Feature Selection for Recommender Systems with Quantum Computing

@article{Nembrini2021FeatureSF,
  title={Feature Selection for Recommender Systems with Quantum Computing},
  author={Riccardo Nembrini and Maurizio Ferrari Dacrema and Paolo Cremonesi},
  journal={Entropy},
  year={2021},
  volume={23}
}
The promise of quantum computing to open new unexplored possibilities in several scientific fields has been long discussed, but until recently the lack of a functional quantum computer has confined this discussion mostly to theoretical algorithmic papers. It was only in the last few years that small but functional quantum computers have become available to the broader research community. One paradigm in particular, quantum annealing, can be used to sample optimal solutions for a number of NP… 
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12 Citations
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Methods Citations
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12 Citations

Optimizing the Selection of Recommendation Carousels with Quantum Computing

This paper proposes a formulation of the carousel selection problem for black box recommenders, that can be solved effectively on a quantum annealer and has the advantage of being simple.

Are Quantum Computers Practical Yet? A Case for Feature Selection in Recommender Systems using Tensor Networks

Collaborative filtering models generally perform better than content-based filtering models and do not require careful feature engineering. However, in the cold-start scenario collaborative…

Towards Recommender Systems with Community Detection and Quantum Computing

This work aims to experimentally explore the feasibility of using currently available quantum computers, based on the Quantum Annealing paradigm, to build a recommender system exploiting community detection, and shows promise in its ability to support new recommendation models and to bring improved scalability as technology evolves.

A Survey on Quantum Computing for Recommendation Systems

An overview of the current state of the art in the literature is given, outlining the different proposed methodologies and techniques and highlighting the challenges that arise from this new approach to the recommendation systems domain.

Towards Feature Selection for Ranking and Classification Exploiting Quantum Annealers

The feasibility of using currently available quantum computing architectures to solve some quadratic feature selection algorithms for both ranking and classification is explored and the effectiveness obtained is comparable to that of classical solvers, indicating that quantum computers are now reliable enough to tackle interesting problems.

Implementation of Trained Factorization Machine Recommendation System on Quantum Annealer

This work proposes a quadratic unconstrained binary optimization (QUBO) scheme to combine with FM and apply quantum annealing (QA) computation and demonstrates the aforementioned computational advantage on current NISQ hardware by experimenting with a real example on a D-Wave annealer.

An Advantage Using Feature Selection with a Quantum Annealer

This paper tests this intuition against classical methods by utilizing open-source data sets and evaluating the efficacy of each trained statistical model well-known prediction algorithms and results display an advantage utilizing the features selected from the algorithm that leveraged QA.

How to Solve Combinatorial Optimization Problems Using Real Quantum Machines: A Recent Survey

This survey analyzes recent studies that solve real-scale COPs using quantum annealers and discusses how to reduce the size of the COP to be input to overcome the hardware limitations of the existing quantumAnnealer.

Evaluating the job shop scheduling problem on a D-wave quantum annealer

Job Shop Scheduling is a combinatorial optimization problem of particular importance for production environments where the goal is to complete a production task in the shortest possible time given…

Virtual Network Function Embedding with Quantum Annealing

This work proposes a Quadratic Unconstrained Binary Optimisation (QUBO) formulation of this embedding process, exploring the implementation possibilities on D-Wave’s Quantum Annealers.

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