Experimentally realizable continuous-variable quantum neural networks
Shikha Bangar, Leanto Sunny, Kübra Yeter‐Aydeniz, George Siopsis
Abstract
The authors propose a continuous-variable quantum-classical neural network protocol that could be potentially realized in current photonic experiments. Their protocol only uses Gaussian operations, and a nonlinear operation is obtained through repeat-until-success measurements on ancillary qumodes. They applied their method to simulate quantum machine learning and computing problems.
Topics & Concepts
Continuous variableArtificial neural networkComputer scienceProtocol (science)Variable (mathematics)Nonlinear systemQuantumGaussianPhotonicsStatistical physicsPhysicsQuantum mechanicsArtificial intelligenceMathematical optimizationMathematicsMathematical analysisPathologyAlternative medicineMedicineNeural Networks and Reservoir ComputingQuantum Information and CryptographyQuantum Computing Algorithms and Architecture