Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 10·5 min read
Fidelity-Aware Scheduling of Quantum Circuits on Multi-QPU Systems
TL;DR
A new framework for scheduling quantum circuits on multi-QPU systems uses a Graph Neural Network to estimate execution fidelity, optimizing resource use while maintaining performance. This approach significantly reduces computational overhead compared to traditional exhaustive methods.
✦ Why It Matters
Implement this framework to enhance the fidelity of quantum circuit execution on multi-QPU systems today.
Key Takeaways
- Utilize GNNs to predict circuit fidelity on various QPUs.
- Balance execution fidelity and parallelism with a tunable scheduler.
- Reduce computational overhead compared to brute-force methods.
- Focus on error management for reliable quantum computations.
How It Works
The framework leverages a Graph Neural Network to analyze the structure of quantum circuits and predict their performance on different QPUs. By estimating fidelity before compilation, it allows for informed scheduling decisions that optimize resource use while maintaining high execution fidelity.