arXiv subjects

Deniz Türkpençe

Publications and source records attributed to Deniz Türkpençe.

15 recordsLinked to original sources

arXiv2026

Operational Collapse Region in Repeaterless Loss-Dephasing Quantum Channels

The distribution of entangled photon pairs over standard optical fiber is a fundamental requirement for the realization of the quantum internet. However, real-world deployment is severely bottlenecked by the interplay of amplitude damping (photon loss) and phase noise (birefringence). In this paper, we numerically investigate the degradation of dual-rail polarization entanglement in telecom C-band fiber links. We demonstrate a critical disparity between the physical survival of quantum correlations and their practical utility in standard communication protocols. By evaluating the unconditional logarithmic negativity against the post-selected teleportation fidelity, we identify a distinct ``operational collapse region'' -- a distance window where the channel retains true quantum entanglement, yet standard coincidence-based detection architectures fail to provide any advantage over classical strategies. Furthermore, we reveal that the width of this inaccessible region exhibits a non-monotonic dependence on the phase noise rate, implying that simply minimizing fiber dephasing does not necessarily optimize the operational efficiency of the network. These findings provide vital guidelines for the design of practical quantum communication links.

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arXiv2026

Characterizing Phase Fragility via Algorithmically Prepared Ancillas in Repeated-Interaction Models

We study how a phase parameter $ϕ$, encoded through a single-qubit $H\,φ\,H$ gate sequence, is reflected in the quantum Fisher information (QFI) under realistic noisy dynamics. Within a collision-model framework, a probe qubit interacts sequentially with algorithmically prepared reservoir ancillas, leading to a $ϕ$-dependent steady state from which $\mathcal{F}_ϕ$ can be evaluated in closed form. In parallel, we perform pulse-resolved open-system simulations of the same gate sequence, using Gaussian-driven control motivated by transmon hardware, to obtain the corresponding pre-measurement density matrix. Despite the distinct physical descriptions, both approaches yield QFI profiles with similar phase dependence on the encoded phase. A quantitative comparison using profile-level similarity metrics further shows that the two descriptions identify the same phase-sensitive regions, although their absolute QFI contrasts differ. This consistency indicates that the steady state of a probe qubit interacting with algorithmically prepared ancillas can capture key features of the phase response observed in noisy device-level implementations. The underlying physical mechanism is the persistence of finite steady coherence in the asymptotic probe state, which retains the phase imprint of the prepared ancillas. Beyond conceptual insight, the steady-state framework provides a model-based, tomography-free diagnostic route for characterizing phase sensitivity. Possible uses in biased-noise error correction, hardware-aware compilation, and pulse-level optimization are therefore presented as future outlook directions.

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arXiv2025

Ultrafast Single-Qubit Gates in the Diabatic Regime

Logic gates in superconducting quantum processors are implemented through precise quantum control techniques in the microwave regime. The choice of drive frequency and other control parameters directly determines the duration of quantum gate operations. Because current devices remain too noisy to reach fault tolerance, reducing gate durations, and thereby the overall circuit depth, is of critical importance. In this work, we present a model of single qubit gate execution in both the adiabatic regime, where the rotating wave approximation (RWA) is valid, and the diabatic regime, where the RWA no longer applies. Using parameters representative of superconducting qubits, we investigate how gates can be driven at durations well below conventional timescales, and we examine the associated limitations, performance trade offs. The results demonstrate that ultrashort control pulses in the diabatic regime can achieve fidelities comparable to those obtained under standard RWA conditions, offering a possible route to faster quantum logic without sacrificing accuracy under idealized conditions.

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arXiv2023

Application of Power Flow problem to an open quantum neural hardware

Significant progress in the construction of physical hardware for quantum computers has necessitated the development of new algorithms or protocols for the application of real-world problems on quantum computers. One of these problems is the power flow problem, which helps us understand the generation, distribution, and consumption of electricity in a system. In this study, the solution of a balanced 4-bus power system supported by the Newton-Raphson method is investigated using a newly developed dissipative quantum neural network hardware. This study presents the findings on how the proposed quantum network can be applied to the relevant problem and how the solution performance varies depending on the network parameters.

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arXiv2023

Dissipative learning of a quantum classifier

The expectation that quantum computation might bring performance advantages in machine learning algorithms motivates the work on the quantum versions of artificial neural networks. In this study, we analyze the learning dynamics of a quantum classifier model that works as an open quantum system which is an alternative to the standard quantum circuit model. According to the obtained results, the model can be successfully trained with a gradient descent (GD) based algorithm. The fact that these optimization processes have been obtained with continuous dynamics, shows promise for the development of a differentiable activation function for the classifier model.

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arXiv2023

Stabilization and Dissipative Information Transfer of a Superconducting Kerr-Cat Qubit

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 version of the binary quantum classification, which is the basic processing unit of quantum machine learning algorithms. On the other hand, Cat-Qubit architecture, which has the potential to easily implement activation-like functions in artificial neural networks due to its rich physics, also offers an alternative hardware opportunity for quantum artificial neural networks. Numerical calculations exhibit successful transfer of quantum information from reservoir qubits by a repeated-interactions-based dissipative scheme.

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arXiv2023

Information-driven Nonlinear Quantum Neuron

The promising performance increase offered by quantum computing has led to the idea of applying it to neural networks. Studies in this regard can be divided into two main categories: simulating quantum neural networks with the standard quantum circuit model, and implementing them based on hardware. However, the ability to capture the non-linear behavior in neural networks using a computation process that usually involves linear quantum mechanics principles remains a major challenge in both categories. In this study, a hardware-efficient quantum neural network operating as an open quantum system is proposed, which presents non-linear behaviour. The model's compatibility with learning processes is tested through the obtained analytical results. In other words, we show that this dissipative model based on repeated interactions, which allows for easy parametrization of input quantum information, exhibits differentiable, non-linear activation functions.

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arXiv2022

A quantum collisional classifier driven by information reservoir

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.

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arXiv2020

A thermal quantum classifier

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.

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arXiv2019

A steady state quantum classifier

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.

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arXiv2018

Tailoring the thermalization time of a cavity-field using distinct atomic reservoirs

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 multi-level 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.

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arXiv2018

Decoherence in a quantum neural network

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 extend to which the central spin coherence time would be prolonged under specific parameters and spin-coupling Hamiltonians in a Markov environment. We show that Ising-type spin coupling is not desirable since it rapidly diminishes the coherence time in a dissipative environment.

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arXiv2017

Quantum neural networks driven by information reservoir

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.

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arXiv2016

A photonic Carnot engine powered by a quantum spin-star network

We propose a spin-star network, where a central spin-$1/2$ is coupled with XXZ interaction to $N$ outer spin-$1/2$ particles, as a quantum fuel. If the network is in thermal equilibrium with a cold bath, the central spin can have an effective temperature larger than the bath one and scaling nonlinearly with $N$. The nonlinearity can be tuned to $N^2, N^3$ or $N^4$ with the anisotropy parameter of the coupling. Using a stream of central-spin particles to pump a micromaser cavity, we calculate the dynamics of the cavity field using a coarse-grained master equation. Our study reveals that the central-spin beam effectively acts as a hot reservoir to the cavity field and brings the field to a thermal steady-state whose temperature benefits from the same nonlinear enhancement with $N$, and results in a highly efficient photonic Carnot engine. The validity of our conclusions is tested against the presence of atomic and cavity damping using a microscopic master equation method for typical microwave cavity-QED parameters. An alternative equivalent scheme where the spin-$1/2$ is coupled to a macroscopic spin-$(N/2)$ particle is also discussed.

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arXiv2015

Quantum Fuel with Multilevel Atomic Coherence for Ultrahigh Specific Work in a Photonic Carnot Engine

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 {\bf 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.

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