用5个量子比特实现高效推荐，压缩标签并优化特征选择。
Quantum Semi-Random Forests for Qubit-Efficient Recommender Systems
- 用SVD与k-means构建1000原子词典，保留97%以上方差
- 深度3的QAOA求解2020变量的QUBO，选出5个关键特征
- 5量子比特的量子半随机森林匹配全特征基线表现
现代推荐系统为每个物品分配数百个稀疏语义标签，但多数量子方案仍采用每标签一量子比特映射，需超百个量子比特，远超当前噪声中等规模量子（NISQ）设备能力，且易导致深层、误差放大电路。本文提出三阶段混合机器学习算法：先通过SVD压缩与k-means聚类构建1000原子词典（保留>97%方差），再以深度3的QAOA求解2020变量的QUBO问题，选出5个最优原子特征；最后使用基于5个量子比特的量子半随机森林（QsRF）进行评分。在ICM-150/500数据集上，该方法性能接近现有最先进水平。
原文摘要 · Abstract (English)
Modern recommenders describe each item with hundreds of sparse semantic tags, yet most quantum pipelines still map one qubit per tag, demanding well beyond one hundred qubits, far out of reach for current noisy-intermediate-scale quantum (NISQ) devices and prone to deep, error-amplifying circuits. We close this gap with a three-stage hybrid machine learning algorithm that compresses tag profiles, optimizes feature selection under a fixed qubit budget via QAOA, and scores recommendations with a Quantum semi-Random Forest (QsRF) built on just five qubits, while performing similarly to the state-of-the-art methods. Leveraging SVD sketching and k-means, we learn a 1000-atom dictionary ($>$97 \% variance), then solve a 2020 QUBO via depth-3 QAOA to select 5 atoms. A 100-tree QsRF trained on these codes matches full-feature baselines on ICM-150/500.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。