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Journal of Tecnologia Quantica
Published by Yayasan Adra Karima Hubbi
ISSN : 30626757     EISSN : 30481740     DOI : 10.70177/quantica
Core Subject : Science,
Physics
Journal of Tecnologia Quantica is dedicated to bringing together the latest and most important results and perspectives from across the emerging field of quantum science and technology. Journal of Tecnologia Quantica is a highly selective journal; submissions must be both essential reading for a particular sub-field and of interest to the broader quantum science and technology community with the expectation for lasting scientific and technological impact. We therefore anticipate that only a small proportion of submissions to Journal of Tecnologia Quantica will be selected for publication. We feel that the rapidly growing QST community is looking for a journal with this profile, and one that together we can achieve. Submitted papers must be written in English for initial review stage by editors and further review process by minimum two international reviewers.
Arjuna Subject : Fisika dan Astronomi - Fisika Nuklir dan Molekuler, dan Ooptik
Articles 5 Documents
 1 
Search results for , issue "Vol. 1 No. 4 (2024)" : 5 Documents clear
Quantum Neural Network for Medical Image Pattern Recognition Vann, Dara; Rith, Vicheka; Sothy, Chak
Journal of Tecnologia Quantica Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v1i4.1679

Abstract

The background of this research focuses on the recognition of medical image patterns for disease detection using artificial intelligence technology. Although Convolutional Neural Networks (CNNs) have been widely used, the models are limited in terms of accuracy and efficiency in processing complex medical images. Quantum Neural Networks (QNNs) are considered as a potential solution to address this problem, by leveraging quantum computing to improve speed and accuracy. The purpose of this study is to explore the application of QNN in the recognition of medical image patterns, as well as to compare its performance with more conventional CNN models. The study used a dataset of medical images from cancer and heart disease, which were divided into training and testing data. QNN and CNN were tested on the same dataset to compare accuracy, speed, and efficiency. The results showed that QNN produced 92% accuracy in breast cancer detection, higher than CNN which only reached 88%. QNN is also more efficient in terms of processing speed, with lower use of computing resources. The conclusion of this study shows that QNN has great potential to be used in the recognition of medical image patterns, with significant advantages in terms of accuracy and efficiency. This research paves the way for the further development of QNN technology in medical applications and disease diagnosis.
Quantum Machine Learning for Early Detection of Chronic Diseases Silva, Pedro; Costa, Bruna; Lima, Rafaela
Journal of Tecnologia Quantica Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v1i4.1680

Abstract

The background of this research focuses on t, Malaysiahe development of early detection methods for chronic diseases using Quantum Machine Learning (QML). Chronic diseases such as diabetes, hypertension, heart disease, and cancer are often detected too late, leading to preventable complications. This study aims to explore the potential of QML in improving the accuracy and speed of diagnosis by combining clinical data and medical images. The method used involves the application of quantum machine learning algorithms to analyze medical datasets that include numerical information and medical images such as CT scans and MRIs. The results show that QML can process data faster and more accurately than traditional machine learning methods. QML is also capable of detecting hidden patterns in data that cannot be found with conventional techniques. The conclusion of this study shows that Quantum Machine Learning offers an effective new approach for the early detection of chronic diseases. This technology can improve healthcare systems by providing faster and more accurate predictions, which can reduce mortality rates from chronic diseases. Further research is needed to expand QML applications and address current hardware limitations
Implementation of Quantum Error Correction Code on Qubit Superconducting to Improve Quantum Computing Stability Khan, Jamil; Akhtar, Shazia; Ali, Zara
Journal of Tecnologia Quantica Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v1i4.1681

Abstract

The background of this research focuses on the stability of quantum computing, which is a major challenge in the development of quantum technology. Superconducting qubits are known to be prone to errors due to environmental disturbances and noise, which hinders computational accuracy. Quantum error correction code (QECC) emerged as a solution to solve the problem by detecting and correcting errors that occur in qubits. This study aims to evaluate the application of QECC to superconducting qubits in improving the stability and accuracy of quantum computing. The method used was a quantitative experiment by comparing the qubit error rate before and after the implementation of QECC, with measurements on bit-flip, phase-flip, and decoherence errors. The results showed that the application of QECC successfully reduced the bit-flip and phase-flip error rates from 15.3% to 5.2% and 12.4% to 4.8%, respectively, while the decoherence decreased from 25.6% to 9.3%. These findings suggest that QECC can significantly improve the stability of quantum computing on superconducting qubits. The conclusion of this study is that the implementation of QECC can be an important step in improving efficiency and accuracy in quantum computing systems, although there are still limitations related to scalability and resources required for deployment in larger systems
Development of Quantum Noise-Based Quantum Random Number Generator (QRNG) Xiang, Yang; Jing, Wang; Wei, Sun
Journal of Tecnologia Quantica Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v1i4.1682

Abstract

The background of this research focuses on the development of a quantum noise-based Quantum Random Number Generator (QRNG) to generate random numbers that are safer and more efficient compared to conventional methods. Quantum fluctuation-based QRNG has the potential to generate more unpredictable numbers, improving security in cryptographic and simulation applications. The purpose of this research is to develop a QRNG system that can generate high-quality random numbers with various experimental settings and conditions. The method used is an experiment measuring quantum fluctuations through a photon detector to generate a random number based on quantum noise, followed by statistical testing to test the quality of the randomness. The results show that quantum noise-based QRNG is able to generate random numbers with better quality than conventional random number generators, with p-values that indicate very high random uncertainty. In addition, these QRNGs can operate at various photon intensities without compromising the random quality produced. The conclusion of this study is that quantum noise-based QRNG offers a safer and more efficient solution in generating random numbers for applications that require high randomness. Further research is needed to improve efficiency and overcome implementation obstacles in the real world.
Quantum Metrology for High-Precision Measurement of Fundamental Constants Tan, Jaden; Tan, Marcus; Chan, Rachel
Journal of Tecnologia Quantica Vol. 1 No. 4 (2024)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v1i4.1683

Abstract

High-precision measurements of fundamental constants have an important role in modern physics and technology. Uncertainty in measurements using classical methods is a major obstacle in improving the accuracy and validation of physical theories. Quantum metrology, which makes use of the phenomenon of quantum entanglement and superposition, offers a solution to overcome these limitations. This study aims to evaluate the effectiveness of quantum metrology in improving the measurement accuracy of fundamental constants, such as Planck's constant and Newton's gravity. The research was conducted using an experimental design with quantum sensing-based devices, such as quantum interferometers and ion traps. The data were analyzed to compare the level of measurement uncertainty between classical methods and quantum metrology. Case studies were conducted in a microgravity environment to test the reliability of this technology under extreme conditions. The results showed that quantum metrology significantly reduced measurement uncertainty to two orders of magnitude compared to classical methods. The technology has also proven to be effective in extreme conditions, providing flexibility for applications outside of the laboratory. The conclusion of the study confirms that quantum metrology is able to overcome the limitations of classical methods and has great potential to support the development of global measurement standards in the future.  

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