arXiv subjects

Junan Lin

Publications and source records attributed to Junan Lin.

12 recordsLinked to original sources

arXiv2026

Multi-scale closed-loop melt pool control for LPBF via policy optimization

Laser powder bed fusion (LPBF) is a metal additive manufacturing process where temperature stabilization is of vital importance to avoid defects such as distortion and cracking. Existing control methods require manual tuning, increasing the risk of part failure when printing complex geometries. This paper introduces a dual-loop, data-driven control strategy to stabilize the surface temperature, ensuring robustness and near-optimal performance in the presence of disturbances. The proposed method integrates (i) an in-layer linear output feedback control with gains optimized through policy gradient, and (ii) a layer-to-layer feedforward control combining temperature trajectory optimization and iterative learning control. Simulation results show that the multi-scale controller effectively stabilizes the temperature even under significant model mismatch and measurement noise. Experimental results demonstrate that a simplified, hardware-constrained version of this method matches the state-of-the-art performance of in-situ data-driven methods, reducing mean tracking error by 3.4% and mean input-constraint violation by 47.5% relative to a Bayesian Optimization-tuned baseline. For this physical LPBF validation, the controller is tuned entirely offline using uncontrolled print data from a single calibration layer. Our experiments also demonstrate a new class of high-frequency excitation dynamics that result in reduced vector head swelling, opening up new avenues of research in the additive manufacturing community. This work marks one of the first successful applications of sim-to-real policy optimization in LPBF processes.

eess.SY↗

arXiv2026

Learning Over-Relaxation Policies for ADMM with Convergence Guarantees

The Alternating Direction Method of Multipliers (ADMM) is a widely used method for structured convex optimization, and its practical performance depends strongly on the choice of penalty and relaxation parameters. Motivated by settings such as Model Predictive Control (MPC), where one repeatedly solves related optimization problems with fixed structure and changing parameter values, we propose learning online updates of the relaxation parameter to improve performance on problem classes of interest. This choice is computationally attractive in OSQP-like architectures, since adapting relaxation does not trigger the matrix refactorizations associated with penalty updates. We establish convergence guarantees for ADMM with time-varying penalty and relaxation parameters under mild assumptions, and show on benchmark quadratic programs that the resulting learned policies improve both iteration count and wall-clock time over baseline OSQP.

math.OC↗

arXiv2025

Audio Does Matter: Importance-Aware Multi-Granularity Fusion for Video Moment Retrieval

Video Moment Retrieval (VMR) aims to retrieve a specific moment semantically related to the given query. To tackle this task, most existing VMR methods solely focus on the visual and textual modalities while neglecting the complementary but important audio modality. Although a few recent works try to tackle the joint audio-vision-text reasoning, they treat all modalities equally and simply embed them without fine-grained interaction for moment retrieval. These designs are counter-practical as: Not all audios are helpful for video moment retrieval, and the audio of some videos may be complete noise or background sound that is meaningless to the moment determination. To this end, we propose a novel Importance-aware Multi-Granularity fusion model (IMG), which learns to dynamically and selectively aggregate the audio-vision-text contexts for VMR. Specifically, after integrating the textual guidance with vision and audio separately, we first design a pseudo-label-supervised audio importance predictor that predicts the importance score of the audio, and accordingly assigns weights to mitigate the interference caused by noisy audio. Then, we design a multi-granularity audio fusion module that adaptively fuses audio and visual modalities at local-, event-, and global-level, fully capturing their complementary contexts. We further propose a cross-modal knowledge distillation strategy to address the challenge of missing audio modality during inference. To evaluate our method, we further construct a new VMR dataset, i.e., Charades-AudioMatter, where audio-related samples are manually selected and re-organized from the original Charades-STA to validate the model's capability in utilizing audio modality. Extensive experiments validate the effectiveness of our method, achieving state-of-the-art with audio-video fusion in VMR methods. Our code is available at https://github.com/HuiGuanLab/IMG.

cs.IR↗

arXiv2025

Bounding Eigenstate Overlap from Hamiltonian Moments: Success Probability Guarantees for Quantum Phase Estimation

Estimating the overlap between a prepared state and a target eigenstate is crucial for the efficiency of quantum phase estimation (QPE), since QPE succeeds with probability equal to this overlap. We present a systematically improvable method to compute certified upper and lower bounds on such overlaps using a finite set of Hamiltonian moments. Our approach constructs optimal polynomial upper/lower bounds on an energy-window indicator and evaluates them through linear and semidefinite programs, yielding the tightest bounds consistent with the available moment and spectral-interval information. We demonstrate the method on strongly correlated molecular Hamiltonians and study the impact of approximate moments obtained from tensor-network contractions. The resulting bounds provide a practical pre-QPE screening tool for selecting initial states and can be implemented with either classical moment computation or quantum expectation estimation.

quant-ph↗

arXiv2024

Thermodynamic Analysis of Algorithmic Cooling Protocols: Efficiency Metrics and Improved Designs

Algorithmic cooling (AC) protocols have been predominantly studied for their cooling capabilities, with limited attention paid to their thermodynamic properties. This work explores a novel perspective by analyzing a broad family of AC protocols from a thermodynamic standpoint. First, we give an in-depth review and formal classification of standard AC protocols. Leveraging the transfer matrix formalism, we achieve a consistent calculation of performance metrics, encompassing both cooling limits and target state evolution. We obtained a unification of these diverse cooling limits into a single, coherent mathematical expression, streamlining comparative analyses. Then, to assess the efficiency of coherent cooling protocols, we introduce two generic metrics: the coefficient of performance $K$ and the Landauer Ratio $R_L$, and establish a direct interrelation. Applying these metrics, we thoroughly evaluate selected AC protocols, highlighting their relative strengths. Finally, we propose improved versions of AC protocols that exhibit enhanced thermodynamic performance, achieving desired target temperatures with lower work inputs. This research contributes to a deeper understanding of AC protocols and provides valuable insights for designing efficient cooling strategies in various applications.

quant-ph↗

arXiv2023

CaSAR: Contact-aware Skeletal Action Recognition

Skeletal Action recognition from an egocentric view is important for applications such as interfaces in AR/VR glasses and human-robot interaction, where the device has limited resources. Most of the existing skeletal action recognition approaches use 3D coordinates of hand joints and 8-corner rectangular bounding boxes of objects as inputs, but they do not capture how the hands and objects interact with each other within the spatial context. In this paper, we present a new framework called Contact-aware Skeletal Action Recognition (CaSAR). It uses novel representations of hand-object interaction that encompass spatial information: 1) contact points where the hand joints meet the objects, 2) distant points where the hand joints are far away from the object and nearly not involved in the current action. Our framework is able to learn how the hands touch or stay away from the objects for each frame of the action sequence, and use this information to predict the action class. We demonstrate that our approach achieves the state-of-the-art accuracy of 91.3% and 98.4% on two public datasets, H2O and FPHA, respectively.

cs.CV↗

arXiv2022

Algorithmic cooling for resolving state preparation and measurement errors in quantum computing

State preparation and measurement errors are commonly regarded as indistinguishable. The problem of distinguishing state preparation (SPAM) errors from measurement errors is important to the field of characterizing quantum processors. In this work, we propose a method to separately characterize SPAM errors using a novel type of algorithmic cooling protocol called measurement-based algorithmic cooling (MBAC). MBAC assumes the ability to perform (potentially imperfect) projective measurements on individual qubits, which is available on many modern quantum processors. We demonstrate that MBAC can significantly reduce state preparation error under realistic assumptions, with a small overhead that can be upper bounded by measurable quantities. Thus, MBAC can be a valuable tool not only for benchmarking near-term quantum processors, but also for improving the performance of quantum processors in an algorithmic manner.

quant-ph↗

arXiv2021

NISQ: Error Correction, Mitigation, and Noise Simulation

Error-correcting codes were invented to correct errors on noisy communication channels. Quantum error correction (QEC), however, may have a wider range of uses, including information transmission, quantum simulation/computation, and fault-tolerance. These invite us to rethink QEC, in particular, about the role that quantum physics plays in terms of encoding and decoding. The fact that many quantum algorithms, especially near-term hybrid quantum-classical algorithms, only use limited types of local measurements on quantum states, leads to various new techniques called Quantum Error Mitigation (QEM). This work examines the task of QEM from several perspectives. Using some intuitions built upon classical and quantum communication scenarios, we clarify some fundamental distinctions between QEC and QEM. We then discuss the implications of noise invertibility for QEM, and give an explicit construction called Drazin-inverse for non-invertible noise, which is trace preserving while the commonly-used Moore-Penrose pseudoinverse may not be. Finally, we study the consequences of having an imperfect knowledge about the noise, and derive conditions when noise can be reduced using QEM.

quant-ph↗

arXiv2021

Quantum information and beyond -- with quantum candies

The field of quantum information is becoming more known to the general public. However, effectively demonstrating the concepts underneath quantum science and technology to the general public can be a challenging job. We investigate, extend, and greatly expand here "quantum candies" (invented by Jacobs), a pedagogical model for intuitively describing some basic concepts in quantum information, including quantum bits, complementarity, the no-cloning principle, and entanglement. Following Jacob's quantum candies description of the well-known quantum key distribution protocol BB84, we explicitly demonstrate additional quantum cryptography protocols and quantum communication protocols, using generalized quantum candies (including correlated pairs of qandies). These demonstrations are done in an approachable manner, that can be explained to high-school students, without using the hard-to-grasp concept of superpositions and its mathematics. The intuitive model we investigate has a fascinating overlap with some of the most basic features of quantum theory. Hence, it can be a valuable tool for science and engineering educators who would like to help the general public to gain more insights into quantum science and technology. For the experts, the model we present, due to not employing quantum superpositions, enables - in some sense - extending far beyond quantum theory. Most remarkably, "quantum" candies of some unique type can be defined, such that non-local boxes (of the Popescu-Rohrlich type) as well as regular (correlated) quantum candies can be generated by a single `"quantum" candies machine.

physics.ed-ph↗

arXiv2020

Quantum Candies and Quantum Cryptography

physics.ed-ph↗

arXiv2019

Independent State and Measurement Characterization for Quantum Computers

Correctly characterizing state preparation and measurement (SPAM) processes is a necessary step towards building reliable quantum processing units (QPUs). In this work, we discuss the subtleties behind separately measuring SPAM errors. We propose a protocol that can separately estimate SPAM errors, in the case where quantum gates are ideal. In the case where the quantum gates are imperfect, we derive bounds on the estimated SPAM error rates, based on gate error measures which can be estimated independently of SPAM processes. Our method shows that the gauge ambiguity in characterizing SPAM operations can be resolved, by assuming that there exists one qubit whose initial state is uncorrelated with other qubits in a QPU. We test the protocol on a publicly available five-qubit QPU and demonstrate its validity by comparing our results with simulations.

quant-ph↗

arXiv2018

On the freedom in representing quantum operations

We discuss the effects of a gauge freedom in representing quantum information processing devices, and its implications for characterizing these devices. We demonstrate with experimentally relevant examples that there exists equally valid descriptions of the same experiment which distribute errors differently among objects in a gate-set, leading to different error rates. Consequently, it can be misleading to attach a concrete operational meaning to figures of merit for individual gate-set elements. We propose an alternative operational figure of merit for a gate-set, the mean variation error, and a protocol for measuring this figure.

quant-ph↗