Robust and Efficient Quantum Property Learning
I’m excited to share our latest work, Demonstration of robust and efficient quantum property learning with shallow shadows, published in Nature Communications! 🎉
📝 Authors: Hong-Ye Hu, Andi Gu, Swarnadeep Majumder, Hang Ren, Yipei Zhang, Derek S. Wang, Yi-Zhuang You, Zlatko Minev, Susanne F. Yelin, Alireza Seif
🔍 Context: Extracting information efficiently from quantum systems is crucial for advancing quantum information processing. Classical shadow tomography offers a powerful technique, but it struggles with noisy, high-dimensional quantum states and complex observables.
🤔 Key Question: Can we overcome noise limitations and improve sample efficiency in quantum state learning, especially for high-weight and non-local observables, using shallow quantum circuits?
💡 Our Findings: We introduce robust shallow shadows—a protocol designed to mitigate noise using Bayesian inference, enabling highly efficient learning of quantum state properties, even in the presence of noise! Our experiments on a 127-qubit superconducting quantum processor confirm the protocol’s power, showing up to 5x reduction in sample complexity compared to traditional methods, even under realistic noise conditions.
✨ Key Takeaways:
Noise-resilience: Accurate predictions across diverse quantum state properties.
Sample Efficiency: Substantial reduction in sample complexity for high-weight and non-local observables.
Scalability: The protocol is well-suited for near-term quantum devices, even with noise.
🔬 This work marks a significant step towards more efficient and scalable quantum state characterization on current quantum hardware.