Quantum Semi-Random Forests for Qubit-Efficient Recommender Systems
This work addresses the challenge of making quantum recommenders feasible on current NISQ devices for researchers and practitioners in quantum machine learning, though it is incremental in improving qubit efficiency.
The paper tackles the problem of high qubit requirements in quantum recommender systems by developing a hybrid algorithm that compresses item tags and uses a Quantum semi-Random Forest on just five qubits, achieving performance similar to state-of-the-art methods on ICM-150/500 benchmarks.
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.