activity
20092026
most citedQuantum tomography benchmarking

13 citations · 49 across the 15 of their papers we have counts for

collaborators

16 papers

quant-ph2026

Disentangling QAOA: From Weakly Entangled Circuits to a Classical QUBO Solver

Boris I. Bantysh, Andrey Yu. Chernyavskiy, Denis A. Kulikov +2

The role of entanglement in quantum optimization remains actively debated. To address this question, we focus on the fixed-parameter expanding-depth regime of the quantum approxima…

quant-ph2025

Evidence for effectively constant shot complexity in the quantum approximate optimization algorithm without per-instance optimization

Andrey Yu. Chernyavskiy, Denis A. Kulikov, Boris I. Bantysh +3

We study a modified fixed-point version of the Quantum Approximate Optimization Algorithm (fpQAOA), where parameters are trained classically on small instances and then transferred…

quant-ph2025

Progress in the development of quantum algorithms and software

Anastasiia S. Nikolaeva, Daria O. Konina, Anatolii V. Antipov +10

A quantum processor, like any computing device, requires the development of both hardware and the necessary set of software solutions, starting with quantum algorithms and ending w…

quant-ph2025★ 1 cited

Experimental factoring integers using fixed-point-QAOA with a trapped-ion quantum processor

Ilia V. Zalivako, Andrey Yu. Chernyavskiy, Anastasiia S. Nikolaeva +10

Factoring integers is considered as a computationally-hard problem for classical methods, whereas there exists polynomial-time Shor's quantum algorithm for solving this task. Howev…

quant-ph2024★ 5 cited

Fast reconstruction of programmable interferometers with intensity-only measurements

B. I. Bantysh, A. Yu. Chernyavskiy, S. A. Fldzhyan +1

Programmable linear optical interferometers are promising for classical and quantum applications. Their integrated design makes it possible to create more scalable and stable devic…

quant-ph2023★ 1 cited

Entropic property of randomized QAOA circuits

A. Yu. Chernyavskiy, B. I. Bantysh, Yu. I. Bogdanov

Quantum approximate optimization algorithm (QAOA) aims to solve discrete optimization problems by sampling bitstrings using a parameterized quantum circuit. The circuit parameters…