaches, and map requirements to current hardware capabilities. From initial use-case selection to algorithm synthesis and execution on quantum processors, the program is tailored to your project’s complexity and your team’s quantum maturity. It's a practical, results-driven pathway to developing and deploying real-world quantum solutions with clarity, speed, and technical confidence.

Advanced Quantum Application Development

Classiq’s Advanced Quantum Application Development offering is designed for teams looking to elevate their quantum work into scalable, future-ready solutions. This offering supports the development of complex quantum circuits using Classiq’s high-level synthesis platform, enabling modular, optimized, and hardware-agnostic quantum algorithm design. It’s ideal for organizations aiming to turn their quantum initiatives into long-term assets, reusable components, or proprietary IP. Whether refining advanced algorithms like QAOA or VQE, or preparing applications for next-gen quantum hardware, this offering helps teams industrialize their quantum development and ensure their work is robust, efficient, and strategically aligned with long-term R&D goals.

Explore Quantum Finance Applications