Quantum Error Mitigation for Burgers Equation Solvers
Analysis
This paper presents a hybrid quantum-classical framework for solving the Burgers equation on NISQ hardware. The key innovation is the use of an attention-based graph neural network to learn and mitigate errors in the quantum simulations. This approach leverages a large dataset of noisy quantum outputs and circuit metadata to predict error-mitigated solutions, consistently outperforming zero-noise extrapolation. This is significant because it demonstrates a data-driven approach to improve the accuracy of quantum computations on noisy hardware, which is a crucial step towards practical quantum computing applications.
Key Takeaways
- •Introduces a hybrid quantum-classical framework for solving the Burgers equation on NISQ hardware.
- •Employs an attention-based graph neural network for data-driven error mitigation.
- •The learned model outperforms zero-noise extrapolation in reducing errors.
- •Demonstrates a promising approach for improving the accuracy of quantum computations on noisy devices.
“The learned model consistently reduces the discrepancy between quantum and classical solutions beyond what is achieved by ZNE alone.”