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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 7 Documents
 1 
Search results for , issue "vol. 3 no. 2 (2026)" : 7 Documents clear
The Post-Quantum Cryptography Challenge: A Security Analysis of Lattice-Based vs. Code-Based Algorithms Loso Judijanto; Zhou Hui; Sun Wei
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.v2i5.2794

Abstract

The emergence of large-scale quantum computers poses a critical threat to classical public-key cryptographic systems, prompting the rapid development of post-quantum cryptography as a foundational component of future digital security. Lattice-based and code-based algorithms have become leading candidates due to their strong conjectured resistance to quantum attacks; however, their comparative security characteristics remain insufficiently examined under unified analytical frameworks. This study aims to provide a comprehensive security analysis of lattice-based and code-based post-quantum cryptographic algorithms by evaluating their resilience against known classical and quantum attack vectors. A structured methodological approach is employed, combining complexity-theoretic assessment, parameter-sensitivity evaluation, and simulated attack modeling across representative schemes such as CRYSTALS-Kyber, NTRU, Classic McEliece, and BIKE. The results indicate that lattice-based schemes offer strong security margins under current attack models but exhibit notable sensitivity to parameter misconfiguration and structured lattice weaknesses. Code-based schemes demonstrate exceptional robustness due to the hardness of decoding random linear codes, yet face practical limitations in key size and implementation overhead. The study concludes that both families remain viable for post-quantum standardization, although their security assurances depend heavily on careful parameter selection and continued cryptanalytic scrutiny as quantum hardware evolves.
Thermodynamic Uncertainty Relations in Driven Open Quantum Systems: Bounds on Precision and Dissipation Beyond the Markovian Approximation Sofia Pereira; Carlos Lopes; Marta Carvalho
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.v3i2.4157

Abstract

Thermodynamic Uncertainty Relations in Driven Open Quantum Systems: Bounds on Precision and Dissipation Beyond the Markovian Approximation
Sub Shot Noise Phase Estimation via Adaptive Bayesian Protocols in Noisy Intermediate-Scale Quantum Sensors Helen Nabirye; Ronald Muwanguzi; Deborah Wanyama
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.v3i2.4158

Abstract

Quantum sensing has emerged as a transformative technology for precision measurement, offering the potential to surpass the classical shot-noise limit through quantum-enhanced estimation strategies. Practical implementation within Noisy Intermediate-Scale Quantum (NISQ) platforms, however, remains constrained by decoherence, gate imperfections, measurement uncertainty, and limited quantum resources that reduce achievable sensing accuracy. This study aimed to evaluate the effectiveness of adaptive Bayesian protocols in achieving sub-shot-noise phase estimation under realistic NISQ operating conditions while examining the roles of adaptive feedback, posterior convergence, quantum coherence, and resource optimization. A mixed-methods sequential explanatory design was employed using 12,000 quantum sensing simulations complemented by experimental benchmark datasets, laboratory implementation records, and expert evaluations. Quantitative data were analyzed through Bayesian performance analysis, repeated-measures statistical testing, multivariate regression, and Monte Carlo uncertainty estimation, whereas qualitative evidence was interpreted using thematic analysis of experimental observations and implementation reports. Findings demonstrated that adaptive Bayesian estimation significantly improved phase estimation accuracy, accelerated posterior convergence, enhanced Fisher information, preserved quantum coherence, and consistently achieved sub-shot-noise precision across diverse NISQ sensing architectures despite realistic noise conditions. Results indicate that intelligent probabilistic inference functions as a critical component of practical quantum metrology by dynamically optimizing measurement strategies and compensating for hardware limitations. The proposed framework provides a robust foundation for developing scalable, resource-efficient, and experimentally feasible quantum sensing systems for future scientific and industrial precision measurement applications.
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

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