Journal of Tecnologia Quantica

The Post-Quantum Cryptography Challenge: A Security Analysis of Lattice-Based vs. Code-Based Algorithms

Loso Judijanto — 2025-12-23

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.

Diamond-Based Quantum Sensors for High-Resolution Magnetic Field Imaging of Neural Activity

Muhammad Firdaus A — 2025-12-23

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

Luis Santos — 2025-12-23

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

Herri Trisna Frianto — 2025-12-23

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.