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Honglin Chen

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3 papers

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NeurIPS Conference 2025 Conference Paper

Purest Quantum State Identification

Yingqi Yu
Honglin Chen
Jun Wu
Wei Xie
Xiangyang Li

Quantum noise constitutes a fundamental obstacle to realizing practical quantum technologies. To address the pivotal challenge of identifying quantum systems least affected by noise, we introduce the purest quantum state identification, which can be used to improve the accuracy of quantum computation and communication. We formulate a rigorous paradigm for identifying the purest quantum state among $K$ unknown $n$-qubit quantum states using total $N$ quantum state copies. For incoherent strategies, we derive the first adaptive algorithm achieving error probability $\exp\left(- \Omega\left(\frac{N H_1}{\log(K) 2^n }\right) \right)$, fundamentally improving quantum property learning through measurement optimization. By developing a coherent measurement protocol with error bound $\exp\left(- \Omega\left(\frac{N H_2}{\log(K) }\right) \right)$, we demonstrate a significant separation from incoherent strategies, formally quantifying the power of quantum memory and coherent measurement. Furthermore, we establish a lower bound by demonstrating that all strategies with fixed two-outcome incoherent POVM must suffer error probability exceeding $ \exp\left( - O\left(\frac{NH_1}{2^n}\right)\right)$. This research advances the characterization of quantum noise through efficient learning frameworks. Our results establish theoretical foundations for noise-adaptive quantum property learning while delivering practical protocols for enhancing the reliability of quantum hardware.

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TMLR Journal 2024 Journal Article

Unsupervised 3D Scene Representation Learning via Movable Object Inference

Honglin Chen
Wanhee Lee
Hong-Xing Yu
Rahul Mysore Venkatesh
Joshua B. Tenenbaum
Daniel Bear
Jiajun Wu
Daniel LK Yamins

Unsupervised, category-agnostic, object-centric 3D representation learning for complex scenes remains an open problem in computer vision. While a few recent methods can discover 3D objects from a single image, they remain struggling on scenes with diverse and complex object configurations as they discover objects mostly by appearance similarity which is insufficient for textured objects. In this work, we propose Movable Object Radiance Fields (MORF), aiming at scaling to complex scenes with diverse categories of objects. Inspired by cognitive science studies of object learning in babies, MORF learns 3D object representations via movable object inference. While obtaining 3D movable object signals requires multi-view videos of moving objects, we propose lifting a 2D movable object inference module that can be unsupervisedly pretrained on monocular videos. Thus, MORF requires only multi-view images of static training scenes. During testing, MORF can discover, reconstruct, and move unseen objects from novel categories, all from a single image of novel scenes. We propose a challenging simulated dataset with a diverse set of textured objects for training and testing. Experiments show that MORF extracts accurate object geometry and supports realistic object and scene reconstruction and editing, significantly outperforming the state-of-the-art.

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ICML Conference 2023 Conference Paper

Implicit Neural Spatial Representations for Time-dependent PDEs

Honglin Chen
Rundi Wu
Eitan Grinspun
Changxi Zheng
Peter Yichen Chen

Implicit Neural Spatial Representation (INSR) has emerged as an effective representation of spatially-dependent vector fields. This work explores solving time-dependent PDEs with INSR. Classical PDE solvers introduce both temporal and spatial discretizations. Common spatial discretizations include meshes and meshless point clouds, where each degree-of-freedom corresponds to a location in space. While these explicit spatial correspondences are intuitive to model and understand, these representations are not necessarily optimal for accuracy, memory usage, or adaptivity. Keeping the classical temporal discretization unchanged (e. g. , explicit/implicit Euler), we explore INSR as an alternative spatial discretization, where spatial information is implicitly stored in the neural network weights. The network weights then evolve over time via time integration. Our approach does not require any training data generated by existing solvers because our approach is the solver itself. We validate our approach on various PDEs with examples involving large elastic deformations, turbulent fluids, and multi-scale phenomena. While slower to compute than traditional representations, our approach exhibits higher accuracy and lower memory consumption. Whereas classical solvers can dynamically adapt their spatial representation only by resorting to complex remeshing algorithms, our INSR approach is intrinsically adaptive. By tapping into the rich literature of classic time integrators, e. g. , operator-splitting schemes, our method enables challenging simulations in contact mechanics and turbulent flows where previous neural-physics approaches struggle. Videos and codes are available on the project page: http: //www. cs. columbia. edu/cg/INSR-PDE/

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