Journal of Applied Statistics and Information Technology

Quantum computing: Overview and applications

Mon, 22 Jun 2026 00:00:00 +0700

Advancements in computer technologies have been evolving in order to provide new services or to improve recent computing performance. Applications of computer technologies to several real-world domains are becoming more realistic and efficient. Two challenges are taken into consideration when applying such technologies including more complicated user requirements and exponential growth of data. In the real world, which is driven by business, achieving user requirements is one promising way of yielding competitive advantages. Moreover, time constraints are also crucial for corporate users. Apart from structured data, unstructured data have been significantly generated via several platforms. Such aspects make data processing more demanding. Quantum technology, which is instantiated in physics, has been utilized in various fields including computer science. Even the real quantum computer is still in its early development, but other concepts such as quantum information and quantum computing are making progress. Furthermore, some quantum-based algorithms have been analytically proven to have higher performance compared to the traditional ones. According to such important milestones, quantum computing has been regarded as one of the key infrastructures of future computing. This paper aims to focus on the main aspects of quantum computing including its backgrounds and recent advancement to provide a clearer picture. Its recent applications to intelligent system enhancement, cybersecurity and cryptography empowerment, and finance are addressed. Several key ideas, and opportunities of quantum computing utilization are also outlined based upon systematic reviews of existing works and case studies. Moreover, comparative analysis between classical and quantum computing in terms of core principles, computational efficiency, and practical feasibility is given. Better understanding and proper preparation of applying quantum computing are thus achieved.

Overdispersed Count Data Using Negative Binomial-Quasi XGamma Distribution

Siriporn Samutwachirawong — Mon, 22 Jun 2026 00:00:00 +0700

This paper introduces the negative binomial-quasi xgamma (NB-QX) distribution, a three-parameter model derived by mixing the negative binomial and quasi xgamma distributions. The NB-QX distribution offers significant flexibility in analyzing overdispersed count data and subsumes the negative binomial-gamma distribution as a special case. We derive its fundamental properties, including the probability mass function, factorial moments, mean, and variance. Parameter estimation is conducted via the maximum likelihood method. The model’s performance is evaluated using three real-world count datasets, with goodness-of-fit assessed through the Kolmogorov–Smirnov (KS) test for discrete distributions, AIC, and BIC. The empirical results demonstrate that the NB-QX distribution outperforms Poisson, negative binomial, and negative binomial-gamma distributions, particularly in capturing overdispersion within a unified framework.

The Role of AI Literacy and Generative AI Reliance in Decision Making Tasks: Case Studies Using Business Simulation Games

Phaninthorn Swanyawatthaga, Thanachart Ritbumroong — Tue, 23 Jun 2026 00:00:00 +0700

This study examines how AI literacy, trust, and decision-making style shape reliance on Generative AI in high-stakes business decisions. Fifty-two participants completed an inventory management task in the MonsoonSIM business simulation, supported by a Generative AI advisor powered by Google Gemini.
We measured AI literacy, trust, decision-making style, and performance expectancy via survey, and reliance, decision quality, and business outcomes from participant responses and game data. AI literacy and performance expectancy significantly predicted reliance and trust, respectively. Avoidant decision-makers showed the strongest tendency to rely on AI, while rational decision-makers remained cautious. Reliance alone did not improve decision quality or business outcomes, but experience in data analysis buffered the negative effect of over-reliance on decision quality. These findings highlight the importance of AI literacy, trust calibration, and analytical competence, rather than reliance itself, in shaping effective human-AI decision-making.