11 papers
Evaluating Verified Autonomy in Quantum Engineering
Naixu Guo, Changhao Li, Siyu Cheng +6
Reliable quantum engineering is essential for turning quantum phenomena into practical technologies. As quantum platforms grow in scale and complexity, their characterization and o…
OmniQEC: discovering practical quantum error-correcting codes by an AI scientist
Ge Yan, Shanchuan Li, Pengyue Ma +5
Quantum error correction (QEC) is indispensable for scalable fault-tolerant quantum computing. However, discovering QEC codes that remain effective is challenging, as logical perfo…
Efficient foundation decoders for fault-tolerant quantum computing
Ge Yan, Shanchuan Li, Shiyi Xiao +4
Foundation decoders, a class of high-capacity neural decoders, are leading candidates for fault-tolerant quantum computing, with accurate and efficient decoding at large code dista…
Maximum Likelihood Decoding of Quantum Error Correction Codes
Hanyan Cao, Ge Yan, Yuxuan Du +1
Quantum error correction (QEC) is indispensable for realizing fault-tolerant quantum computation, yet its effectiveness hinges critically on the classical decoding algorithm that i…
Rethink the Role of Neural Decoders in Quantum Error Correction
Ge Yan, Shanchuan Li, Yuxuan Du
Quantum error correction (QEC) is essential for enabling quantum advantages, with decoding as a central algorithmic primitive. Owing to its importance and intrinsic difficulty, sub…
Sample-efficient quantum error mitigation via classical learning surrogates
Wei-You Liao, Ge Yan, Yujin Song +5
The pursuit of practical quantum utility on near-term quantum processors is critically challenged by their inherent noise. Quantum error mitigation (QEM) techniques are leading sol…
PALQO: Physics-informed Model for Accelerating Large-scale Quantum Optimization
Yiming Huang, Yajie Hao, Jing Zhou +3
Variational quantum algorithms (VQAs) are leading strategies to reach practical utilities of near-term quantum devices. However, the no-cloning theorem in quantum mechanics preclud…
TITAN: A Trajectory-Informed Technique for Adaptive Parameter Freezing in Large-Scale VQE
Yifeng Peng, Xinyi Li, Samuel Yen-Chi Chen +4
Variational quantum Eigensolver (VQE) is a leading candidate for harnessing quantum computers to advance quantum chemistry and materials simulations, yet its training efficiency de…
Rethink the Role of Deep Learning towards Large-scale Quantum Systems
Yusheng Zhao, Chi Zhang, Yuxuan Du
Characterizing the ground state properties of quantum systems is fundamental to capturing their behavior but computationally challenging. Recent advances in AI have introduced nove…
Neural auto-designer for enhanced quantum kernels
Cong Lei, Yuxuan Du, Peng Mi +2
Quantum kernels hold great promise for offering computational advantages over classical learners, with the effectiveness of these kernels closely tied to the design of the quantum…
Coreset selection can accelerate quantum machine learning models with provable generalization
Yiming Huang, Huiyuan Wang, Yuxuan Du +1
Quantum neural networks (QNNs) and quantum kernels stand as prominent figures in the realm of quantum machine learning, poised to leverage the nascent capabilities of near-term qua…