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Quantum-Assisted Physics-Informed Neural Networks for Computational Fluid Dynamics
BQP develops advanced simulation technologies for aerospace, space, defense, and complex engineering applications. Its BQPhy® platform combines advanced simulation solvers with quantum-inspired methods designed to accelerate simulation and optimization workflows.
This challenge focuses on Quantum-Assisted Physics-Informed Neural Networks, or QAPINNs. A QAPINN is a hybrid quantum-classical model in which a variational quantum circuit replaces part of a traditional Physics-Informed Neural Network.
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BQP Challenge
Investigate how the introduction of a quantum layer changes the learning process of a Physics-Informed Neural Network.
The goal is not simply to claim that a QAPINN outperforms a classical PINN. Teams should explain:
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When the quantum layer changes learning behavior
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Why those changes occur
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How the quantum circuit should be designed for a particular class of differential equations
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What is gained or lost by adding the quantum layer
Teams should support their conclusions with experimental results and mathematical analysis wherever possible.
The Challenge
Teams may examine:
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Data-encoding methods
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Measurement operators
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Circuit depth
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Entanglement structures
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Fourier-spectrum analysis
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Loss landscapes
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Barren plateaus
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Number of qubits
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Model expressivity
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Generalization
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Parameter reduction
Suggested benchmark problems include:
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Burgers’ equation
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Heat equation
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Computational fluid dynamics problems
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Materials-related partial differential equations
Suggested Areas of Investigation
Teams should consider reporting:
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PDE residual error
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Relative L2 error
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Training time
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Number of trainable parameters
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Memory requirements
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Explainability or interpretability metrics
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Performance on unseen domains
Recommended Metrics
Teams should:
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Solve a set of PDEs using classical PINNs and QAPINNs.
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Use Explainable AI techniques to compare how learning occurs in each model.
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Analyze how a variational quantum circuit affects the classical learning process.
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Develop a methodology for constructing a problem-specific quantum circuit and QAPINN architecture.
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Provide a mathematical basis for the conclusions.
Key Project Tasks
Your final GitHub repository should include:
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A technical report of approximately 5–15 pages
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Source code
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Reproducibility instructions
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Presentation slides
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A summary of key findings
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Recommendations for future research
Required Deliverables
Teams may use:
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PennyLane
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Qiskit
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Classical machine-learning frameworks
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Quantum simulators
All quantum work may be completed using simulators.
Platforms & Tools
Strong submissions will:
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Explore multiple PDEs or benchmark problems.
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Visualize learning behavior with and without a quantum layer.
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Compare individual neuron or layer behavior.
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Explain the effect of qubit count, entanglement, encoding, and measurement.
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Identify both benefits and limitations of the quantum layer.
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Propose an ideal quantum-layer configuration for different problem types.
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Support conclusions with mathematical rigor and reproducible experiments.
Judging Criteria
Use only:
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Public datasets
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Synthetic datasets
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Self-generated simulation data
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Properly cited research and open-source resources
Do not upload confidential, personal, classified, export-controlled, or restricted information to external AI, cloud, or quantum platforms.
All work must be original and completed during the challenge period.
Data & Research Requirements
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Maximum team size: 3 participants
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Final submission deadline: August 7, 2026
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No deadline extensions will be offered.
Team & Deadline
