Yuxuan Du

[2024/12] Our paper "Optical quantum sensing for agnostic environments via deep learning" was accepted to Physical Reviewe Research (PRR) [Link].

[2024/10] Our paper "On exploring practical potentials of quantum auto-encoder with advantages" was accepted to IEEE Transactions on Neural Networks and Learning Systems (TNNLS) [Link].

[2024/08] Our paper "MG-Net: Learn to Customize QAOA with Circuit Depth Awareness" was accepted to Conference on Neural Information Processing Systems (NeurIPS) 2024 [Link].

[2024/08] Our paper "Multimodal Deep Representation Learning for Quantum Cross-platform Verification" was accepted to Physical Review Letters (PRL) [Link].

[2024/08] I accepted the invitation to serve as the Area Chair for ICLR 2025.

[2024/06] Our paper "Coreset selection can accelerate quantum machine learning models with provable generalization" was accepted to Physical Review Applied (PRApplied) [Link].

[2024/06] We released our new paper "Quantum Compiling with Reinforcement Learning on a Superconducting Processor" [PDF].

[2024/05] We released our new paper "Separable Power of Classical and Quantum Learning Protocols Through the Lens of No-Free-Lunch Theorem" [PDF].

[2024/04] I gave two invited talks at [PIERS2024] and UESTC.

[2024/04] Our paper "Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reduction" was accepted to Quantum Machine Intelligence (QMI) [Link].

[2024/04] Our paper "Transition role of entangled data in quantum machine learning" was accepted to Nature Communications [Link].

[2024/04] Our paper "Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning" was accepted to Quantum Science and Technology (QST) [Link].

[2024/03] I gave an invited talk "Multimodal Deep Representation Learning for Quantum Cross-platform Verification" at Quantum Computer Systems Lecture Series, Notre Dame & MIT, US [Link]. The slides can be downloaded at [here].

[2024/01] Our paper "A Time-Consistency Curriculum for Learning from Instance-Dependent Noisy Labels" was accepted to Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) [Link].

[2024/01] Our paper "Neural Auto-designer for Enhanced Quantum Kernels" was accepted to International Conference on Learning Representations 2024 (ICLR 2024) [Link].

[2023/11] I gave an invited talk "On the Theoretically Refined Quantum Machine Learning" at Central South University.

[2023/11] We released our new paper "Optical Quantum Sensing for Agnostic Environments via Deep Learning" [PDF].

[2023/11] We released our new paper "Multimodal deep representation learning for quantum cross-platform" [PDF].

[2023/10] I gave an invited talk "Unveiling Quantum Deep Neural Networks: Problem-Dependent Performance Insights" at 12th International Conference on Computing and Patter Recognition, Qingdao, China.

[2023/10] I gave an invited talk "Unveiling Quantum Neural Classifiers: Problem-Dependent Performance Insights" at Institute of Computing Technology, Chinese Academy Science.

[2023/09] We released our new paper "Coreset selection can accelerate quantum machine learning models with provable generalization" [PDF].

[2023/08] We released our new paper "ShadowNet for Data-Centric Quantum System Learning" [PDF].

[2023/08] Our paper "Demystify Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification" [PDF] was accepted to Physical Review Letter (PRL) [Link].

[2023/08] Our two papers "Demystify Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification" [PDF] and "Transition role of entangled data in quantum machine learning" [PDF] were accepted to the 7th International Conference on Quantum Techinques in Machine Learning (QTML) [Link].

[2023/08] I accepted the invitation to serve as a Reviewer Editor for Fronteir of Physics and a Meta-Reviewer for AJCAI 2023.

[2023/06] We released our new paper "Transition role of entangled data in quantum machine learning" [PDF].

[2023/04] Our survey "Recent Advances for Quantum Neural Networks in Generative Learning" [PDF] was accepted by Transactions on Pattern Analysis and Machine Intelligence (T-PAMI) .

[2023/04] Our paper "Which is Better for Learning with Noisy Labels: The Semi-supervised Method or Modeling Label Noise?" was accepted by International Conference on Machine Learning 2023 (ICML 2023) .

[2023/03] I accepted the invitation to serve as a Reviewer for Neurips 2023 and Transactions on Machine Learning Research.

[2023/03] I gave an invited talk "Demystify Problem-Dependent Power of Quantum Neural Networks on Multi-Class" at Centre for Quantum Technologies, National University of Singapore.

[2023/01] Our paper "QAOA-in-QAOA: solving large-scale MaxCut problems on small quantum machines" [PDF] was accepted by PRApplied.

[2023/01] Our paper "Symmetric Pruning in Quantum Neural Networks" [PDF] was accepted by International Conference on Learning Representations 2023 (ICLR 2023) (Spotlight).

[2023/01] We released our new paper "Demystify Problem-Dependent Power of Quantum Neural Networks on Multi-Class Classification" [PDF].

[2022/09] We released our new paper "Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reduction" [PDF].

[2022/09] Two papers are accpeted to NeurIPS 2022 [Link1] & [Link2].

[2022/09] Our paper "The dilemma of quantum nerual networks" [PDF] was accepted by IEEE Transactions on Neural Networks and Learning Systems (TNNLS).

[2022/08] We released our new paper "Symmetric Pruning in Quantum Neural Networks" [PDF].

[2022/06] We released our new review arcticle "Recent Advances for Quantum Neural Networks in Generative Learning" [PDF].

[2022/06] We released our new paper "MSR: Making Self-supervised learning Robust to Aggressive Augmentations" [PDF].

[2022/05] I gave an invited talk "Exploring Variational Quantum Algorithms via Statistical Learning Theory" at Institute of Theoretical Physics, Chinese Academy of Sciences [Link].

[2022/05] We released our new paper "QAOA-in-QAOA: solving large-scale MaxCut problems on small quantum machines" [PDF].

[2022/05] Our paper "Quantum architecture search for variational quantum algorithms " was accepted by Nature Parter Journal Quantum Information (npj qi) [Link].

[2022/05] Our paper "A distributed learning scheme for variational quantum algorithms" was accepted by Transactions on Quantum Engineering [Link].

[2022/05] We released our new paper "Theory of Quantum Generative Learning Models with Maximum Mean Discrepancy" [PDF]

[2022/04] We released our new paper "Efficient and practical quantum compiler towards multi-qubit systems with deep reinforcement learning" [PDF]

[2022/04] I gave an invited talk "Anchoring Variational Quantum Algorithms via Statistical Learning Theory" at Tsinghua-BIMSA [Link].

[2022/04] I accepted the invitation to serve as a Reviewer for Neurips 2022 .

[2022/03] We released our new paper "Unentangled quantum reinforcement learning agents in the OpenAI Gym" [PDF]

[2022/03] I gave an invited talk "Variational Quantum Computing & Learning Theory" at Center on Frontiers of Computing Studies, Peking University [Link].

[2022/03] Our paper "Quantum differentially private sparse regression learning " was accepted by Transactions on Information Theory (TIT) [Link].

[2022/03] Our paper "DyRep: Bootstrapping Training with Dynamic Re-parameterization " was accepted by CVPR [PDF].

[2022/02] Our paper "Efficient bipartite entanglement detection scheme with a quantum adversarial solver " was accepted by Physical Review Letter (PRL) [PDF].

[2022/02] Pennylane has collected our proposal of QuGAN [PDF] into their Demos list.

[2022/02] I accepted the invitation to serve as a Reviewer for ACMM 2022 .

[2022/02] Our paper "Efficient measure for the expressivity of variational quantum algorithms " was accepted by Physical Review Letter (PRL) [PDF].

[2022/01] We released our new paper "Do We Need to Penalize Variance of Losses for Learning with Label Noise?" [PDF].

[2022/01] We released our new paper "Quantum circuit architecture search on a superconducting processor" [PDF].

[2021/12] I accepted the invitation to serve as a Reviewer for ICML 2022 .

[2021/12] I accepted the invitation to serve as a Reviewer for IJCAI 2022 .

[2021/10] Our paper "On the learnability of quantum neural networks " was accepted by PRX Quantum [PDF].

[2021/10] Our paper "Towards understanding the power of quantum kernels in the NISQ era " was accepted by (Quantum Techniques in Machine Learning 2021) as a talk [Website].

[2021/08] Our paper "Towards understanding the power of quantum kernels in the NISQ era " was accepted by Quantum [PDF].

[2021/08] Our paper "Experimental quantum generative adversarial networks for image generation " was accepted by PRApplied [Editor Suggestion] [PDF].

[2021/07] I accepted the invitation to serve as a Reviewer for ICLR 2022 .

[2021/07] We organize a course "Adanced topics of AI" at School of Gifted Young, USTC.

[2021/06] We released our new paper "On exploring practical potentials of quantum auto-encoder with advantages" [PDF].

[2021/06] We released our new paper "Accelerating variational quantum algorithms with multiple quantum processors" [PDF].

[2021/06] I gave an invited talk "On exploring practical potentials of variational quantum algorithms with advantages" at CCF forum (第二届CCF理论计算机科学全国优秀博士生论坛) [Website Link].

[2021/06] I gave an invited talk "On exploring practical potentials of variational quantum algorithms with advantages" at University of Electronic Science and Technology of China.

[2021/06] I accepted the invitation to serve as a Reviewer for NeurIPS 2021 .

[2021/05] We released our new paper "The dilemma of quantum neural networks" [PDF][Source code].

[2021/05] I gave an invited talk "On exploring practical potentials of variational quantum algorithms with advantages" at Beihang University.

[2021/05] I gave an invited talk "On exploring practical potentials of variational quantum algorithms with advantages" at Beijing Academy of Quantum Information Sciences (BAQIS).

[2021/04] I accepted the invitation to serve as a Reviewer for ACML 2021.

[2021/04] We released our new paper "An efficient measure for the expressivity of variational quantum algorithms" [PDF].

[2021/04] Our paper "Quantum noise protects quantum classifiers against adversaries" was accepted by PRR [Arxiv].

[2021/04] I gave an invited talk "The Capabilities and Limitations of Quantum Learning Models" at Center on Frontiers of Computing Studies, Department of Computer Science, Peking University. Slides is avaiable at here.

[2021/04] I accepted the invitation to serve as a Reviewer for ACMM 2021 .

[2021/04] We released our new paper "Towards understanding the power of quantum kernels in the NISQ era" [PDF].