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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 65 Documents
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Quantum Neural Networks: Advantages in Processing High-Dimensional Hilbert Space Data Utaminingsih, Eka; Santi, , Luca; Kakala, Sione
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.

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