Collaborative Filtering using Variational Quantum Hopfield Associative Memory

2025-08-12

AI Summary

This work addresses the practicality bottleneck of quantum recommendation systems under real-world data and noisy conditions. Methodologically, it proposes a hybrid architecture integrating a variational quantum Hopfield associative memory (VQHAM) with deep neural networks: (1) K-means clustering on MovieLens 1M user behavior generates user prototypes, encoded as polar quantum states; (2) a single-qubit update mechanism is designed to drastically reduce quantum resource overhead; and (3) classical neural networks are jointly trained for end-to-end collaborative filtering. The key contribution is the first integration of VQHAM into the recommendation pipeline with hardware-aware optimization. Experiments demonstrate strong performance: under ideal conditions, the model achieves an ROC score of 0.9795 and accuracy of 88.41%; under Qiskit AER noise simulation, ROC remains at 0.9177—confirming robustness and advancing quantum recommendation toward practical deployment.

Technology Category

• Machine Learning: Quantum Machine Learning
• Data Mining & Knowledge Management: Recommender Systems
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Quantum computing, with its ability to do exponentially faster computation compared to classical systems, has found novel applications in various fields such as machine learning and recommendation systems. Quantum Machine Learning (QML), which integrates quantum computing with machine learning techniques, presents powerful new tools for data processing and pattern recognition. This paper proposes a hybrid recommendation system that combines Quantum Hopfield Associative Memory (QHAM) with deep neural networks to improve the extraction and classification on the MovieLens 1M dataset. User archetypes are clustered into multiple unique groups using the K-Means algorithm and converted into polar patterns through the encoder's activation function. These polar patterns are then integrated into the variational QHAM-based hybrid recommendation model. The system was trained using the MSE loss over 35 epochs in an ideal environment, achieving an ROC value of 0.9795, an accuracy of 0.8841, and an F-1 Score of 0.8786. Trained with the same number of epochs in a noisy environment using a custom Qiskit AER noise model incorporating bit-flip and readout errors with the same probabilities as in real quantum hardware, it achieves an ROC of 0.9177, an accuracy of 0.8013, and an F-1 Score equal to 0.7866, demonstrating consistent performance. Additionally, we were able to optimize the qubit overhead present in previous QHAM architectures by efficiently updating only one random targeted qubit. This research presents a novel framework that combines variational quantum computing with deep learning, capable of dealing with real-world datasets with comparable performance compared to purely classical counterparts. Additionally, the model can perform similarly well in noisy configurations, showcasing a steady performance and proposing a promising direction for future usage in recommendation systems.

Problem

• Proposing a hybrid quantum-classical recommendation system for MovieLens dataset
• Integrating Quantum Hopfield Memory with deep neural networks
• Optimizing qubit overhead in quantum architectures for recommendations

Innovation

• Hybrid quantum-classical recommendation system with QHAM
• Variational QHAM integrated with deep neural networks
• Optimized qubit overhead by updating one random qubit