Revolutionizing computational fluid dynamics with quantum-enhanced neural networks
Advancing the future of computational science
QuaSi combines quantum computing principles with advanced neural network architectures to solve complex fluid dynamics problems that were previously intractable with classical methods.
Our groundbreaking approach leverages Physics-Informed Neural Networks (PINNs) and their quantum variants to deliver unprecedented accuracy and computational efficiency.
Transforming scientific computing across sectors
Our quantum PINNs framework revolutionizes single crystal growth modeling with unprecedented precision, accelerating materials discovery for semiconductors and optics.
Learn moreAdvanced quantum-classical models for cryogenic vessel optimization, accurately predicting fluid behavior, heat transfer, and pressurization dynamics.
Learn moreHigh-fidelity aerodynamic and propulsion system simulations that reduce design iterations and accelerate product development cycles.
Learn moreQuantum-enhanced models for complex multiphase flows in chemical engineering, oil & gas, and environmental applications.
Learn moreThe science behind quantum-enhanced simulation
At the bleeding edge of quantum computing, we're developing hybrid quantum-classical platforms using TensorFlow Quantum and Qiskit to design revolutionary QPINNs. Our approach leverages Parameterized Quantum Circuits for feature encoding and variational quantum algorithms for optimization.
Our classical PINN architectures implement state-of-the-art deep learning frameworks with multi-head attention mechanisms that intelligently focus computational resources on critical solution regions, dramatically improving convergence rates and accuracy.
Our hybrid approach combines the strengths of quantum computing for specific computational bottlenecks with classical high-performance computing infrastructure, creating an optimal balance of quantum advantage and practical deployability.
Discover how QuaSi can transform your computational capabilities
aditya.a.sesh@alumni.tu-berlin.de
Quantum Valley, Silicon Hills
+49 1746959355