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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. 2 No. 4 (2025)" : 5 Documents clear
Quantum Bayesianism: Interpretation of Probability in Quantum Mechanics Judijanto, Loso
Journal of Tecnologia Quantica Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i1.1956

Abstract

Quantum mechanics presents challenges in understanding probability, which is often seen as a measure of uncertainty in quantum systems. Quantum Bayesianism (QBism) is an alternative interpretation that considers probability as an observer's subjective belief, not as an objective representation of the state of the system. This study aims to delve deeper into the role of probability in quantum mechanics through the perspective of QBism. This study aims to examine the differences between Quantum Bayesianism and traditional quantum probability interpretations, as well as analyze how QBism can provide a more dynamic understanding of probability in quantum experiments. The methods used include literature analysis to identify publication trends related to QBism as well as case studies of quantum experiments that show the application of subjective probability theory. Data is obtained from various scientific sources and the latest publications in the field of quantum physics. The results show that Quantum Bayesianism provides a more flexible and subjective approach to probability, which allows probabilities to be calculated based on the observer's beliefs and can change according to the information obtained. The study also confirms that more and more researchers are adopting QBism in their research, replacing the more traditional view of objective probability. The study concluded that QBism offers a more relevant and applicable view of probability in quantum mechanics. Although there are still limitations in practical application, QBism opens up new opportunities in the understanding and development of quantum technology in the future.
Diamond-Based Quantum Sensors for High-Resolution Magnetic Field Imaging of Neural Activity A, Muhammad Firdaus; Tan, Ethan; Lee, Ava
Journal of Tecnologia Quantica Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i5.2795

Abstract

Advances in quantum sensing technologies have opened new opportunities for noninvasive, high-resolution detection of neural activity, particularly through diamond-based quantum sensors utilizing nitrogen–vacancy (NV) centers. Conventional neuroimaging techniques often face limitations in spatial resolution, temporal precision, and sensitivity to weak magnetic fields generated by neuronal currents. These constraints motivate the development of quantum-enhanced sensing approaches capable of capturing neural dynamics with unprecedented fidelity. This study aims to evaluate the performance of diamond-based quantum sensors for high-resolution magnetic field imaging and to assess their potential for real-time neural activity monitoring. A combined experimental and simulation-based methodology was employed, involving controlled magnetic field measurements using NV-center ensembles, calibration against established magnetometry systems, and computational modeling of neuronal magnetic signatures. The results show that NV-based sensors achieve sub-micron spatial resolution and detect magnetic fields in the nanotesla range, significantly outperforming traditional optical and electromagnetic techniques. The findings further demonstrate strong temporal responsiveness, enabling the reconstruction of fast neuronal firing patterns. The study concludes that diamond-based quantum sensors represent a promising frontier for next-generation neuroimaging, offering a scalable, minimally invasive platform for studying neural circuits with high spatial–temporal precision.
Quantum Machine Learning for Drug Discovery: Accelerating the Simulation of Molecular Hamiltonians on Noisy Intermediate-Scale Quantum (NISQ) Devices Santos, Luis; Reyes, Maria Clara; Gonzales, Samantha; Anurogo, Dito
Journal of Tecnologia Quantica Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i5.2796

Abstract

Drug discovery increasingly relies on accurate simulation of molecular Hamiltonians, yet classical computational methods face exponential scaling barriers when modeling complex quantum systems. Recent advances in quantum machine learning (QML) and the availability of Noisy Intermediate-Scale Quantum (NISQ) devices offer new opportunities to accelerate molecular simulation despite hardware noise and qubit limitations. This study aims to evaluate the effectiveness of QML-based variational algorithms in improving the efficiency and accuracy of Hamiltonian simulation for drug-relevant molecules on NISQ platforms. A hybrid quantum–classical methodology was employed, combining variational quantum eigensolvers, noise-aware circuit optimization, and supervised learning models trained to predict energy landscapes. Experimental simulations were performed using IBM-Q and Rigetti NISQ architectures, supported by classical benchmarks for validation. The results demonstrate that QML-enhanced variational circuits significantly reduce computational depth while maintaining competitive accuracy compared to classical methods, particularly for medium-sized molecular systems. The findings also reveal that noise-adaptive training improves algorithm robustness, enabling more reliable energy estimation under realistic quantum noise conditions. The study concludes that QML provides a promising pathway for accelerating early-stage drug discovery by enabling efficient molecular Hamiltonian simulation on current-generation quantum hardware. Further integration of error mitigation and scalable QML frameworks will be essential for future advancements.
Quantum Nanorobotics: A Proposal for Quantum-Enhanced Actuation and Sensing at the Molecular Scale Frianto, Herri Trisna; A, Muhammad Firdaus; Aslam, Bilal
Journal of Tecnologia Quantica Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i5.2884

Abstract

Quantum nanorobotics has emerged as a promising interdisciplinary field aimed at enabling precise manipulation and sensing at the molecular scale, where classical mechanical approaches face fundamental limitations. The purpose of this study is to propose a unified framework for quantum-enhanced actuation and sensing that leverages quantum mechanical effects as functional resources in nanorobotic systems. The research adopts a conceptual–theoretical design supported by computational modeling and simulation grounded in quantum mechanics and quantum control theory. Simulation-based analyses demonstrate that quantum-enhanced sensing achieves significantly higher sensitivity, lower noise variance, and reduced energy consumption compared to classical nanoscale sensors, while quantum-based actuation exhibits improved precision, faster response times, and enhanced stability under environmental noise. The integrated sensing–actuation architecture reveals synergistic performance gains that surpass isolated enhancements, enabling reliable molecular-scale navigation and task execution. The study concludes that quantum coherence and tunneling can be systematically engineered to overcome classical constraints in nanorobotics, establishing quantum-enhanced control as a viable design paradigm. The novelty of this research lies in its integrative conceptual framework that unifies quantum sensing and actuation within a single nanorobotic architecture, providing a foundational model for future experimental development and interdisciplinary applications.
Benchmarking Quantum Annealers vs. Classical Solvers for Complex Optimization Problems in Financial Modeling Nur, Muh.; Farah, Rina; Anis, Nina
Journal of Tecnologia Quantica Vol. 2 No. 4 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i4.2601

Abstract

Quantum annealing has emerged as a promising computational paradigm for solving large-scale combinatorial optimization problems that are traditionally intractable for classical algorithms. The financial modeling sector, characterized by complex portfolio optimization, risk minimization, and option pricing problems, offers a fertile ground for benchmarking the performance of quantum versus classical solvers. This study aims to systematically evaluate the computational efficiency, scalability, and accuracy of quantum annealers specifically the D-Wave Advantage system against leading classical optimization algorithms, including simulated annealing and branch-and-bound methods. A comparative experimental framework was developed to test both solver types on real-world financial datasets encompassing portfolio selection and risk-parity optimization tasks. Quantitative performance metrics such as solution quality, convergence time, and energy landscape exploration were assessed. Results revealed that quantum annealers achieved near-optimal solutions significantly faster for high-dimensional problem instances with non-convex cost functions, whereas classical solvers maintained superior consistency for smaller, well-conditioned models. The findings suggest a complementary paradigm where quantum annealing can accelerate subproblems within hybrid financial optimization pipelines. The study concludes that quantum computing, while not yet universally superior, represents a viable accelerator for specific financial optimization classes under current hardware constraints.

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