10 citations · 13 across the 7 of their papers we have counts for
7 papers
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…
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…
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…
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…
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…
Depth scaling of unstructured search via quantum approximate optimization
Ernesto Campos, Daniil Rabinovich, Alexey Uvarov
Variational quantum algorithms have become the de facto model for current quantum computations. A prominent example of such algorithms -- the quantum approximate optimization algor…
Progress towards analytically optimal angles in quantum approximate optimisation
D. Rabinovich, R. Sengupta, E. Campos +2
The Quantum Approximate Optimisation Algorithm is a layer, time-variable split operator method executed on a quantum processor and driven to convergence by classical outer loop…