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This Author published in this journals
All Journal Journal of Tecnologia Quantica Journal of Loomingulisus ja Innovatsioon
Aram Hakobyan
Yerevan State University
Religion Computer Science & IT Environmental Science Law, Crime, Criminology & Criminal Justice Physics Other

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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.
Development of Innovative Products Based on Local Wisdom to Increase the Competitiveness of MSMEs Levon Galstyan; Aram Hakobyan; Leyla Guliyeva
Journal of Loomingulisus ja Innovatsioon Vol. 2 No. 2 (2025)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/innovatsioon.v2i2.1976

Abstract

Micro, Small, and Medium Enterprises (MSMEs) play a crucial role in the economic development of many countries, including Indonesia. However, MSMEs often face challenges in enhancing competitiveness, especially in the global market. One potential solution lies in the development of innovative products based on local wisdom, which can add unique value and appeal. This study aims to explore how MSMEs can develop innovative products based on local wisdom to enhance their competitiveness. Specifically, it investigates the integration of local cultural values, traditional knowledge, and sustainable practices into product design and development processes within MSMEs. This research employs a qualitative case study approach, focusing on several MSMEs in Indonesia that have successfully incorporated local wisdom into their product offerings. Data was collected through interviews with business owners, product designers, and local community members, along with field observations. The study also analyzed secondary data from industry reports and market trends. The findings indicate that MSMEs that integrate local wisdom into their products can significantly improve their market differentiation and appeal. The study identifies key factors such as cultural authenticity, sustainable sourcing, and the ability to tell a compelling product story as essential to enhancing competitiveness.
Co-Authors Ali Mohamed Carlos González Levon Galstyan Leyla Guliyeva