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The algorithm takes as its input a suitable quantum description of an arbitrary SOCP and outputs a classical description of a <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mi>\u03b4<\/mml:mi><\/mml:math>-approximate <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mi>\u03f5<\/mml:mi><\/mml:math>-optimal solution of the given problem.Furthermore, we perform numerical simulations to determine the values of the aforementioned parameters when solving the SOCP up to a fixed precision <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mi>\u03f5<\/mml:mi><\/mml:math>. We present experimental evidence that in this case our quantum algorithm exhibits a polynomial speedup over the best classical algorithms for solving general SOCPs that run in time <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mi>O<\/mml:mi><mml:mo stretchy=\"false\">(<\/mml:mo><mml:msup><mml:mi>n<\/mml:mi><mml:mrow class=\"MJX-TeXAtom-ORD\"><mml:mi>\u03c9<\/mml:mi><mml:mo>+<\/mml:mo><mml:mn>0.5<\/mml:mn><\/mml:mrow><\/mml:msup><mml:mo stretchy=\"false\">)<\/mml:mo><\/mml:math> (here, <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mi>\u03c9<\/mml:mi><\/mml:math> is the matrix multiplication exponent, with a value of roughly <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mn>2.37<\/mml:mn><\/mml:math> in theory, and up to <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mn>3<\/mml:mn><\/mml:math> in practice). For the case of random SVM (support vector machine) instances of size <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mi>O<\/mml:mi><mml:mo stretchy=\"false\">(<\/mml:mo><mml:mi>n<\/mml:mi><mml:mo stretchy=\"false\">)<\/mml:mo><\/mml:math>, the quantum algorithm scales as <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mi>O<\/mml:mi><mml:mo stretchy=\"false\">(<\/mml:mo><mml:msup><mml:mi>n<\/mml:mi><mml:mi>k<\/mml:mi><\/mml:msup><mml:mo stretchy=\"false\">)<\/mml:mo><\/mml:math>, where the exponent <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mi>k<\/mml:mi><\/mml:math> is estimated to be <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mn>2.59<\/mml:mn><\/mml:math> using a least-squares power law. On the same family random instances, the estimated scaling exponent for an external SOCP solver is <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mn>3.31<\/mml:mn><\/mml:math> while that for a state-of-the-art SVM solver is <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mn>3.11<\/mml:mn><\/mml:math>.<\/jats:p>","DOI":"10.22331\/q-2021-04-08-427","type":"journal-article","created":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T14:00:00Z","timestamp":1617890400000},"page":"427","update-policy":"https:\/\/doi.org\/10.22331\/q-crossmark-policy-page","source":"Crossref","is-referenced-by-count":23,"title":["Quantum algorithms for Second-Order Cone Programming and Support Vector Machines"],"prefix":"10.22331","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0659-3727","authenticated-orcid":false,"given":"Iordanis","family":"Kerenidis","sequence":"first","affiliation":[{"name":"QCWare, Palo Alto, California"},{"name":"Universit\u00e9 de Paris, CNRS, IRIF, F-75006, Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6853-7125","authenticated-orcid":false,"given":"Anupam","family":"Prakash","sequence":"additional","affiliation":[{"name":"QCWare, Palo Alto, California"},{"name":"Universit\u00e9 de Paris, CNRS, IRIF, F-75006, Paris, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8518-8422","authenticated-orcid":false,"given":"D\u00e1niel","family":"Szil\u00e1gyi","sequence":"additional","affiliation":[{"name":"Universit\u00e9 de Paris, CNRS, IRIF, F-75006, Paris, France"}]}],"member":"9598","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"0","doi-asserted-by":"publisher","unstructured":"F. 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