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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 74 Documents
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Quantum Neural Networks: Advantages in Processing High-Dimensional Hilbert Space Data Eka Utaminingsih; , Luca Santi; Sione Kakala
Journal of Tecnologia Quantica Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i1.3388

Abstract

Quantum machine learning has emerged as a promising paradigm for addressing the limitations of classical learning models in handling data with exponentially growing dimensionality. In particular, many problems in physics, chemistry, and quantum information are naturally represented in high-dimensional Hilbert spaces, where classical neural networks face significant challenges related to representation efficiency and scalability. This study aims to analyze the advantages of quantum neural networks in processing data embedded in high-dimensional Hilbert spaces and to clarify the structural sources of their potential superiority over classical architectures. The research adopts a theoretical–computational approach that combines analytical modeling with numerical simulations of variational quantum circuits and comparable classical neural network models across increasing dimensional regimes. Performance is evaluated in terms of learning fidelity, parameter scaling behavior, and stability under dimensional growth. The results show that quantum neural networks consistently maintain higher fidelity with substantially fewer parameters as Hilbert space dimensionality increases, while classical models exhibit rapid performance degradation and escalating complexity. These findings indicate that quantum neural networks benefit from intrinsic alignment with Hilbert space geometry through superposition and entanglement. In conclusion, the study demonstrates that quantum neural networks constitute a distinct and scalable learning framework for high-dimensional data, supporting their relevance for future quantum-enhanced machine learning applications..
Heisenberg-Limited Metrology: Utilizing Entangled States for Ultra-Precise Gravitational Wave Detection Nurul Huda; Ming Pong; Pedro Silva
Journal of Tecnologia Quantica Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i1.3579

Abstract

Gravitational wave detection has reached unprecedented sensitivity through interferometric technologies, yet it remains fundamentally constrained by quantum noise, particularly the standard quantum limit (SQL). Advances in quantum metrology suggest that entangled states can surpass classical limits and approach the Heisenberg limit, offering a pathway to ultra-precise measurements. This study aims to investigate the potential of entangled quantum states to enhance sensitivity in gravitational wave detectors under realistic conditions. A theoretical–computational approach was employed, combining analytical modeling with large-scale numerical simulations of interferometric systems. Various quantum states, including NOON states, squeezed states, and hybrid entangled–squeezed configurations, were evaluated using quantum Fisher information and phase variance as performance metrics. The results indicate that entangled states achieve Heisenberg-limited scaling in ideal conditions, significantly outperforming classical and squeezed states. Hybrid states demonstrate superior robustness against loss and decoherence, maintaining enhanced sensitivity in non-ideal environments. These findings suggest that the integration of entangled states into interferometric detectors can substantially reduce quantum noise and improve detection capabilities. This study concludes that entanglement-based metrology offers a promising and practical pathway toward next-generation gravitational wave detection with ultra-high precision.
Quantum Sensing of Weak Magnetic Fields using Diamond NV Centers in Biological Environments Rithy Vann; Amir Raza; Nomsa Zulu
Journal of Tecnologia Quantica Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i1.3581

Abstract

Quantum sensing using nitrogen-vacancy (NV) centers in diamond has emerged as a powerful approach for detecting extremely weak magnetic fields with high spatial resolution and ambient operational conditions. Despite their proven sensitivity in controlled environments, the performance of NV-based sensors in biological systems remains challenged by decoherence, optical scattering, and environmental noise. This study aims to investigate the capability of diamond NV centers to detect weak magnetic fields in biologically relevant environments and to evaluate the factors influencing their performance. An experimental–computational approach was employed, combining optical detection of magnetic resonance (ODMR) measurements with simulations of spin dynamics under varying environmental conditions. Nanodiamond samples were tested across buffer solutions, cell culture media, and tissue-like environments. The results indicate that NV centers retain the ability to detect weak magnetic fields in biological settings, although sensitivity decreases due to reduced coherence time and optical contrast. Surface functionalization improves stability and partially mitigates environmental effects, enhancing overall sensor performance. These findings suggest that NV-based quantum sensors offer a promising platform for non-invasive biological magnetometry, provided that material engineering and noise mitigation strategies are optimized. This study concludes that integrating quantum sensing with biological systems is feasible and can advance applications in biomedical diagnostics and cellular imaging..
Adaptive Quantum State Tomography: Reconstructing High-Dimensional States with Minimal Measurements Aram Hakobyan; Carlos González; Ali Mohamed
Journal of Tecnologia Quantica Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i1.3582

Abstract

Quantum state tomography is essential for characterizing quantum systems, yet conventional methods suffer from exponential scaling in measurement requirements, limiting their applicability in high-dimensional systems. Efficient reconstruction of quantum states with minimal measurements has become a critical challenge in advancing quantum information technologies. This study aims to develop and evaluate an adaptive quantum state tomography framework capable of reconstructing high-dimensional quantum states with reduced measurement resources while maintaining high accuracy. A theoretical–computational approach was employed, integrating Bayesian adaptive measurement strategies with convex optimization–based reconstruction algorithms. Simulations were conducted across varying system dimensions, state types, and noise conditions to assess performance. The results indicate that the proposed adaptive method significantly reduces the number of required measurements by up to 75% while achieving reconstruction fidelity comparable to full tomography. The approach demonstrates strong robustness under moderate noise and exhibits faster convergence compared to compressed sensing techniques. These findings suggest that adaptive quantum state tomography provides an efficient and scalable solution for quantum state reconstruction. This study concludes that integrating adaptive measurement selection with optimized reconstruction algorithms can overcome fundamental scalability challenges and support the development of practical quantum technologies.
Quantum Lithography: Achieving Sub-Diffraction Resolution using N00N States and Multi-Photon Absorption Elchin Mammadov; Kerry John; John Langa
Journal of Tecnologia Quantica Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i2.3583

Abstract

Classical optical lithography is fundamentally limited by the diffraction limit, restricting achievable resolution in nanoscale fabrication. Quantum lithography has been proposed as a solution by exploiting entangled photon states, particularly N00N states, which enable interference patterns with sub-wavelength spacing. This study aims to investigate the feasibility of achieving sub-diffraction resolution using N00N states combined with multi-photon absorption processes under realistic conditions. A theoretical–computational approach was employed, integrating quantum optical modeling with numerical simulations across varying photon numbers, absorption orders, and loss parameters. Spatial resolution, fringe visibility, and absorption efficiency were used as key performance metrics. The results indicate that N00N states achieve resolution scaling inversely with photon number, successfully surpassing the classical diffraction limit. However, increased photon number significantly reduces multi-photon absorption probability and makes the system more sensitive to loss and decoherence. These findings reveal a fundamental trade-off between resolution enhancement and detection feasibility. This study concludes that quantum lithography offers a powerful pathway for sub-diffraction patterning, but practical implementation requires optimization of photon number, absorption efficiency, and system robustness to environmental disturbances.
Benchmarking Quantum Annealers vs. Classical Solvers for Complex Optimization Problems in Financial Modeling Muh. Nur; Rina Farah; Nina Anis
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.
Engineering Hybrid Quantum Systems: Strong Coupling Between Nitrogen-Vacancy Centers and a Superconducting Resonator Herri Trisna Frianto; Ming Pong; Rit Som
Journal of Tecnologia Quantica Vol. 2 No. 3 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v2i3.2602

Abstract

Hybrid quantum systems that integrate solid-state qubits with superconducting circuits have emerged as a promising architecture for scalable quantum information processing. Achieving strong coherent coupling between distinct quantum subsystems, such as spin ensembles and microwave resonators, remains a critical challenge in realizing hybrid quantum technologies. This study aims to engineer and characterize a hybrid platform that couples nitrogen-vacancy (NV) centers in diamond with a superconducting coplanar waveguide resonator. A combination of cryogenic microwave spectroscopy and time-domain measurements was employed to evaluate coupling strength, coherence times, and collective spin photon interactions at millikelvin temperatures. The experimental results demonstrated a vacuum Rabi splitting of 22 MHz, confirming the realization of a strong coupling regime between the NV spin ensemble and the superconducting resonator. The coherence lifetime of the NV centers remained above 100 ?s under optimized magnetic field alignment, ensuring stable quantum-state transfer. The findings reveal that hybrid systems combining spin-based and superconducting components can serve as viable interfaces for quantum memory and quantum communication nodes. The study concludes that engineering such strong spin–photon coupling represents a foundational step toward the development of coherent, scalable hybrid quantum networks.
Fault-Tolerant Quantum Computing: Engineering Surface Codes for Scalable Error Correction Agustinus Suradi; Mohamed Ould El Had; Aicha Mint Mohamed
Journal of Tecnologia Quantica Vol. 3 No. 1 (2026)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/quantica.v3i1.3389

Abstract

Fault-tolerant quantum computing is a fundamental requirement for realizing large-scale, reliable quantum processors, as quantum information is inherently vulnerable to noise, decoherence, and operational imperfections. Among existing quantum error correction schemes, surface codes are widely regarded as the most promising approach due to their high error thresholds and compatibility with realistic hardware constraints. This study aims to investigate how surface codes can be engineered to support scalable fault-tolerant quantum computing under non-ideal noise conditions. The research employs a computational and engineering-oriented methodology based on numerical simulations of surface code architectures with varying code distances, physical error rates, and decoding strategies. Performance is evaluated using logical error rates, threshold behavior, and classical decoding overhead as key indicators. The results demonstrate that surface codes achieve exponential suppression of logical errors in sub-threshold regimes, confirming their robustness for scalable error correction. However, the findings also reveal that classical decoding complexity and correlated noise effects emerge as dominant constraints at larger scales. These results indicate that fault tolerance is not solely determined by quantum error correction theory but arises from the integrated performance of quantum hardware and classical processing systems. In conclusion, the study establishes that scalable fault-tolerant quantum computing requires a co-design approach that simultaneously optimizes surface code architecture, noise mitigation, and decoding efficiency to ensure reliable large-scale quantum computation.
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.

Page 7 of 8 | Total Record : 74

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