{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T11:56:46Z","timestamp":1726142206264},"reference-count":62,"publisher":"Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften","license":[{"start":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T00:00:00Z","timestamp":1674691200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["quantum-journal.org"],"crossmark-restriction":false},"short-container-title":["Quantum"],"abstract":"We introduce a variational quantum algorithm to solve unconstrained black box binary optimization problems, i.e., problems in which the objective function is given as black box. This is in contrast to the typical setting of quantum algorithms for optimization where a classical objective function is provided as a given Quadratic Unconstrained Binary Optimization problem and mapped to a sum of Pauli operators. Furthermore, we provide theoretical justification for our method based on convergence guarantees of quantum imaginary time evolution.To investigate the performance of our algorithm and its potential advantages, we tackle a challenging real-world optimization problem: feature selection<\/mml:mtext><\/mml:mrow><\/mml:math>. This refers to the problem of selecting a subset of relevant features to use for constructing a predictive model such as fraud detection. Optimal feature selection---when formulated in terms of a generic loss function---offers little structure on which to build classical heuristics, thus resulting primarily in \u2018greedy methods\u2019. This leaves room for (near-term) quantum algorithms to be competitive to classical state-of-the-art approaches. We apply our quantum-optimization-based feature selection algorithm, termed VarQFS, to build a predictive model for a credit risk data set with 20<\/mml:mn><\/mml:math> and 59<\/mml:mn><\/mml:math> input features (qubits) and train the model using quantum hardware and tensor-network-based numerical simulations, respectively. We show that the quantum method produces competitive and in certain aspects even better performance compared to traditional feature selection techniques used in today's industry.<\/jats:p>","DOI":"10.22331\/q-2023-01-26-909","type":"journal-article","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T17:25:32Z","timestamp":1674753932000},"page":"909","update-policy":"http:\/\/dx.doi.org\/10.22331\/q-crossmark-policy-page","source":"Crossref","is-referenced-by-count":10,"title":["Variational quantum algorithm for unconstrained black box binary optimization: Application to feature selection"],"prefix":"10.22331","volume":"7","author":[{"given":"Christa","family":"Zoufal","sequence":"first","affiliation":[{"name":"IBM Quantum, IBM Research Europe \u2013 Zurich"}]},{"given":"Ryan V.","family":"Mishmash","sequence":"additional","affiliation":[{"name":"IBM Quantum, Almaden Research Center \u2013 Almaden"}]},{"given":"Nitin","family":"Sharma","sequence":"additional","affiliation":[{"name":"PayPal \u2013 San Jose"}]},{"given":"Niraj","family":"Kumar","sequence":"additional","affiliation":[{"name":"PayPal \u2013 San Jose"}]},{"given":"Aashish","family":"Sheshadri","sequence":"additional","affiliation":[{"name":"PayPal \u2013 San Jose"}]},{"given":"Amol","family":"Deshmukh","sequence":"additional","affiliation":[{"name":"IBM Quantum, Thomas J. Watson Research Center \u2013 Yorktown Heights"}]},{"given":"Noelle","family":"Ibrahim","sequence":"additional","affiliation":[{"name":"IBM Quantum, Thomas J. Watson Research Center \u2013 Yorktown Heights"}]},{"given":"Julien","family":"Gacon","sequence":"additional","affiliation":[{"name":"IBM Quantum, IBM Research \u2013 Zurich"},{"name":"Institute of Physics, \u00c9cole Polytechnique F\u00e9d\u00e9rale de Lausanne (EPFL)"}]},{"given":"Stefan","family":"Woerner","sequence":"additional","affiliation":[{"name":"IBM Quantum, IBM Research \u2013 Zurich"}]}],"member":"9598","published-online":{"date-parts":[[2023,1,26]]},"reference":[{"key":"0","doi-asserted-by":"publisher","unstructured":"E. Farhi, J. Goldstone, and S. Gutmann. A Quantum Approximate Optimization Algorithm. arXiv preprint - arXiv:1411.402, 2014. DOI: https:\/\/doi.org\/10.48550\/arXiv.1411.4028.","DOI":"10.48550\/arXiv.1411.4028"},{"key":"1","doi-asserted-by":"publisher","unstructured":"N. Moll et al. 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