Browsing by Author "Borisenok, S."

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On Feedback Control Algorithms for Nitrogen-Vacancy Quantum Sensing
(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2024) Borisenok, S.
Ultrasensitive quantum detection of external weak signals at the nanoscale levels can be implemented in a variety of forms. Here we discuss different feedback control algorithms for the sensing scenario based on the semiclassical Tavis-Cummings model for nitrogen-vacancy (NV) centers located in the diamond. In the frame of this model, the sensing elements are considered as non-interacting two-level quantum systems, distributed in-homogeneously due to heterogeneous local magnetic and strain environments. The dynamical system of ordinary differential equations corresponding to the model contains the set of control parameters: the detunings between the drive frequency and the cavity frequency and between the drive frequency and NV transition frequency, as well as the relaxation coefficients. Correspondingly, it opens a gate for developing feedback control algorithms for tracking the cavity field, the income signal, and the reflection signal in the model sensing system. To study the principal features of algorithmic feedback we formulate the simplified ’toy model’ for the TavisCummings system and investigate alternative schemes of feedback (gradient methods, target attractor methods) to compare their pros and cons for effective control for nitrogen-vacancy-cavity quantum sensing based on different choices of the control parameter set. This work was supported by the Research Fund of Abdullah Gül University; Project Number: BAP FBA-2023-176 ’Geribesleme kontrol algoritmaları ile kubit tabanlı sensörlerin verimliliğinin artırılması’. The paper was presented at PhysCon2024. © 2024 Elsevier B.V., All rights reserved.
Open-Loop Control on the Efficiency of Quantum Battery With Reservoir Engineering
(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2025) Borisenok, S.
We discuss the case of a harmonic oscillator-based quantum battery strongly coupled to a highly nonMarkovian thermal reservoir via the quantum charger described by the Caldeira–Leggett model. The coupling between the reservoir and the battery serves as a control parameter for the system. We consider the system to stay in the strongly underdamped regime. Within the framework of the open-loop approach, we determine the optimal shape of control for the battery charging work and then restore the control coupling characteristics of QB in the Hamiltonian for the alternative cases of low and high temperatures. Ultimately, we discuss some possible ways to develop our model for the feedback algorithms. © 2025 Elsevier B.V., All rights reserved.
Citation - Scopus: 2
Detection and Control of Epileptiform Regime in the Hodgkin–Huxley Artificial Neural Networks via Quantum Algorithms
(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2022) Borisenok, S.
The problem of detection and the following suppression of epileptiform dynamics in artificial neural networks (ANN) still is a hot topic in modern theoretical and applied neuroscience. For the purpose of such modeling, the Hodgkin–Huxley (HH) elements are important due to the variety of their behavior such as resting, singular spikes, and spike trains and bursts. This dynamical spectrum of individual HH neurons can cause an epileptiform regime originated in the hyper-synchronization of the cell outcomes. Our model covers the detection and suppression of ictal behavior in a small ANN consisting of HH cells. The model follows our approach [Borisenok et al., 2018] for the HH neurons as a classical dynamical system driving the collective neural bursting, but here we use a quantum paradigm-based algorithm emulated with the pair of HH neurons. Such emulation becomes possible due to the complexity of the individual 4d HH dynamics. The linear chain of two HH neurons is connected to the rest of ANN and works autonomously. The first neuron plays a role of the detecting element for the hyper-synchronization in the ANN and the quantum algorithm emulator; while the second one works as a measuring element (emulation of the quantum measurement converting the signals into the classical domain) and the trigger for the feedback suppressing the epileptiform regime. We use here the speed gradient algorithm for controling the emulating neuron and discuss its pros and cons to compare with our classical model of epileptiform suppression. © 2022 Elsevier B.V., All rights reserved.
Citation - Scopus: 4
Speed Gradient Control Over Qubit States
(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2024) Borisenok, S.; Gogoleva, Elena
We discuss the model of a quantum bit driven by an external classical field without decay in the rotating wave approximation. In such a model, the whole evolution of the qubit states takes place on the Bloch sphere. We reformulate the model as a unitless set of real ordinary differential equations and use the normalized external field as a feedback control parameter. The closed-loop algorithm is designed in the form of the speed gradient, driving the dynamical system towards minimizing a given nonnegative goal function expressed via the qubit variables. We investigate the achievability of the control goal, and focus on the most important features of the speed gradient algorithm applied to a quantum system in comparison with classical systems. Our approach is valid for the control over the ground and excited population levels, and over the qubit phase variables. The paper was presented at PhysCon2024. © 2024 Elsevier B.V., All rights reserved.
Citation - Scopus: 1
Control Over Performance of Qubit-Based Sensors
(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences dvv@msa.ipme.ru, 2018) Borisenok, S.
The extreme sensitivity of quantum systems towards the external perturbations and in the same time their ability to be strongly coupled to the measured target field makes them to be stable under the environmental noise. A high quality quantum sensor can be engineered even on the platform of a single trapped qubit. There is a variety of optimal and sub-optimal algorithms for effective control over the quantum system states. Here we discuss the opportunity to improve the efficiency of the external field quantum sensor based on a single qubit via its feedback tracking. © 2020 Elsevier B.V., All rights reserved.
Citation - WoS: 7
Citation - Scopus: 12
Use of Topological Data Analysis in Motor Intention Based Brain-Computer Interfaces
(European Signal Processing Conference, EUSIPCO, 2018) Altindis, Fatih; Yilmaz, Bulent; İçöz, Kutay; Borisenok, S.
This study aims to investigate the use of topological data analysis in electroencephalography (EEG) based on brain-computer interface (BCI) applications. Our study focused on extracting topological features of EEG signals obtained from the motor cortex area of the brain. EEG signals from 8 subjects were used for forming data point clouds with a real-time simulation scenario and then each cloud was processed with JPlex toolbox in order to find out corresponding Betti numbers. These numbers represent the topological structure of the point data cloud related to the persistent homologies, which differ for different motor activity tasks. The estimated Betti numbers has been used as features in k-NN classifier to discriminate left or right hand motor intentions. © 2019 Elsevier B.V., All rights reserved.
Citation - Scopus: 3
Ergotropy of Bosonic Quantum Battery Driven via Repelling Feedback Algorithms
(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2021) Borisenok, S.; Gürsey, Feza
Feedback algorithms can be efficiently applied to control the basic characteristics of quantum batteries (QBs): the ergotropy, the charging power, the storage capacity and others. We invent here two alternative approaches, target repeller and speed gradient feedback, to maximize the ergotropy for bosonic types of single-qubit based quantum batteries. We demonstrate the achievability of the control goal and discuss some pros and cons of both proposed algorithms. © 2021 Elsevier B.V., All rights reserved.
Citation - Scopus: 11
Energy Transfer in Two-Level Quantum Systems via Speed Gradient-Based Algorithm
(Elsevier B.V., 2015) Pechen, Alexander N.; Borisenok, S.
We develop the speed gradient-based algorithm for controlled transfer of energy in a two-level quantum system towards a predefined value of energy using as control spectral density of incoherent photons. The algorithm can stabilize energy at a value less than one half of the energy gap between the two system states and is shown to be more effective for cooling than for heating. © 2021 Elsevier B.V., All rights reserved.
Control Over the Training Performance of Quantum State Tomography With Reservoir Computing Networks
(Institute for Problems in Mechanical Engineering, Russian Academy of Sciences, 2024) Borisenok, S.
The evaluation of unknown states for a given quantum system is one of the key problems in quantum information processing. The most efficient method of state characterization is quantum state tomography (QST), where the full-density matrices are reconstructed from the experimental measurements or numerical simulations performed on quantum states. The improvement of the computational performance in quantum state tomography and its related problems is a challenging task for modern theoretical physics. The general scheme of computing deals with the input information that goes into a quantum reservoir through a recurrent evolution. After the evolution, the final output is obtained as the linear combination of the readout elements. In our approach, the quantum reservoir is modeled with the Lindbladian equation. The control over performance is made by the coherent coupling parameter between the input quantum state and the reservoir. The control feedback algorithm is represented with the set of Kolesnikov’s target attractor algorithm to drive certain parameters of quantum state tomography, particularly, the outputs for the density matrix. Here we formulate the target attractor feedback in a discrete form to improve the training performance of QST and then develop a basic example of the state tomography for the quantum system of spin 1/2. We conclude by mentioning the basic features of our algorithm and its possible development. © 2025 Elsevier B.V., All rights reserved.