Kuantum Teknolojileri ve Enformatik Araştırmaları Dergisi

Quantum-Enhanced Conformal Methods for Multi-Output Uncertainty: A Holistic Exploration and Experimental Analysis

Davut Emre Tasar — 2025-04-05

Quantum computing introduces unique forms of randomness arising from measurement processes, gate noise, and hardware imperfections. Ensuring reliable uncertainty quantifi- cation in such quantum-driven or quantum-derived predictions is an emerging challenge. In classical machine learning, conformal prediction has proven to be a robust frame- work for distribution-free uncertainty calibration, often focusing on univariate or low- dimensional outputs. Recent advances have extended conformal methods to handle multi-output or multi-dimensional responses, addressing sophisticated tasks such as time-series, image classification sets, and quantum-generated probability distributions. However, bridging the gap between these powerful conformal frameworks and the high- dimensional, noise-prone distributions typical of quantum measurement scenarios remains largely open. In this paper, we propose a unified approach to harness quantum conformal methods for multi-output distributions, with a particular emphasis on two experimental paradigms: (i) a standard 2-qubit circuit scenario producing a four-dimensional outcome distribution, and (ii) a multi-basis measurement setting that concatenates measurement probabilities in different bases (Z, X, Y) into a twelve-dimensional output space. By combining a multi- output regression model (e.g., random forests) with distributional conformal prediction, we validate coverage and interval-set sizes on both simulated quantum data and multi-basis measurement data. Our results confirm that classical conformal prediction can effectively provide coverage guarantees even when the target probabilities derive from inherently quantum processes. Such synergy opens the door to next-generation quantum-classical hybrid frameworks, providing both improved interpretability and rigorous coverage for quantum machine learning tasks. All codes and full reproducible Colab notebooks are made available at https://github.com/detasar/QECMMOU.

Iterative Solutions for Certain Complex Coefficient Linear Systems: Jacobi and Gauss-Seidel Methods

Ahmet Zahid Küçük — 2025-04-11

In this study, the performance of the Jacobi and Gauss-Seidel iteration methods for solving systems of linear equations with complex coefficients is analyzed. The coefficient matrix of the system is transformed into a real coefficient system by separating the real and imaginary parts. The study aims to compare the accuracy and computational efficiency of these methods within the context of selected examples, while also evaluating their convergence behavior. The findings demonstrate that, for the examples considered, the Gauss-Seidel method converges faster and with lower initial errors compared to the Jacobi method.

Establishing of Quantum Voltage Standard in Cryogenic Cooler at TÜBİTAK UME

Mehedin Arifoviç — 2025-04-05

This paper describes in detail the implementation of a programmable Josephson voltage standard in a cryogenic cooler at the National Metrology Institute of Türkiye (TÜBİTAK Ulusal Metroloji Enstitüsü). This work includes the installation of a 10 V Josephson array in the cryostat, setting the devices for its operation, the preparation of software for system control and optimization, and the testing of the complete system.

Why E-Commerce Startups Fail: can machine learning provide solution?

Mona Yadegar — 2025-04-05

E-commerce has transformed how businesses operate, providing customers with convenience and companies with access to global markets. However, despite its vast potential, many e-commerce initiatives have failed due to either external conditions such as local or global market fluctuations or internal conditions such as a mixture of poor planning, financial mismanagement, operational inefficiencies, and cybersecurity risks. Focusing on the market fluctuations which is a key component for external conditions. A simulative dataset that mimics real-world market conditions is used to present contribution of machine learning to decision making stages. The usage of informatics could help mitigate these risks by improving decision-making, security, and operational efficiency, and in turn could prevented many of the failures.