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BQPhy: Quantum-Powered Solvers for a Better Future
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At BQPhy, we believe quantum computing can unlock solutions to the world’s most complex scientific and engineering problems. We’re inviting researchers, institutions, and industry pioneers to collaborate with us in harnessing Quantum-classical & Quantum Native algorithms to accelerate tough to impossible simulations.
Our Current Focus Areas
Hybrid Quantum-Classical Methods for Large Scale, Intractable CFD
- Revolutionize fluid dynamics with quantum-enhanced solvers for aerospace, climate modeling, and energy systems.
- Achieve faster convergence and higher accuracy in turbulent flow simulations.
Quantum-Assisted Physics-Informed Neural Networks (PINNs) for Rapid Surrogate Modeling
- Accelerate design optimization with quantum-boosted neural networks.
- Reduce computational costs in material science, engineering, and more.
Quantum Monte Carlo for Uncertainty Quantification
- Tackle stochastic modeling with quantum-powered probabilistic methods.
- Improve reliability in forecasting, and risk analysis.
RESEARCH
Download our Scientific Paper on
Benchmarking of GPU-optimized Quantum-Inspired Evolutionary Optimization Algorithm using Functional Analysis
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Demonstration of Scalability and Accuracy of Variational Quantum Linear Solver for Computational Fluid Dynamics
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Benchmarking Quantum-Assisted PINN (QA-PINN) for Computational Fluid Dynamics
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In the Spotlight
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