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.Comment: 8 pages, 5 figure

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.Comment: 11 pages, 6 figure

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.Comment: 8 pages, 5 figure

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.Comment: 5 Figures, 6 Page

Thyroid function in healthy and unhealthy preterm newborns

Background: The thyroid gland and hormonal regulation are among the most important systems to be investigated in pre-term infants. This study sought to investigate thyroid hormone levels of healthy and unhealthy pre-term infants.Methods: The prospective study included 53 consecutive premature infants admitted to the neonatal intensive care unit within a duration of one year. Of these preterm babies, 20 were healthy, while 33 had problems such as asphyxia or RDS. Venous blood samples were collected at baseline 0-24 hours, 7 and 14 days and FT3, FT4, and TSH levels were determined. Other data recorded included demographic characteristics of the patients and clinical variables.Results: The most frequent health problems were RDS (87.9%), sepsis (30.3%), and retinopathy of prematurity (24.2%). The mean TSH levels showed a consistent decline at three consequent measurements in both groups, which were always significantly lower in unhealthy pre-terms. In both groups, TSH levels showed significant decreases on Day 7 and Day 14 compared to the baseline levels (p<005). The levels of FT3 and FT4 consistently showed significant correlations with gestational week and birth weight at each of the three measurements.Conclusion: Pre-term infants, especially those having problems, have significant hypothyroxinemia that may require thyroid hormone replacement therapy.Keywords: Prematurity, hypothyroxinemia, thyroid, TSH

Quantitative Assessment of Salivary Gland Parenchymal Vascularization Using Power Doppler Ultrasound and Superb Microvascular Imaging: A Potential Tool in the Diagnosis of Sjögren’s Syndrome

Background: Primary Sjögren’s syndrome is a chronic inflammatory autoimmune disease. Minor salivary gland biopsy is the gold standard for the diagnosis of primary Sjögren’s syndrome. Superb microvascular imaging, power Doppler ultrasound, and color Doppler of the salivary glands represent non-invasive, non-irradiating modality for evaluating the vascularity of the salivary glands in the diagnosis and follow-up of primary Sjögren’s syndrome. Aims: To evaluate the efficacy of superb microvascular imaging and vascularity index in salivary glands for the sonographic diagnosis of primary Sjögren’s syndrome. Study Design: Prospective case-control study. Methods: Twenty participants with primary Sjögren’s syndrome and 20 healthy subjects were included in the study. Both parotid glands and submandibular glands were evaluated by superb microvascular imaging, power Doppler ultrasound, and color Doppler. The diagnostic accuracy of superb microvascular imaging was compared using these techniques. Results: In the patient group, the vascularity index values of superb microvascular imaging in parotid glands and submandibular glands were 3.5±1.66, 5.06±1.94, respectively. While the same values were 1.0±0.98 and 2.44±1.34 in the control group (p?0.001). In the patient group, the vascularity index values of power Doppler ultrasound in parotid glands and submandibular glands were 1.3±1.20 and 2.59±1.82, respectively. While the same values were 0.3±0.32 and 0.85±0.68 in the control group (p?0.001). The superb microvascular imaging vascularity index cut-off value for the diagnosis of primary Sjögren’s syndrome in parotid glands that maximizes the accuracy was 1.85 (area under the curve: 0.906; 95% confidence interval: 0.844, 0.968), and its sensitivity and specificity were 87.5% and 72.5%, respectively. While the superb microvascular imaging vascularity index cut-off value for the diagnosis of primary Sjögren’s syndrome in submandibular gland that maximizes the accuracy was 3.35 (area under the curve: 0.873; 95% confidence interval: 0.800, 0.946), its sensitivity and specificity were 82.5% and 70%, respectively. Conclusion: Superb microvascular imaging with high reproducibility of the vascularity index has a higher sensitivity and specificity than the power Doppler ultrasound in the diagnosis of primary Sjögren’s syndrome. It can be a noninvasive technique in the diagnosis of primary Sjögren’s syndrome when used with clinical, laboratory and other imaging methods

Detection of Epileptic Seizure Using STFT and Statistical Analysis

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