量子机器学习用于内容推荐，参数仅为 BiLSTM 的 0.26%
A Quantum Machine Learning Approach to Content-Based Media Recommendation
BBC iPlayer 数据上做的量子推荐系统，参数只有经典模型四百分之一还能打平，甚至真跑上了 Quantinuum 的量子硬件，做推荐或量子计算的都该看看。
论文基于 Quantum Natural Language Processing（QNLP）框架，把 BBC iPlayer 一周的节目简介映射到变分量子电路，通过成对相似度学习训练推荐模型。在相同数据集和目标下，量子模型与经典 BiLSTM 基线表现相当，但参数量仅 47k，对 17.8M。当 BiLSTM 被限制到同等参数预算时，其 Mean Average Precision 下降 3.63 个百分点。研究团队还将训练好的电路部署到 Quantinuum System Model H2 量子硬件及模拟器上，用 SWAP-test 协议验证了相似度信号在有噪声条件下仍然保留。
A Quantum Machine Learning Approach to Content-Based Media Recommendation
We use quantum machine learning techniques to develop a content-based media recommender system and evaluate its performance on TV programmes using textual descriptions from one week of BBC iPlayer data. Grounded in the Quantum Natural Language Processing framework (QNLP), programme synopses are mapped to variational quantum circuits using their semantic structure. The parameters of these circuits are trained via a supervised pairwise similarity learning task. We benchmark the quantum recommender against a classical BiLSTM baseline trained on the same dataset and objective. While both models achieve comparable performance on the similarity learning task, the quantum model requires significantly fewer parameters (47k vs. 17.8M). When the BiLSTM is constrained to a matching parameter budget, its performance degrades substantially (by 3.63 percentage points in Mean Average Precision (MAP)), indicating that the structured quantum representation provides a highly parameter-efficient alternative in this setting. We demonstrate the feasibility of executing the trained models on quantum hardware by deploying the circuits on Quantinuum's System Model H2 (and accompanying emulator) using a SWAP-test-based protocol. The results confirm that the similarity signal is preserved under realistic, noisy conditions. Taken together, these findings highlight the potential of variational quantum circuits for recommendation tasks and represent a practical step toward their real-world deployment.
