arXiv subjects

Ufuk Korkmaz

Publications and source records attributed to Ufuk Korkmaz.

7 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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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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