Quantum machine learning cuts parameters in media recommendations
A Quantum Machine Learning Approach to Content-Based Media Recommendation
Machine Learning
Summary
Making good recommendations about TV shows or movies involves understanding their content and comparing it to what people like. The authors used quantum machine learning to represent show descriptions in a compact, structured way and trained their system to find similar shows. Their quantum method performed just as well as a well-known classical technique but used far fewer parameters, meaning it is more efficient. They also tested their quantum model on real quantum hardware, showing it can work even with current noisy devices.
What this means in practice
- •For streaming platform engineers: Build efficient content-based recommendation models with fewer parameters while maintaining accuracy using quantum-inspired representations.
- •For quantum hardware developers: Deploy and test variational quantum machine learning circuits for similarity-based tasks under realistic noise conditions on quantum devices.
Tested on one dataset.
Authors
Mina Abbaszadeh, Ouissal Moumou, Anna Pearson, Bob Coecke, Rob Cooper, Alessandro Piscopo, Mehrnoosh Sadrzadeh
Abstract
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.