Machine Learning for Practical Quantum Error Mitigation
Published in Nature Machine Intelligence today, we've been working on how to get the most out of a quantum computer using machine learning. Excited to share it!
"Machine Learning for Practical Quantum Error Mitigation"
by the dream team: Haoran Liao, Derek S. Wang, Iskandar Sitdikov, Ciro Salcedo, Alireza Seif, Zlatko Minev
🔍 Context:
Quantum computers progress to outperform classical supercomputers, but quantum errors remain the primary obstacle. Quantum error mitigation offers a solution but at the high cost of added runtime.
🤔 Key Question:
Can classical machine learning help us overcome errors in today's quantum computers by lowering mitigation overheads, in practice, on real hardware, at the 100 qubit+ scale?
🔬 Our Findings:
Using both simulations and experiments on state-of-art quantum computers (up to 100 qubits), we find that machine learning for quantum error mitigation (ML-QEM) can:
- Significantly reduce overheads.
- Maintain or even outperform the accuracy of traditional methods.
- Deliver nearly noise-free results for quantum algorithms.
We tested multiple machine learning models on various quantum circuits and noise profiles. And, by leveraging ML-QEM, we were able to mimic conventional mitigation results for large quantum circuits, but with much less overhead.
🌟 Conclusion:
Our research underscores the potential synergy between classical hashtag#ML and hashtag#AI and quantum computing. We're excited about the prospects and further research!
🙌 Big thanks to the dream team and many folks who contributed!
Let’s share and discuss the implications of this exciting work! 🌟👇
📄 Paper: Nature Machine Intelligence https://lnkd.in/dGYzC3fq
🔓 Free access: View the paper here https://lnkd.in/dN222X7D
📚 Preprint on arXiv https://lnkd.in/dGbzjtjA
👩💻 Code Repository: Explore on GitHub https://lnkd.in/dcn-xPtm
🎥 Seminar: Watch hashtag#IBM @Qiskit on YouTube here https://lnkd.in/dEPRcMVK