Automatic data processing

Frank H. George

Ruihong Jia, Guang Yang, Min Nie +2 more

With the advent of the era of big data, how to efficiently process data has become an urgent problem. Although classical machine learning algorithms are very mature in processing data, they require a large amount of data to complete the training process. Quantum machine learning uses the superposition, entanglement, parallelism and other characteristics of quantum computing to improve the performance of classical machine learning algorithms. At the same time, circuit optimization of the quantum classifier can reduce the computational complexity. This paper proposes an automatic optimization model of the classifier based on the variational quantum algorithm, which reduces the complexity of the parameter update process through the deep optimization of the core circuit. This model greatly speeds up the training process and has lower complexity than other quantum classifiers. In addition, a unique regularization method is used to avoid overfitting. The simulation results show that the classification of some typical data sets can achieve better classification results, and the training parameters and training speed are significantly better than other quantum classifiers. Finally, the robustness of the classifier is further verified in the presence of noise.

Rizka Khairunnisa, Dion Ogi, Anang Adi Permana +7 more

Quantum cryptography learning is now more important as the threats toward secure communication system using quantum computing are increasing. EDU-QCRY is one of the quantum cryptography demonstration devices which implements BB84 protocol. EDU-QCRY is still run manually that make it has limited data processing capacity, large operating time, and low-level accuracy. In this study, automation is done by parallelizing the process of selecting bases and states on the polarization rotator and integrating an Arduino-based controller module using demultiplexing techniques. EDU-QCRYI automation changes the process to be more efficient and effective because the system works automatically by a controller module based on qubit data that is inputted by the sender and the receiver gets qubit data automatically. EDU-QCRYI automation is expected to make demonstration of secure communication using Quantum cryptography more reliable for implementation.

Vlad Stirbu, Arianne Meijer van de Griend

Understanding the effectiveness of quantum compilation techniques requires visibility into the entire transpilation process, not just the final circuit metrics. This demonstration presents an MLflow-inspired autologging framework for Qiskit that automatically captures compiler provenance, including transpilation stages, pass-level execution data, backend characteristics, compiler configuration, and execution results. The framework extends the QProv provenance model with compiler-specific information and stores the collected data in an MLflow Tracking Server for analysis and visualization. By eliminating manual instrumentation, the proposed approach improves compiler observability and supports reproducible evaluation of quantum compilation workflows.

T. Van Duzer

Hafiz Md. Hasan Babu