---
title: "Distribution-Aware Energy Minimization: Physical-Inspired Efficient Active Learning and Quantum Potentials"
authors:
  - "Zhicheng Yao"
  - "Wenguo Yang"
  - "Yancheng Chen"
  - "Dun Ma"
  - "Shengminjie Chen"
  - "Xiaoming Sun"
doi: "10.24963/ijcai.2026/225"
url: "https://ijcai.org/proceedings/2026/225"
pdf: "https://ijcai.org/proceedings/2026/0225.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "2019-2027"
track: "Main Track"
keywords:
  - "Computer Vision: Efficiency and Optimization"
  - "Machine Learning: Supervised Learning"
  - "Machine Learning: Active learning"
---

# Distribution-Aware Energy Minimization: Physical-Inspired Efficient Active Learning and Quantum Potentials

Zhicheng Yao, Wenguo Yang, Yancheng Chen, Dun Ma, Shengminjie Chen, Xiaoming Sun

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 2019-2027. DOI: [10.24963/ijcai.2026/225](https://doi.org/10.24963/ijcai.2026/225)

## Abstract

Active learning aims to maximize model performance with minimal annotation costs by selecting the most informative samples from large unlabeled pools, which often face a budget dilemma: uncertainty-based methods induce redundancy under low budgets, while representativeness-based methods struggle to mine challenging samples under high budgets. Although some heuristic parameter interpolation schemes attempt to bridge this gap, such strategies suffer from a misalignment between theoretical assumptions and real-world distributions, failing to achieve a good exploration-exploitation balance across all scenarios. In this paper, we propose a general active learning framework based on Distribution-Aware Energy Minimization, which reformulates sample selection as minimizing the energy function for the distributional discrepancy between the selected subset and the global uncertainty field. This physical-inspired perspective naturally derives a Hamiltonian comprising attractive terms and repulsive terms, mathematically achieving an intrinsic and dynamic balance between uncertainty and representativeness. Furthermore, we transform the optimization objective to the ground-state search of an Ising Model, enabling efficient solutions via Coherent Ising Machine. Extensive numerical experiments show that our method outperforms prior state-of-the-art methods across multi-budget regimes on several benchmark datasets. Validation on a real quantum hardware also demonstrates the potential for quantum computer in future large-scale selection tasks.