4 papers
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…
Role of overparametrization in quantum approximate optimization
Daniil Rabinovich, Andrey Kardashin, Soumik Adhikary
Variational quantum algorithms have emerged as a cornerstone of contemporary quantum algorithms research. While they have demonstrated considerable promise in solving problems of p…
On measurement-dependent variance in quantum neural networks
Andrey Kardashin, Konstantin Antipin
Variational quantum circuits have become a widely used tool for performing quantum machine learning (QML) tasks on labeled quantum states. In some specific tasks or for specific va…
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…