Supervised Quantum Learning without Measurements

Abstract

We propose a quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations. The central physical mechanism of the protocol is the iteration of a quantum time-delayed equation that introduces feedback in the dynamics and eliminates the necessity of intermediate measurements. The performance of the quantum algorithm is analyzed by comparing the results obtained in numerical simulations with the outcome of classical machine learning methods for the same problem. The use of time-delayed equations enhances the toolbox of the field of quantum machine learning, which may enable unprecedented applications in quantum technologies.

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• Quantum Artificial Life in an IBM Quantum Computer
• Reconstruction of a Photonic Qubit State with Reinforcement Learning
• Reconstructing quantum states with quantum reservoir networks
• Measurement-based adaptation protocol with quantum reinforcement learning
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• Multiqubit and multilevel quantum reinforcement learning with quantum technologies
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• Enhanced Quantum Synchronization via Quantum Machine Learning
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• A quantum collisional classifier driven by information reservoir
• Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Study
• Storage and retrieval of von Neumann measurements
• Quantum Pattern Recognition in Photonic Circuits