Papers by Deniz Türkpençe
Balkan Journal of Electrical and Computer Engineering
Today, the competition to build a quantum computer continues, and the number of qubits in hardwar... more Today, the competition to build a quantum computer continues, and the number of qubits in hardware is increasing rapidly. However, the quantum noise that comes with this process reduces the performance of algorithmic applications, so alternative ways in quantum computer architecture and implementation of algorithms are discussed on the one hand. One of these alternative ways is the hybridization of the circuit-based quantum computing model with the dissipative-based computing model. Here, the goal is to apply the part of the algorithm that provides the quantum advantage with the quantum circuit model, and the remaining part with the dissipative model, which is less affected by noise. This scheme is of importance to quantum machine learning algorithms that involve highly repetitive processes and are thus susceptible to noise. In this study, we examine dissipative information transfer to a qubit model called Cat-Qubit. This model is especially important for the dissipative-based versi...
Physical review, Jan 30, 2023
We investigate the open dynamics of a probe qubit weakly interacting with distinct qubit environm... more We investigate the open dynamics of a probe qubit weakly interacting with distinct qubit environments bearing quantum information. We show that the proposed dissipative model yields a binary classification of the reservoir qubits' quantum information in the steady state in the Bloch qubit parameter space, depending on the coupling rates. To describe the dissipation model dynamics, we have adopted the collision model, in which the input information parameters of the reservoir qubits are easily determined. We develop a generalized classification rule based on the results of the micromaser-like master equation where the classification can be described in terms of the Bloch parameters. Moreover, we show that the proposed classification scheme can also be achieved through quantum parameter estimation. Finally, we demonstrate that the proposed dissipative classification scheme is suitable for gradient descent based supervised learning tasks.
Advances in Neural Signal Processing
In this study, EEG data from two volunteer individuals, a healthy individual and a patient with e... more In this study, EEG data from two volunteer individuals, a healthy individual and a patient with epilepsy, were investigated with two different methods in order to distinguish healthy and patient individuals from each other. The data were obtained from a healthy individual and from a patient with epilepsy at the time of epileptic seizure and of seizure-free interval. The data are those of which validity and reliability were proven and were supplied from the data bank records of University Hospital of Bonn in Germany. In the study, the statistical parameters of the collected data were calculated, then the same data were analysed using short-time Fourier transform (STFT) method, and then they were compared. Both statistical parameter results and spectrum analysis results are compatible with each other, and they can successfully detect healthy individuals and epileptic patients at the time of epileptic seizure and seizure-free interval. In this sense, the results were mathematically highly compatible, which offers significant information for the diagnosis of the disease. In the analysis, the variance values were determined as 253.203 for the healthy individual, 806.939 for the patient at seizure-free interval and 6985.755 for that patient at the time of seizure. Accordingly, standard deviation can be said to be quite distinctive in the designation of values. The frequencies of all three cases resulted in 0, 0-5 and 0-20 Hz, respectively, as a result of conducted STFT analysis, which is quite consistent with the results of the statistical analysis parameters.
Sakarya University Journal of Science
arXiv: Quantum Physics, May 1, 2019
A data classifier is the basic structural unit of an artificial neural network. These classifiers... more A data classifier is the basic structural unit of an artificial neural network. These classifiers, known as perceptron, make an output prediction over the linear summation of the input information. Quantum versions of artificial neural networks are considered to provide more efficient and faster artificial intelligence and learning algorithms. The most generic and realistic open quantum systems are the quantum systems in thermal environments and the information carried by the thermal reservoirs is the temperature information. This study shows that an open quantum system that is in contact with many information channels is a natural information classifier. More specifically, it has been demonstrated that a two-level quantum system can classify temperature information of distinct thermal reservoirs. The results of the manuscript are of importance to the construction of thermal quantum neural networks and the development of minimal quantum thermal machines. Also, a physical model, proposed and discussed with realistic parameters, shows that faster operating thermal quantum classifiers can be built than the classical versions.
arXiv: Quantum Physics, 2017
This study concerns with the dynamics of a quantum neural network unit in order to examine the su... more This study concerns with the dynamics of a quantum neural network unit in order to examine the suitability of simple neural computing tasks. More specifically, we examine the dynamics of an interacting spin model chosen as a candidate of a quantum perceptron for closed and open quantum systems. We adopt a collisional model enables examining both Markovian and non-Markovian dynamics of the proposed quantum system. We show that our quantum neural network (QNN) unit has a stable output quantum state in contact with an environment carrying information content. By the performed numerical simulations one can compare the dynamics in the presence and absence of quantum memory effects. We find that our QNN unit is suitable for implementing general neural computing tasks in contact with a Markovian information environment and quantum memory effects cause complications on the stability of the output state.
2021 5th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2021
The dissipative model of quantum computation is proven to be equivalent to its circuit model. Par... more The dissipative model of quantum computation is proven to be equivalent to its circuit model. Particularly, multi-qubit gates require time-dependent control with optimized parameters for some specific problems. One such problem is the simulation of a quantum version of a perceptron that classifies quantum information as binary using the framework of open quantum systems. In this scheme, a probe qubit is in contact with multiple, distinct quantum information-bearing environments and returns a binary decision depending on its amplitude parameter in its steady state. We refer to these environments as quantum information reservoirs. We choose a standard quantum collisional model in which the reservoir parameters can be defined in detail. In this study, we present the analytical results of the proposed classifier with an application to the superconductor quantum circuits for a single information reservoir. We exploit the additivity of quantum dynamic maps for dissipative processes in the weak coupling regime where optimized time-dependent control is not required to achieve the classification result. We show that the current state-of-the-art for superconducting circuits allows for the physical implementation of dissipative quantum information processing in the presence of information reservoirs with realistic parameters.
NeuroQuantology, 2018
In this study, we propose a spin-star model for spin-(1/2) particles in order to examine the cohe... more In this study, we propose a spin-star model for spin-(1/2) particles in order to examine the coherence dynamics of a quantum neural network (QNN) unit. Since quantum computing paradigm promises advantages over their classical counterparts, quantum versions of neural networks can be examined in this context. We focus on quantum coherence as a natural resource for quantum computing and investigate the central spin coherence of a spin star model in the time domain in a dissipative environment. More particularly, we investigate the extent to which the central spin coherence time would be prolonged under specific parameters and spin-coupling Hamiltonians in a Markov environment. We find that Heisenberg XX-type couplings are more favourable for spin coherence time and the increase on the number of ambient spins extend the coherence time only in this coupling scheme. We also show that isingtype spin coupling is not desirable since it rapidly diminishes the coherence time in a dissipative environment.
Journal of the Optical Society of America B, 2019
We study how the thermalization time of a single radiation cavity-field mode changes drastically ... more We study how the thermalization time of a single radiation cavity-field mode changes drastically depending on the type of the atomic reservoir it interacts. Temporal evolution of the field is analyzed within the micromaser scheme, where each atomic reservoir is modeled as a beam of atoms crossing an electromagnetic cavity in which they weakly interact with the field. The cavity-field thermalizes when we consider either multi-atom or multilevel atom reservoirs. We found that each atomic reservoir generates a different scaling law in the thermalization time of the cavity-field. Such scaling laws can be used for a faster or slower heating and cooling process. We have obtained analytical expressions for the thermalization time that were verified by means of a numerical simulation of the injection of each atomic reservoir into the cavity. We also discussed how our results could boost the efficiency and power output of some quantum heat engines during a finite time operation when the radiation field mode acts as the working substance.
Physical Review E, 2016
We investigate scaling of work and efficiency of a photonic Carnot engine with the number of quan... more We investigate scaling of work and efficiency of a photonic Carnot engine with the number of quantum coherent resources. Specifically, we consider a generalization of the "phaseonium fuel" for the photonic Carnot engine, which was first introduced as a three-level atom with two lower states in a quantum coherent superposition by [M. O. Scully, M. Suhail Zubairy, G. S. Agarwal, and H. Walther, Science 299, 862 (2003)], to the case of N + 1 level atoms with N coherent lower levels. We take into account atomic relaxation and dephasing as well as the cavity loss and derive a coarse grained master equation to evaluate the work and efficiency, analytically. Analytical results are verified by microscopic numerical examination of the thermalization dynamics. We find that efficiency and work scale quadratically with the number of quantum coherent levels. Quantum coherence boost to the specific energy (work output per unit mass of the resource) is a profound fundamental difference of quantum fuel from classical resources. We consider typical modern resonator set ups and conclude that multilevel phaseonium fuel can be utilized to overcome the decoherence in available systems. Preparation of the atomic coherences and the associated cost of coherence are analyzed and the engine operation within the bounds of the second law is verified. Our results bring the photonic Carnot engines much closer to the capabilities of current resonator technologies.
Photonics and Nanostructures - Fundamentals and Applications, 2016
We demonstrate effective background-free continuous wave nonlinear optical excitation of molecule... more We demonstrate effective background-free continuous wave nonlinear optical excitation of molecules that are sandwiched between asymmetrically constructed plasmonic gold nanoparticle clusters. We observe that near infrared photons are converted to visible photons through efficient plasmonic second harmonic generation. Our theoretical model and simulations demonstrate that Fano resonances may be responsible for being able to observe nonlinear conversion using a continuous wave light source. We show that nonlinearity enhancement of plasmonic nanostructures via coupled quantum mechanical oscillators such as molecules can be several orders larger as compared to their classical counterparts.
We show that, nonlinear optical processes in a plasmonic metal nanoparticle (MNP) dimer can be co... more We show that, nonlinear optical processes in a plasmonic metal nanoparticle (MNP) dimer can be controlled by the presence of a molecule or a quantum dot. By choosing the appropriate level spacing for the quantum emitter, one can either suppress or enhance the nonlinear frequency conversion. (i) Suppression occurs simply because transparency induced by Fano resonance does not allow an excitation at the converted frequency. (ii) Enhancement emerges since nonlinear process can be brought to resonance. Path interference effect cancels the nonresonant frequency terms. We demonstrate the underlying physics using a simplified model, and we show that the predictions of the model are in good agreement with the 3-dimensional boundary element method (MNPBEM toolbox) simulations. Here, we consider the second harmonic generation in a MNP dimer as an example to demonstrate the control mechanism. However, the method can be easily generalized to other nonlinear processes emerging on plasmonic reson...
Journal of Optics, 2014
We show that, nonlinear optical processes in a plasmonic metal nanoparticle (MNP) dimer can be co... more We show that, nonlinear optical processes in a plasmonic metal nanoparticle (MNP) dimer can be controlled by the presence of a molecule or a quantum dot. (i) Frequency conversion can be suppressed if the dimer is coupled to a quantum object which is resonant to the generated frequency. This occurs simply because, EIT does not allow an excitation at the converted frequency frequency. (ii) On the contrary, a similar effect can be used to enhance the frequency conversion. Nonlinear processes can be brought to resonance without tuning the dimer modes. Path interference effect cancels the nonresonant frequency terms. Here, we consider the second harmonic generation (SHG) as an example to demonstrate the control mechanism. However, the method can be easily generalized to other nonlinear processes.
Physics Letters A, 2019
We report that under some specific conditions a single qubit model weakly interacting with inform... more We report that under some specific conditions a single qubit model weakly interacting with information environments can be referred to as a quantum classifier. We exploit the additivity and the divisibility properties of the completely positive (CP) quantum dynamical maps in order to obtain an open quantum classifier. The steady state response of the system with respect to the input parameters was numerically investigated and it's found that the response of the open quantum dynamics at steady state acts non-linearly with respect to the input data parameters. We also demonstrate the linear separation of the quantum data instances that reflects the success of the functionality of the proposed model both for ideal and experimental conditions. Superconducting circuits were pointed out as the physical model to implement the theoretical model with possible imperfections.
A data classifier is the basic structural unit of an artificial neural network. These classifiers... more A data classifier is the basic structural unit of an artificial neural network. These classifiers, known as perceptron, make an output prediction over the linear summation of the input information. Quantum versions of artificial neural networks are considered to provide more efficient and faster artificial intelligence and learning algorithms. The most generic and realistic open quantum systems are the quantum systems in thermal environments and the information carried by the thermal reservoirs is the temperature information. This study shows that an open quantum system that is in contact with many information channels is a natural information classifier. More specifically, it has been demonstrated that a two-level quantum system can classify temperature information of distinct thermal reservoirs. The results of the manuscript are of importance to the construction of thermal quantum neural networks and the development of minimal quantum thermal machines. Also, a physical model, prop...
arXiv: Optics, 2014
We propose and demonstrate a method which is feasible for deterministic activation of few molecul... more We propose and demonstrate a method which is feasible for deterministic activation of few molecules. Our method relies on non-linear optical excitation of few enhanced yellow fluorescent protein molecules that are sandwiched between gaps of asymmetrically constructed plasmonic gold nanoparticle clusters. We observe that as infrared photons, which cannot get absorbed by fluorescent molecules, are converted through efficient second harmonic generation activity of gold nanoparticles to visible photons, the molecules absorb them and fluoresce. Our numerical simulations demonstrate that observation of SHG with cw laser becomes possible owing to the cooperative action of conversion enhancement through Fano resonance, hybridization in the plasmon absorption spectrum and the size asymmetry of nanoparticle dimers.
Quantum Inf. Comput., 2020
A data classifier is the basic structural unit of an artificial neural network. These classifiers... more A data classifier is the basic structural unit of an artificial neural network. These classifiers, known as perceptron, make an output prediction over the linear summation of the input information. Quantum versions of artificial neural networks are considered to provide more efficient and faster artificial intelligence and learning algorithms. The most generic and realistic open quantum systems are the quantum systems in thermal environments and the information carried by the thermal reservoirs is the temperature information. This study shows that an open quantum system that is in contact with many information channels is a natural information classifier. More specifically, it has been demonstrated that a two-level quantum system can classify temperature information of distinct thermal reservoirs. The results of the manuscript are of importance to the construction of thermal quantum neural networks and the development of minimal quantum thermal machines. Also, a physical model, prop...
Turkish journal of physics, 2011
Product operator formalism is widely used for the analytical description of multi-dimensional and... more Product operator formalism is widely used for the analytical description of multi-dimensional and multiple-pulse NMR experiments for the weakly coupled spin systems having spin-1/2 and spin-1 nuclei. The INEPT NMR experiment is a polarization transfer experiment including J-coupling. In this study, the INEPT NMR experiment was analytically investigated by using product operator theory for weakly coupled IS n ( NDn) (I =1, S =1; n=1, 2, 3) spin systems. The obtained theoretical results represent the FID values of NDn groups. In order to make Fourier transform of the obtained FID values, a Maple program is used and then simulated spectra of the INEPT experiment are obtained for 14 NDn groups. Then, the experimental suggestions are made for the INEPT NMR experiment of NDn groups. Also, it is suggested that the INEPT NMR experiment of IS (I =1, S=1) spin system can be used in NMR quantum computing.
Distribution of entangled parties with the longest time possible is of importance to quantum comm... more Distribution of entangled parties with the longest time possible is of importance to quantum communication. Therefore analysing the decay character of entanglement of correlated qubits in the presence of reservoir effects is of significance to the quantum-based technologies. This study covers the analysis of the temporal entanglement decay of two maximally entangled qubits against different reservoir types and system parameters. It's shown how varying the coupling type of the system to the environment affects the lifetime of entanglement. It's also found that in the presence of quantum interaction between entangled qubits, it's possible to enlarge the entanglement lifetime depending on the initialization of entanglement. Model parameters used in the numerical calculations and the results are general enough to be applied in any specific quantum-based experimental task. © 2021 Tim Pengembang Jurnal UPI Article History: Received 11 Aug 2020 Revised 18 Jan 2021 Accepted 03 F...
Uploads
Papers by Deniz Türkpençe