5 papers
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…
Machine Learning-based Quantum Error Mitigation for Variational Algorithms
Nikita Korolev, Kirill Lakhmanskiy, Daniil Rabinovich
Machine Learning-based quantum error mitigation (ML-QEM) has emerged as a promising approach for improving the performance of noisy quantum algorithms. However, existing ML-QEM met…
Zero-Noise Extrapolation via Cyclic Permutations of Quantum Circuit Layouts
Zahar Sayapin, Daniil Rabinovich, Nikita Korolev +1
Increasing the utility of currently available Noisy Intermediate-Scale Quantum (NISQ) devices requires developing efficient methods to mitigate hardware errors. In this work we pro…
Problem specific ion native ansatz for combinatorial optimization
Georgii Paradezhenko, Daniil Rabinovich, Ernesto Campos +1
Variational quantum algorithms have become a standard approach for solving a wide range of problems on near-term quantum computers. Identifying an appropriate ansatz configuration…
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…