Automatic design of quantum feature maps
Sergio Altares-López, Ángela Ribeiro, Juan José García‐Ripoll
Abstract
Abstract We propose a new technique for the automatic generation of optimal ad-hoc ansätze for classification by using quantum support vector machine. This efficient method is based on non-sorted genetic algorithm II multiobjective genetic algorithms which allow both maximize the accuracy and minimize the ansatz size. It is demonstrated the validity of the technique by a practical example with a non-linear dataset, interpreting the resulting circuit and its outputs. We also show other application fields of the technique that reinforce the validity of the method, and a comparison with classical classifiers in order to understand the advantages of using quantum machine learning.
Topics & Concepts
Computer scienceSupport vector machineQuantumAnsatzGenetic algorithmArtificial intelligenceFeature (linguistics)Machine learningAlgorithmMathematicsMathematical physicsLinguisticsQuantum mechanicsPhilosophyPhysicsQuantum Computing Algorithms and ArchitectureQuantum Information and CryptographyNeural Networks and Reservoir Computing