70 citations · 131 across the 9 of their papers we have counts for
9 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…
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…
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…
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…
Quantum-machine-learning channel discrimination
Andrey Kardashin, Anna Vlasova, Anastasiia Pervishko +2
In the problem of quantum channel discrimination, one distinguishes between a given number of quantum channels, which is done by sending an input state through a channel and measur…
Numerical hardware-efficient variational quantum simulation of a soliton solution
Andrey Kardashin, Anastasiia Pervishko, Jacob Biamonte +1
Implementing variational quantum algorithms with noisy intermediate-scale quantum machines of up to a hundred qubits is nowadays considered as one of the most promising routes towa…
Variational Simulation of Schwinger's Hamiltonian with Polarisation Qubits
O. V. Borzenkova, G. I. Struchalin, A. S. Kardashin +5
The numerical emulation of quantum physics and quantum chemistry often involves an intractable number of degrees of freedom and admits no known approximation in general form. In pr…
Certified variational quantum algorithms for eigenstate preparation
Andrey Kardashin, Alexey Uvarov, Dmitry Yudin +1
Solutions to many-body problem instances often involve an intractable number of degrees of freedom and admit no known approximations in general form. In practice, representing quan…
Machine Learning Phase Transitions with a Quantum Processor
Alexey Uvarov, Andrey Kardashin, Jacob Biamonte
Machine learning has emerged as a promising approach to study the properties of many-body systems. Recently proposed as a tool to classify phases of matter, the approach relies on…