243 citations · 1.6k across the 79 of their papers we have counts for
10 papers · 1 filter
Experimental characterization of the Toffoli gate via channel spectrum benchmarking
D. K. Korliakov, B. I. Bantysh, A. S. Borisenko +2
Channel spectrum benchmarking (CSB) provides a robust framework for characterizing quantum gate fidelities while remaining insensitive to state preparation and measurement (SPAM) e…
Applying Grover-mixer quantum alternating operator ansatz algorithm to higher-order unconstrained binary optimization problems
Evgeniy O. Kiktenko, Elizaveta V. Krendeleva, Aleksey K. Fedorov
The quantum approximate optimization algorithm (QAOA) is among the leading candidates for achieving quantum advantage on near-term processors. While typically implemented with a tr…
Qudit-native simulation of the Potts model
Maksim A. Gavreev, Evgeniy O. Kiktenko, Aleksey K. Fedorov +1
Simulating entangled, many-body quantum systems is notoriously hard, especially in the case of high-dimensional nature of physical underlying objects. In this work, we propose an a…
Further improvements to stabilizer simulation theory: classical rewriting of CSS-preserving stabilizer circuits, quadratic form expansions of stabilizer operations, and framed hidden variable models
Vsevolod I. Yashin, Vladimir V. Yatsulevich, Aleksey K. Fedorov +1
Simulation of stabilizer circuits is a well-studied problem in quantum information processing, with a number of highly optimized algorithms available. Yet, we argue that further im…
Transition-aware decomposition of single-qudit gates
Denis A. Drozhzhin, Evgeniy O. Kiktenko, Aleksey K. Fedorov +1
Quantum computation with -level quantum systems, also known as qudits, benefits from the possibility to use a richer computational space compared to qubits. However, for an arbi…
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…