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Quantum Optimization

Implementation of Trained Factorization Machine Recommendation System on Quantum Annealer

arXiv
Authors: Chen-Yu Liu, Hsin-Yu Wang, Pei-Yen Liao, Ching-Jui Lai, Min-Hsiu Hsieh

Year

2022

Paper ID

58110

Status

Preprint

Abstract Read

~2 min

Abstract Words

121

Citations

N/A

Abstract

Factorization Machine (FM) is the most commonly used model to build a recommendation system since it can incorporate side information to improve performance. However, producing item suggestions for a given user with a trained FM is time-consuming. It requires a run-time of O\((Nm log Nm\)2), where Nm is the number of items in the dataset. To address this problem, we propose a quadratic unconstrained binary optimization (QUBO) scheme to combine with FM and apply quantum annealing (QA) computation. Compared to classical methods, this hybrid algorithm provides a faster than quadratic speedup in finding good user suggestions. We then demonstrate the aforementioned computational advantage on current NISQ hardware by experimenting with a real example on a D-Wave annealer.

Why This Paper Matters

  • This paper contributes to the Quantum Optimization research area in the Quantum Articles archive.
  • It adds a 2022 reference point for readers tracking recent quantum research.
  • Factorization Machine (FM) is the most commonly used model to build a recommendation system since it can incorporate side information to improve performance.

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2210.12953 [2] arXiv https://arxiv.org/abs/2210.12953 [3] Publisher https://arxiv.org/abs/2210.12953

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