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quant-ph2026
Parametrized-circuit-free quantum regression with variance regularization
Yerassyl Balkybek, Andrey Kardashin, Vladimir V. Palyulin +1
Quantum regression tasks for predicting properties of quantum states are commonly addressed using variational quantum algorithms. While variational quantum circuits are highly expr…
quant-ph2025
Distributed quantum architecture search using multi-agent reinforcement learning
Mikhail Sergeev, Georgii Paradezhenko, Daniil Rabinovich +1
Quantum architecture search (QAS) automates the design of parameterized quantum circuits for variational quantum algorithms. The framework finds a well-suited problem-specific stru…
quant-ph2025
Predicting properties of quantum systems by regression on a quantum computer
Andrey Kardashin, Yerassyl Balkybek, Vladimir V. Palyulin +1
Quantum computers can be considered as a natural means for performing machine learning tasks for inherently quantum labeled data. Many quantum machine learning techniques have been…