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What is BQPhy®
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Our Story
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February 20, 2025
Press
BQP Achieves Breakthrough in Quantum CFD Simulation with Just 30 Logical Qubits
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BQP sponsors SAE Aerothon 2025
•
BQP Raises $5M Oversubscribed Seed Round
•
25X speed-up achieved with BQPhy’s QA-PINN for accelerated CFD training
•
BQP × Classiq × NVIDIA set a new milestone in QCFD with 100X circuit compression
BQP sponsors SAE Aerothon 2025
•
BQP Raises $5M Oversubscribed Seed Round
•
25X speed-up achieved with BQPhy’s QA-PINN for accelerated CFD training
•
BQP × Classiq × NVIDIA set a new milestone in QCFD with 100X circuit compression
BQP sponsors SAE Aerothon 2025
•
BQP Raises $5M Oversubscribed Seed Round
•
25X speed-up achieved with BQPhy’s QA-PINN for accelerated CFD training
•
BQP × Classiq × NVIDIA set a new milestone in QCFD with 100X circuit compression
BQP sponsors SAE Aerothon 2025
•
BQP Raises $5M Oversubscribed Seed Round
•
25X speed-up achieved with BQPhy’s QA-PINN for accelerated CFD training
•
BQP × Classiq × NVIDIA set a new milestone in QCFD with 100X circuit compression
BQP sponsors SAE Aerothon 2025
•
BQP Raises $5M Oversubscribed Seed Round
•
25X speed-up achieved with BQPhy’s QA-PINN for accelerated CFD training
•
BQP × Classiq × NVIDIA set a new milestone in QCFD with 100X circuit compression
BQP sponsors SAE Aerothon 2025
•
BQP Raises $5M Oversubscribed Seed Round
•
25X speed-up achieved with BQPhy’s QA-PINN for accelerated CFD training
•
BQP × Classiq × NVIDIA set a new milestone in QCFD with 100X circuit compression
BQP sponsors SAE Aerothon 2025
•
BQP Raises $5M Oversubscribed Seed Round
•
25X speed-up achieved with BQPhy’s QA-PINN for accelerated CFD training
•
BQP × Classiq × NVIDIA set a new milestone in QCFD with 100X circuit compression
BQP sponsors SAE Aerothon 2025
•
BQP Raises $5M Oversubscribed Seed Round
•
25X speed-up achieved with BQPhy’s QA-PINN for accelerated CFD training
•
BQP × Classiq × NVIDIA set a new milestone in QCFD with 100X circuit compression
BQP sponsors SAE Aerothon 2025
•
BQP Raises $5M Oversubscribed Seed Round
•
25X speed-up achieved with BQPhy’s QA-PINN for accelerated CFD training
•
BQP × Classiq × NVIDIA set a new milestone in QCFD with 100X circuit compression
BQP sponsors SAE Aerothon 2025
•
BQP Raises $5M Oversubscribed Seed Round
•
25X speed-up achieved with BQPhy’s QA-PINN for accelerated CFD training
•
BQP × Classiq × NVIDIA set a new milestone in QCFD with 100X circuit compression
BQP sponsors SAE Aerothon 2025
•
BQP Raises $5M Oversubscribed Seed Round
•
25X speed-up achieved with BQPhy’s QA-PINN for accelerated CFD training
•
BQP × Classiq × NVIDIA set a new milestone in QCFD with 100X circuit compression
BQP sponsors SAE Aerothon 2025
•
BQP Raises $5M Oversubscribed Seed Round
•
25X speed-up achieved with BQPhy’s QA-PINN for accelerated CFD training
•
BQP × Classiq × NVIDIA set a new milestone in QCFD with 100X circuit compression
BQP sponsors SAE Aerothon 2025
•
BQP Raises $5M Oversubscribed Seed Round
•
25X speed-up achieved with BQPhy’s QA-PINN for accelerated CFD training
•
BQP × Classiq × NVIDIA set a new milestone in QCFD with 100X circuit compression
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In your experience, what is the PRIMARY benefit that high-quality Digital Engineering software brings to your business?
Faster design cycles and shorter time to market
Higher product performance and reliability
Lower production costs without trade-offs in quality
Deliver product innovations that are ahead of the market
How often does the organization allocate dedicated time or budget for "tech scouting" and digital engineering upgrades?
We are currently focused on maximizing our existing software that does the job well
We adopt new technology only after at least two other industry leaders have succeeded with it.
We run small-scale pilots for promising new digital engineering tools as and when available.
We have a dedicated a budget and an innovation team that evaluates new tech
We are always on the lookout for breakthrough solutions that can solve our engineering challenges
What is the primary output of your digital engineering efforts?
Performance Prediction: Using CFD/Simulation to see if a design will work before it's built.
Efficiency & Logic: Using Mathematical Optimization to reduce costs and streamline operations like supply chain, manufacturing processes, scheduling critical tasks.
Automated Intelligence: Using AI/ML to process data faster and catch errors that humans might miss.
Generative Insights: Using a mix of CFD, optimization and/or AI to suggest entirely new designs or solutions
When selecting a new digital engineering tool, which "Core Requirement" is your absolute non-negotiable?
It should have a low learning curve and "plug-and-play" capability with our current stack.
It should be easily customizable for our unique requirements
Its performance is the only thing that matters, and we don't mind changing our existing workflows.
Low total cost of ownership includes training and maintenance.
Long-term vendor stability and support commitment
What challenges do you face with current digital engineering and simulation tools?
Takes too much time for high fidelity simulations
Too much time spent on data preparation, pre and post processing
High Computational cost, hence we scale down the problem
Difficulty integrating multiple tools
Tools are easy to use, but I can't tweak the underlying algorithms for our specific use case.
Lack of documentation and support
Which of the following process‑related challenges does your team face most often?
Long approval cycles for new tools
Difficulty measuring tool ROI
Lack of time for proper evaluation
Poor vendor responsiveness during trials
Resistance from end‑users to change
Where do you primarily run compute-heavy workloads for digital engineering and simulation tools?
On-premise (HPC / GPUs)
Cloud infrastructure
Hybrid (on-prem + cloud)
Local machines / workstations
How extensively do you use GPUs in your digital engineering and simulation tool workflows?
Heavily for most workloads
Moderately (used for specific tasks)
Rarely
Not used
What is your approximate annual spend on compute (HPC / GPU / cloud) for digital engineering and simulation tools?
< $10K
$10K – $50K
$50K – $200K
$200K+
Not sure
How would you prefer to access advanced capabilities within digital engineering and simulation tools?
Inside existing tools, seamless integration with our current environment
Programmable and script‑driven workflows such as Python SDK
Cloud platform with minimal setup, ready for immediate use
APIs integrated into internal systems that plug into our own infrastructure
Please name up to social media channels, blogs, or technical publications that you trust most for keeping up with new digital engineering tools.
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