arXiv subjects

Serkan Akkoyun

Publications and source records attributed to Serkan Akkoyun.

At least 19 recordsLinked to original sources

arXiv2026

Systematic VQE Benchmarking of the Deuteron, Triton, and Helium-3 within Lattice Pionless Effective Field Theory

We investigate the performance of quantum algorithms for light nuclear systems by studying the deuteron (2H), triton (3H), and helium-3 (3He) nuclei within a lattice formulation of pionless effective field theory (EFT). We first compute ground-state energies using classical exact diagonalization (ED), serving as a benchmark reference for variational quantum algorithms. We then perform Variational Quantum Eigensolver (VQE) calculations using noiseless classical statevector simulations of quantum circuits, enabling a controlled assessment of algorithmic performance in the absence of hardware-induced noise. We calibrate the two-body low-energy constant using the deuteron system and fit the three-body interaction strength to the triton, then consistently apply the resulting Hamiltonian parameters to the helium-3 nucleus. Our VQE calculations employ physically motivated ansatze targeting the relevant particle-number sector, with explicit particle-number-conserving constructions implemented for the triton and helium-3 systems. The variational optimization includes an analysis of the Hamiltonian energy variance roviding additional insight into convergence behavior and the quality of the variational states. We find that the VQE results are in good agreement with the corresponding classical ED ground-state energies across all three systems, including the isospin-asymmetric helium-3 nucleus with Coulomb interactions. Overall, our study provides a transparent and reproducible benchmark for assessing the applicability of variational quantum algorithms to few-body nuclear systems. Additionally, we perform a noisy VQE simulation with a depolarizing noise model for the triton system to illustrate the impact of realistic Noisy Intermediate-Scale Quantum (NISQ)-era hardware noise on variational energy estimation.

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arXiv2025

From Classical to Quantum Machine Learning: Different Approaches in Fission Barrier Height Estimation

The fission barrier energy is a fundamental property of nuclear structure that governs the stability of nuclei against fission, directly affecting their spontaneous fission half-lives and the formation of superheavy elements. However, because it can only be measured indirectly, it also enables the emergence of alternative, complementary, fast, and accurate prediction tools for traditional theoretical models. In this study, we examine the use of classical, hybrid, and quantum support vector regression (SVR) approaches to estimate fission barrier heights, starting from fundamental nuclear properties and their derived additional properties. For this purpose, eight different SVR-based approaches are considered: (i) Classical SVR, (ii) Enhanced Classical SVR with polynomial and trigonometric feature extensions, (iii) Quantum-Inspired SVR, (iv) Hybrid SVR, (v) Enhanced Hybrid SVR, (vi) Quantum Core SVR, (vii) Fixed-Parameter Quantum Feature Map SVR, and (viii) Pure Quantum SVR. The models were trained and tested on a dataset of 317 isotopes in the Z (atomic number) range of 98-126, encompassing the actinide and superheavy regions. The model performance was evaluated in terms of R2, RMSE, MAE, and training-test R2 gap. The results show that the hybrid model achieves the best overall performance. However, while quantum-enhanced approaches are still limited by circuit depth and optimization precision, they achieve competitive accuracy comparable to classical results. Considering future developments in quantum hardware and algorithms, quantum approaches are expected to achieve considerable improvements. The findings suggest that quantum machine learning can supplement classical approaches and offer a promising path toward more accurate and efficient nuclear property predictions.

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arXiv2025

Schematic Representation Method for Quantum Circuits: An Intuitive Approach to Quantum Gate Effects

In quantum circuits, qubits and the quantum gates acting on them have traditionally been analysed using matrix algebra and Dirac notation. While powerful, these can be unintuitive for conceptual understanding and rapid problem solving. In this work, a new schematic representation method is developed that visualizes the effects of quantum gates on qubits without relying on complex mathematical operations. In the new notation, quantum bits (qubits) are represented using black (0) and white (1) circles. When a quantum gate is applied to a qubit, the circle representing the qubit is visually modified. For example, a Hadamard gate transforms a solid black or white circle into a half-black and half-white circle representing superposition. The work shows how this method simplifies the visualization of quantum algorithms, entanglement, and multi-qubit operations. Thus, the effects of quantum circuits can be analysed with simple schematic representations without having to go into complex mathematical operations.

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arXiv2022

Predicting Beta Decay Energy with Machine Learning

$Q_β$ represents one of the most important factors characterizing unstable nuclei, as it can lead to a better understanding of nuclei behavior and the origin of heavy atoms. Recently, machine learning methods have been shown to be a powerful tool to increase accuracy in the prediction of diverse atomic properties such as energies, atomic charges, volumes, among others. Nonetheless, these methods are often used as a black box not allowing unraveling insights into the phenomena under analysis. Here, the state-of-the-art precision of the $β$-decay energy on experimental data is outperformed by means of an ensemble of machine-learning models. The explainability tools implemented to eliminate the black box concern allowed to identify uncertainty and atomic number as the most relevant characteristics to predict $Q_β$ energies. Furthermore, physics-informed feature addition improved models' robustness and raised vital characteristics of theoretical models of the nuclear structure.

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arXiv2022

Neutron Single-particle States in 101Sn by Polynomial Fits and Shell Model Calculations for Light Sn Isotopes

One of the main ingredients in nuclear structure studies using shell model are the single-particle energy (spe). In order to obtain these values accurately, experimental data is needed. The region around the doubly magic nuclide 100Sn is very interesting for nuclear studies in terms of structure, reaction and nuclear astrophysics. Experimental spectrum data for the 101Sn isotope is required for nuclear shell model studies to be carried out in this region. Since there is not enough experimental data in the literature, different approaches are used for the obtaining spes for the region such as using the hole excitation spectrum in 131Sn or using the lightest and closest isotope 107Sn which figures the model space orbitals. In this work, we have performed second order polynomial fits of the tree single-particle states s1/2, d3/2 and h11/2 in the light Sn isotopes up to 113Sn and 115Sn which are not determined yet experimentally. By an extrapolation toward light Sn isotopes, we can obtain the excitation energies of all the single-particle states in 101Sn. Subsequently, neutron spes of the model space orbitals are defined. Shell model calculations for even and odd 102-107Sn isotopes are carried out using the new interactions and the results are compared with the experimental data and results obtained using the widely used interaction sn100pn.

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arXiv2022

Plasma shielding effects on nuclear spectra: $^{18}$Ne application

In this study, for the first time, in particular to astrophysics and fusion studies, how atomic nuclei embedded in the plasma environment are affected by plasma are systematically analysed. The related interactions in plasma environments considered as Debye and quantum plasma are depicted by more general exponential cosine screened Coulomb (MGECSC) potential. The plasma effects on the change of nuclear energy levels are probed through computations performed within the nuclear shell-model framework. For this purpose, the single-particle energy (spe) values to be used in the calculations are obtained by considering the modified Woods-Saxon (WS) potential due to shielding effect of plasma environment. As the modification in question is executed on Coulomb interaction term in WS potential, the computations are carried out for $ ^{18} $Ne nucleus which has two valence protons. Under the influence of the plasma, it is confirmed that the spe's change within certain limit value ranges. When considering the nuclear shell-model for the related computing, it is clear that this change leads to an obvious shifting in the energies of the nuclear states. It is observed that proton spe values are sensitive to plasma shielding effect, and shielding effect has a significant potent on the ground-state and excited energy states of the nucleus. In particular, the ground-state binding energies are determined to be extremely sensitive to the plasma shielding parameters. Plasma environments affect the proton spe and ground state energy (gse) in the same way. The alternative to each other of plasma shielding parameters on the spe, gse and excited energy levels is also analysed.

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arXiv2021

Artificial Intelligence Supported Shell-Model Calculations for Light Sn Isotopes

The region around the doubly magic nuclide $^{100}Sn$ is very interesting for nuclear physics studies in terms of structure, reaction and nuclear astrophysics. The main ingredients in nuclear structure studies using the shell model are the single-particle energies and the two-body matrix elements. To obtain the former, experimental data of $^{101}Sn$ isotope spectrum are necessary. Since there is not enough experimental data, different approaches are used in the literature to obtain single-particle energies. In sn100pn interaction, the hole excitation spectrum was used in $^{131}Sn$ to determine neutron single-particle energies. The other approach is the use of the lightest isotope, $^{107}Sn$, which figures the model space orbitals. In this study, we estimated the spectrum of the $^{101}Sn$ isotope by artificial neural network method in order to obtain neutron single-particle energies. After the training was carried out by using the experimental spectra of the nuclei around $^{100}Sn$ isotope, the $^{101}Sn$ spectrum was obtained. Subsequently, neutron SPEs of the model space orbitals are defined. Shell model calculations for $^{102-108}Sn$ isotopes are carried out and results are compared to the experimental data and results obtained using the widely used interaction in the region, sn100pn. According to the results, it is seen that the Sn isotope spectra obtained with the new SPE values are more compatible with the experimental data.

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arXiv2020

Investigation of Nuclear Structures of Self-conjugate Zn, Ge, Se, Kr, Sr Nuclei

Nuclear structures of the atomic nuclei can be theoretically investigated by using nuclear shell model. Generally, a doubly closed-shell nucleus has been considered as inert core and the nucleons outside the core are taken into account in the calculation. It is assumed that the nucleons in the inert core do not move but each valance nucleon out of the core moves under an average potential created by the others. The self-conjugate (N=Z) moderate mass nuclei region is one of the region for the investigation of several phenomena because of the maximum spatial overlap of neutrons and protons. In this study, the nuclear structures of the moderate mass N=Z have been analyzed in the scope of the nuclear shell model by using KSHELL computer code. In the calculations, doubly magic 56Ni were taken as core and p3/2, f5/2 and p1/2 single particle orbits were used as valance orbits. Different two-body interactions have been taken into account. The results have been compared with each other and the available values existing in the literature.

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arXiv2020

Investigation of Nuclear Structures of Ne Isotopes by Nuclear Shell Model

One of the common methods used to investigate the nuclear structures of atomic nuclei is the nuclear shell model. Similar to the placement of atomic electrons into orbits, in the nuclear shell model, protons and neutrons are thought to fill the orbits within the nucleus, following the principle of Pauli's exclusion. These orbits are grouped together to form shells, which are said to be closed if all possible places in a shell are full. Atomic nuclei with closed shells are very stable and valence nucleons that are more than these nuclei are included in the nuclear shell model calculations. In this study, the nuclear shell model was used to investigate the nuclear structure of even-even Ne nuclei by considering the 16O core as a closed shell nuclei. Single particle orbits d5/2, s1/2 and d3/2 are taken into account and different parameter sets are used for two-body interactions between valance nucleons. The results were compared with each other and with current literature values. It was seen that the closest results to the experimental values were obtained with parameter sets of usdb and sdnn.

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arXiv2020

Determination of Photonuclear Reaction Cross-Sections on stable p-shell Nuclei by Using Deep Neural Networks

The photonuclear reactions which is induced by high-energetic photon are one of the important type of reactions in the nuclear structure studies. In this reaction, a target material is bombarded by photons with the energies in the range of gamma-ray energy scale and the photons can statistically be absorbed by a nucleus in the target material. In order to get rid of the excess energies of the excited target nuclei, it can first emit protons, neutrons, alphas and light particles according to the separation energy thresholds. After this emitting process, generally an unstable nucleus can be formed. By the investigation of this products forming after photonuclear reactions, nuclear structure information can be obtained. In the present work, (γ, n) photonuclear reaction cross-sections on stable p-shell nuclei have been estimated by using neural network method. The main purpose of this study is to find neural network structures that give the best estimations on the cross-sections and to compare them with each other and available literature data. According to the results, the method is convenient for this task.

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arXiv2020

Improvement Studies of an Effective Interaction for N=Z sd-shell Nuclei by Neural Networks

The nuclear shell model is one of the successful models in theoretical understanding of nuclear structure. If a convenient effective interaction can be found between nucleons, various observables such as energies of nuclear states are accurately predicted by this model. The basic requirements for the shell model calculations are a set of single particle energies and two-body interaction matrix elements (TBME) which construct the residual interaction between nucleons. This latter could be parameterized in different ways. In this study, we have used a different approach to improve existing USD type Hamiltonians for the shell model calculations of N=Z nuclei in the A=16-40 region. After obtaining the SDNN new effective interaction, shell model calculations have been performed for all N=Z nuclei in sd shell. In which, $^{16}{O}$ doubly magic nucleus has been assumed as an inert core and active particles are distributed in the $d_{5/2}$, $s_{1/2}$ and $d_{3/2}$ single particle orbits. The rms deviations from experimental energy values are lower for the newly generated effective interaction than those obtained using the original one for the studied nuclei.

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arXiv2019

Shell Model Calculations for Proton-rich Zn Isotopes via New Generated Effective Interaction by Artificial Neural Networks

In this study, the artificial neural network method has been employed for the generation of the new two-body matrix elements which is used for pfg shell nuclei. For this purpose, jj44b interaction Hamiltonian has been considered as a source. After the generation of the new Hamiltonian, both, original and new generated, are tested on proton-rich Zn isotopes. According to the results, the calculated values are close to the each other. As well the results from new interaction (jj44b_nn) are closer to the available experimental values in some cases.

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arXiv2019

Estimation of fusion-evaporation cross sections by artificial neural networks

Accurate determination of fusion-evaporation reaction cross section is important in experimental nuclear physics studies. In this study, by using artificial neural network (ANN) method, we have estimated the cross section values for different reactions. The related root mean square errors have been obtained as 18.5 and 110.4mb for training and test data which correspond to 1.8% and 10.5% deviations from the experimental values, respectively.This order of deviations is lower than the cross section value from most common theoretical calculations. The results of this study indicate that ANN method is capable for the estimation of cross section values of fusion-evaporation reactions.

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arXiv2018

Teaching Quantum Mechanical Commutation Relations via an Optical Experiment

The quantum mechanical commutation relations, which are directly related to the Heisenberg uncertainty principle, have a crucial importance for understanding the quantum mechanics of students. During undergraduate level courses, the operator formalisms are generally given theoretically and it is documented that these abstract formalisms are usually misunderstood by the students. Based on the idea that quantum mechanical phenomena can be investigated via geometric optical tools, this study aims to introduce an experiment, where the quantum mechanical commutation relations are represented in a concrete way to provide students an easy and permanent learning. The experimental tools are chosen to be easily accessible and economic. The experiment introduced in this paper can be done with students or used as a demonstrative experiment in laboratory based or theory based courses requiring quantum physics content; particularly in physics, physics education and science education programs.

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arXiv2017

Ground-state properties of some N=Z medium mass heavy nuclei

The ground-state properties of 64Ge, 68Se, 72Kr and 76Sr (N=Z) nuclei have been investigated by using Hartree-Fock-Bogolibov (HFB)method with Sly4 Skyrme forces and Relativistic Mean Field (RMF) model with NL3 and recently proposed DEFNE interaction parameters sets. For determination of ground-state axially deformed shape and quadrupole moment constrained calculations have been employed in RMF model. The results of the present study have been compared with each other and available experimental data in the literature. The ground-state binding energies,neutron, proton and charge radii, quadrupole moment deformation parameters of these nuclei have been calculated. Furthermore, neutron skin thickness of considered nuclei as a function of deformation parameter have been obtained and discussed in detail.

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arXiv2016

Surface energy coefficient determination in global mass formula from fission barrier energy

Semi-empirical mass formula of the atomic nucleus describe binding energies of the nuclei. In the simple form of this formula, there are five terms related to the properties of the nuclear structure. The coefficients in each terms can be determined by various approach such as fitting on experimental binding energy values. In this study, the surface energy coefficient in the formula which is a correction on total binding energy has been obtained by a method that is not previously described in the literature. The experimental fission barrier energies of nuclei have been used for this task. According to the results, surface energy coefficient in one of the most conventional formula has been improved by a factor 3.4.

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arXiv2014

Photonuclear Reaction Cross Sections for Gallium Isotopes

The photon induced reactions which are named as photonuclear reactions have a great importance in many field of nuclear, radiation physics and related fields. Since we have planned to perform photonuclear reaction on gallium target with bremmstrahlung photons from clinical linear accelerator in the future, the cross-sections of neutron (photo-neutron (γ,xn)) and proton (photo-proton (γ,xn)) productions after photon activation have been calculated by using TALYS 1.2 computer code in this study. The target nucleus has been considered gallium which has two stable isotopes, 69Ga and 71Ga. According to the results, we have seen that the calculations are in harmony in the limited literature values. Furthermore, the pre-equilibrium and compound process contributions to the total cross-section have been investigated.

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arXiv2013

Improvement studies on neutron-gamma separation in HPGe detectors by using neural networks

The neutrons emitted in heavy-ion fusion-evaporation (HIFE) reactions together with the gamma-rays cause unwanted backgrounds in gamma-ray spectra. Especially in the nuclear reactions, where relativistic ion beams (RIBs) are used, these neutrons are serious problem. They have to be rejected in order to obtain clearer gamma-ray peaks. In this study, the radiation energy and three criteria which were previously determined for separation between neutron and gamma-rays in the HPGe detectors have been used in artificial neural network (ANN) for improving of the decomposition power. According to the preliminary results obtained from ANN method, the ratio of neutron rejection has been improved by a factor of 1.27 and the ratio of the lost in gamma-rays has been decreased by a factor of 0.50.

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