Supervised Quantum Learning without Measurements
This work addresses a specific bottleneck in quantum machine learning by enhancing the toolbox for quantum technologies, but it appears incremental as it builds on existing quantum methods without claiming broad breakthroughs.
The authors tackled the problem of solving a class of problems encoded in quantum controlled unitary operations by proposing a quantum machine learning algorithm that eliminates intermediate measurements through a quantum time-delayed equation with feedback, and they analyzed its performance via numerical simulations compared to classical methods.
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.