Machine Learning for Practical Quantum Error Mitigation
We've been working on how to get the most out of a quantum computer through machine learning in and excited to share it now:
"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:
While quantum computers show promise in surpassing classical supercomputers, quantum errors have been a persistent challenge. 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 found 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 machine learning and quantum computing. We're excited about the future prospects and further research!
Big thanks to the dream team and many folks who contributed!
Feel free to share and let's discuss the implications!
Paper arXiv? https://buff.ly/3PDa296
Code? Yes
Seminar? @Qiskit YouTube https://www.youtube.com/watch?app=desktop&v=w7GHPmfCzZs&ab_channel=Qiskit
Video: