cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Adam Mudinillah
Contact Email
adammudinillah@staialhikmahpariangan.ac.id
Phone
+6285379388533
Journal Mail Official
adammudinillah@staialhikmahpariangan.ac.id
Editorial Address
Jorong Kubang Kaciak Dusun Kubang Kaciak, Kelurahan Balai Tangah, Kecamatan Lintau Buo Utara, Kabupaten Tanah Datar, Provinsi Sumatera Barat, Kodepos 27293.
Location
Kab. tanah datar,
Sumatera barat
INDONESIA
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 74 Documents
 4   5   6   7   8 
Quantum Kernel Methods for High-Dimensional Generalization: Separating Quantum Advantage from Classical Simulability Amadou Ba; Ndeye Ndour; Oumar Gueye
Journal of Tecnologia Quantica Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i2.4159

Abstract

Quantum kernel methods have emerged as a promising approach for exploiting quantum feature representations to improve learning performance in high-dimensional classification tasks. Increasing advances in quantum computing have stimulated expectations of quantum advantage; however, distinguishing genuine quantum computational benefits from efficient classical simulability remains a fundamental challenge. This study aimed to evaluate the effectiveness of quantum kernel methods for high-dimensional generalization while identifying computational conditions under which quantum learning exceeds the capability of advanced classical approximation techniques. A mixed-methods sequential explanatory design was employed using 18,000 large-scale computational experiments complemented by benchmark datasets, quantum algorithm implementation records, computational complexity analyses, and expert evaluations. Quantitative data were analyzed through generalized linear mixed-effects modeling, Monte Carlo uncertainty estimation, multivariate regression, sensitivity analysis, and cross-validation, whereas qualitative evidence was interpreted using thematic analysis of technical documentation and expert perspectives. Findings demonstrated that quantum kernel methods achieved superior generalization when highly expressive quantum feature maps generated representations resistant to efficient classical simulation while maintaining robust statistical learning performance. Classical approximation algorithms successfully reproduced several shallow quantum kernels, indicating that predictive accuracy alone does not establish authentic quantum advantage. Results suggest that meaningful quantum superiority arises from the integrated interaction among feature-map expressivity, computational complexity, statistical generalization, and limited classical simulability. The proposed framework provides a rigorous foundation for evaluating scalable quantum machine learning systems and guiding the development of future fault-tolerant quantum artificial intelligence.
Ground-State Cooling of a Nanomechanical Resonator via Squeezed Light Injection in a Resolved-Sideband Regime Viktoria Sokolova; Igor Vasiliev; Natalya Pavlova
Journal of Tecnologia Quantica Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i2.4160

Abstract

Ground-state cooling of nanomechanical resonators is a fundamental requirement for realizing high-fidelity quantum control, precision sensing, and coherent quantum information processing. Conventional resolved-sideband cooling has achieved remarkable progress; however, quantum back-action, optical losses, thermal fluctuations, and finite cavity linewidths continue to limit cooling performance under realistic experimental conditions. This study aimed to evaluate the effectiveness of squeezed light injection in enhancing ground-state cooling while investigating the interactions among optical squeezing, optomechanical coupling, cavity dynamics, quantum back-action suppression, and thermal phonon reduction within the resolved-sideband regime. A mixed-methods sequential explanatory design was employed using 14,000 large-scale optomechanical simulations complemented by experimental benchmark datasets, cavity calibration records, laboratory implementation reports, and expert evaluations. Quantitative data were analyzed through generalized linear mixed-effects modeling, Monte Carlo uncertainty estimation, sensitivity analysis, and multivariate statistical techniques, whereas qualitative evidence was interpreted using thematic analysis of experimental observations and technical documentation. Findings demonstrated that squeezed-light-assisted cooling significantly reduced the final mean phonon occupation, improved ground-state occupation probability, suppressed quantum back-action, preserved mechanical coherence, and enhanced cooling efficiency across multiple optomechanical configurations. Results indicate that engineered quantum fluctuations provide an effective mechanism for overcoming practical limitations associated with conventional sideband cooling. The proposed framework establishes a robust foundation for developing scalable optomechanical platforms supporting quantum sensing, hybrid quantum systems, precision metrology, and future quantum information technologies.
Fault-Tolerant Logical Qubit Operations Beyond the Threshold: Surface Code Performance Under Correlated Noise Models Seema Bholah; Rakesh Seneviratne; Lila Ramchurn
Journal of Tecnologia Quantica Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i2.4161

Abstract

Fault-tolerant quantum computing is essential for realizing scalable quantum processors capable of executing reliable computations despite unavoidable physical errors and environmental disturbances. Conventional threshold theory predominantly assumes independent stochastic noise, whereas practical quantum hardware increasingly exhibits spatially and temporally correlated error processes that may significantly degrade logical qubit performance. This study aimed to evaluate the robustness of surface-code logical qubit operations under correlated noise models while examining the influence of decoder performance, syndrome extraction fidelity, code distance, and correlated error dynamics on fault-tolerant computation beyond conventional threshold assumptions. A mixed-methods sequential explanatory design was employed using 15,000 large-scale quantum simulations complemented by experimental benchmark datasets, quantum hardware calibration records, decoder implementation reports, and expert evaluations. Quantitative data were analyzed through generalized linear mixed-effects modeling, threshold analysis, Monte Carlo uncertainty estimation, and multivariate statistical techniques, whereas qualitative evidence was interpreted using thematic analysis of experimental observations and technical documentation. Findings demonstrated that optimized surface-code architectures maintained high logical gate fidelity and effective logical error suppression under moderate correlated noise conditions through accurate syndrome extraction and advanced decoding strategies. Decoder adaptation substantially mitigated correlated error propagation, preserving logical qubit stability despite realistic hardware imperfections. Results indicate that practical fault tolerance depends on the integrated interaction among correlated noise characterization, decoder intelligence, logical encoding, and hardware architecture rather than physical error rates alone. The proposed framework provides a comprehensive foundation for designing scalable, experimentally robust, and resource-efficient fault-tolerant quantum computing systems.
Quantum Computing Algorithms for Optimization Problems: A Study Based on QAOA and MAX-CUT Simulation Ndiana Okon Asuquo; Siti Mariam
Journal of Tecnologia Quantica Vol. 3 No. 2 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i2.3479

Abstract

Quantum computing represents a transformative computational paradigm capable of solving certainoptimization problems more efficiently than classical algorithms. Many real?world applicationsincluding logistics planning, transportation routing, network optimization, and machine learninginvolve combinatorial problems whose computational complexity grows exponentially with input size.This research investigates quantum algorithms for solving optimization problems, with emphasis onthe Quantum Approximate Optimization Algorithm (QAOA), Grover search techniques, andHamiltonian?based optimization frameworks. Mathematical formulations are developed for representingclassical optimization problems using quantum Hamiltonians, enabling their implementation inparameterized quantum circuits. Simulation experiments based on the MAX?CUT problem are conducted toevaluate algorithm performance. Benchmark comparisons between classical and quantum optimizationapproaches demonstrate improved scalability for quantum algorithms in simulated environments. Theresults suggest that hybrid quantum?classical optimization methods may offer practical advantagesfor solving medium?scale combinatorial problems on near?term quantum hardware. Keywords: Quantum Computing; QAOA; Combinatorial Optimization; MAX?CUT; Quantum Algorithms; Quantum Annealing

Page 8 of 8 | Total Record : 74

 4   5   6   7   8 

Filter by Year

2024 2026


Reset
Filter By Issues
All Issue Vol. 3 No. 2 (2026) Vol. 3 No. 1 (2026) Vol. 2 No. 6 (2025) Vol. 2 No. 5 (2025) Vol. 2 No. 4 (2025) Vol. 2 No. 3 (2025) Vol. 2 No. 2 (2025) Vol. 2 No. 1 (2025) Vol. 1 No. 6 (2024) Vol. 1 No. 5 (2024) Vol. 1 No. 4 (2024) Vol. 1 No. 3 (2024) Vol. 1 No. 2 (2024) Vol. 1 No. 1 (2024) More Issue