MolPaQ: Modular Quantum-Classical Patch Learning for Interpretable Molecular Generation
Fuente:
arXiv
Saved in:
| Main Authors: | Naqvi, Syed Rameez, Peng, Lu |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
GlassMol: Interpretable Molecular Property Prediction with Concept Bottleneck Models
by: Rivera, Oscar, et al.
Published: (2026)
by: Rivera, Oscar, et al.
Published: (2026)
MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs
by: Zhao, Guojiang, et al.
Published: (2025)
by: Zhao, Guojiang, et al.
Published: (2025)
PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
by: Park, Jinju, et al.
Published: (2026)
by: Park, Jinju, et al.
Published: (2026)
Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-directed Molecular Generation
by: Park, Jinyeong, et al.
Published: (2024)
by: Park, Jinyeong, et al.
Published: (2024)
FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning
by: Liu, Sizhe, et al.
Published: (2024)
by: Liu, Sizhe, et al.
Published: (2024)
$\texttt{MiniMol}$: A Parameter-Efficient Foundation Model for Molecular Learning
by: Kläser, Kerstin, et al.
Published: (2024)
by: Kläser, Kerstin, et al.
Published: (2024)
MolEvolve: LLM-Guided Evolutionary Search for Interpretable Molecular Optimization
by: Chen, Xiangsen, et al.
Published: (2026)
by: Chen, Xiangsen, et al.
Published: (2026)
MolMix: A Simple Yet Effective Baseline for Multimodal Molecular Representation Learning
by: Manolache, Andrei, et al.
Published: (2024)
by: Manolache, Andrei, et al.
Published: (2024)
MolWorld: Molecule World Models for Actionable Molecular Optimization
by: Qiao, Yang, et al.
Published: (2026)
by: Qiao, Yang, et al.
Published: (2026)
Uni-Mol2: Exploring Molecular Pretraining Model at Scale
by: Ji, Xiaohong, et al.
Published: (2024)
by: Ji, Xiaohong, et al.
Published: (2024)
MolRGen: A Training and Evaluation Setting for De Novo Molecular Generation with Reasonning Models
by: Formont, Philippe, et al.
Published: (2026)
by: Formont, Philippe, et al.
Published: (2026)
PatchDecomp: Interpretable Patch-Based Time Series Forecasting
by: Tomioka, Hiroki, et al.
Published: (2026)
by: Tomioka, Hiroki, et al.
Published: (2026)
MetaMolGen: A Neural Graph Motif Generation Model for De Novo Molecular Design
by: Yan, Zimo, et al.
Published: (2025)
by: Yan, Zimo, et al.
Published: (2025)
Mol-Debate: Multi-Agent Debate Improves Structural Reasoning in Molecular Design
by: Zhang, Wengyu, et al.
Published: (2026)
by: Zhang, Wengyu, et al.
Published: (2026)
InstructMol: Multi-Modal Integration for Building a Versatile and Reliable Molecular Assistant in Drug Discovery
by: Cao, He, et al.
Published: (2023)
by: Cao, He, et al.
Published: (2023)
MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization
by: Wang, Ziqing, et al.
Published: (2026)
by: Wang, Ziqing, et al.
Published: (2026)
MolFusion: Multimodal Fusion Learning for Molecular Representations via Multi-granularity Views
by: Cai, Muzhen, et al.
Published: (2024)
by: Cai, Muzhen, et al.
Published: (2024)
MolTC: Towards Molecular Relational Modeling In Language Models
by: Fang, Junfeng, et al.
Published: (2024)
by: Fang, Junfeng, et al.
Published: (2024)
Smart Sampling: Self-Attention and Bootstrapping for Improved Ensembled Q-Learning
by: Khan, Muhammad Junaid, et al.
Published: (2024)
by: Khan, Muhammad Junaid, et al.
Published: (2024)
Training Neural Networks for Modularity aids Interpretability
by: Golechha, Satvik, et al.
Published: (2024)
by: Golechha, Satvik, et al.
Published: (2024)
FlashMol: High-Quality Molecule Generation in as Few as Four Steps
by: Wei, Xinyuan, et al.
Published: (2026)
by: Wei, Xinyuan, et al.
Published: (2026)
KnowMol: Advancing Molecular Large Language Models with Multi-Level Chemical Knowledge
by: Yang, Zaifei, et al.
Published: (2025)
by: Yang, Zaifei, et al.
Published: (2025)
FocusLearn: Fully-Interpretable, High-Performance Modular Neural Networks for Time Series
by: Su, Qiqi, et al.
Published: (2023)
by: Su, Qiqi, et al.
Published: (2023)
AttriLens-Mol: Attribute Guided Reinforcement Learning for Molecular Property Prediction with Large Language Models
by: Lin, Xuan, et al.
Published: (2025)
by: Lin, Xuan, et al.
Published: (2025)
Embedded Quantum Machine Learning in Embedded Systems: Feasibility, Hybrid Architectures, and Quantum Co-Processors
by: Dey, Somdip, et al.
Published: (2026)
by: Dey, Somdip, et al.
Published: (2026)
3D Optimization for AI Inference Scaling: Balancing Accuracy, Cost, and Latency
by: Jung, Minseok, et al.
Published: (2025)
by: Jung, Minseok, et al.
Published: (2025)
Pathway-based Progressive Inference (PaPI) for Energy-Efficient Continual Learning
by: Gaurav, Suyash, et al.
Published: (2025)
by: Gaurav, Suyash, et al.
Published: (2025)
MolLangBench: A Comprehensive Benchmark for Language-Prompted Molecular Structure Recognition, Editing, and Generation
by: Cai, Feiyang, et al.
Published: (2025)
by: Cai, Feiyang, et al.
Published: (2025)
Mol-LLM: Multimodal Generalist Molecular LLM with Improved Graph Utilization
by: Lee, Chanhui, et al.
Published: (2025)
by: Lee, Chanhui, et al.
Published: (2025)
Mildly Conservative Q-Learning for Offline Reinforcement Learning
by: Lyu, Jiafei, et al.
Published: (2022)
by: Lyu, Jiafei, et al.
Published: (2022)
Quantum-Enhanced Generative Adversarial Networks: Comparative Analysis of Classical and Hybrid Quantum-Classical Generative Adversarial Networks
by: Goh, Kun Ming
Published: (2025)
by: Goh, Kun Ming
Published: (2025)
A Unified Theory of Compositionality, Modularity, and Interpretability in Markov Decision Processes
by: Ringstrom, Thomas J., et al.
Published: (2025)
by: Ringstrom, Thomas J., et al.
Published: (2025)
FedPaI: Achieving Extreme Sparsity in Federated Learning via Pruning at Initialization
by: Wang, Haonan, et al.
Published: (2025)
by: Wang, Haonan, et al.
Published: (2025)
Hybrid Quantum-Classical Spatiotemporal Forecasting for 3D Cloud Fields
by: Wang, Fu, et al.
Published: (2026)
by: Wang, Fu, et al.
Published: (2026)
Q-resafe: Assessing Safety Risks and Quantization-aware Safety Patching for Quantized Large Language Models
by: Chen, Kejia, et al.
Published: (2025)
by: Chen, Kejia, et al.
Published: (2025)
Towards Empirical Interpretation of Internal Circuits and Properties in Grokked Transformers on Modular Polynomials
by: Furuta, Hiroki, et al.
Published: (2024)
by: Furuta, Hiroki, et al.
Published: (2024)
Learning More Expressive General Policies for Classical Planning Domains
by: Ståhlberg, Simon, et al.
Published: (2024)
by: Ståhlberg, Simon, et al.
Published: (2024)
Q-Newton: Hybrid Quantum-Classical Scheduling for Accelerating Neural Network Training with Newton's Gradient Descent
by: Li, Pingzhi, et al.
Published: (2024)
by: Li, Pingzhi, et al.
Published: (2024)
PaCA: Partial Connection Adaptation for Efficient Fine-Tuning
by: Woo, Sunghyeon, et al.
Published: (2025)
by: Woo, Sunghyeon, et al.
Published: (2025)
Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planning
by: Bettker, R. V., et al.
Published: (2022)
by: Bettker, R. V., et al.
Published: (2022)
Similar Items
-
GlassMol: Interpretable Molecular Property Prediction with Concept Bottleneck Models
by: Rivera, Oscar, et al.
Published: (2026) -
MolReasoner: Toward Effective and Interpretable Reasoning for Molecular LLMs
by: Zhao, Guojiang, et al.
Published: (2025) -
PaAno: Patch-Based Representation Learning for Time-Series Anomaly Detection
by: Park, Jinju, et al.
Published: (2026) -
Mol-AIR: Molecular Reinforcement Learning with Adaptive Intrinsic Rewards for Goal-directed Molecular Generation
by: Park, Jinyeong, et al.
Published: (2024) -
FlexMol: A Flexible Toolkit for Benchmarking Molecular Relational Learning
by: Liu, Sizhe, et al.
Published: (2024)