The Grand Challenge of Quantum Computing: Bridging the Capacity Gap

Le Thi Lan, Tran Minh Quang

Quantum computing represents a revolutionary leap in computational science, promising to solve problems that are currently intractable for classical computers. This article provides an in-depth exploration of the theoretical foundations of quantum computing, the current state of research, and the practical applications that are beginning to bridge the gap between theory and real-world implementation. We discuss the challenges faced in the field, including qubit stability, error correction, and scalability, and explore future directions for quantum computing. By examining these aspects, this article aims to provide a comprehensive understanding of the transformative potential of quantum computing and its implications for advanced computing systems.

Paul M. B. Vitanyi

Ming-Lang Tun Hwang, Wi-Lang Collin, Lee Sen Wang-xu

Quantum computing is used to address supply chain optimization complexity and efficiency. Multiple locations, time periods, transportation expenses, facility opening costs, production capacity, and demand fulfillment requirements complicate supply chains. Supply chain optimization's complexity and huge solution areas challenge traditional optimization methods. Quantum algorithms can efficiently explore bigger solution areas in quantum computing. Starting with problem identification, this research reviews quantum computing and supply chain optimization literature. The supply chain optimization problem is modeled mathematically to incorporate transportation, facility opening, production, and cost. Binary choice factors and constraints ensure demand fulfillment, facility capacity limitations, and flow balance. The mathematical theory is applied numerically. The example addresses three locations, two time periods, transportation costs, demand amounts, production capacity, and facility opening costs. A proper optimization solver optimizes the decision variables to reduce total cost while meeting demand and making efficient supply chain decisions. The supply chain optimization model reduces costs and informs transportation, facility opening, and production decisions. The numerical example shows how quantum computing may optimize supply chain topologies and reduce costs. The study explains the findings, highlights gaps in the literature, and stresses the need for more research to bridge theory and practice. This study advances supply chain optimization with quantum computing. It shows how quantum computing might improve supply chain network decision-making, efficiency, and cost.

Aida Todri‐Sanial

Priyanshi Nayak

This research introduces a grand unified interdisciplinary framework from Element 1 to 118, bridging the conceptual gaps between classical physics, quantum mechanics, and organic biology under a singular governing principle: The Priority of Volitional Wave Dynamics. Challenging the traditional thermodynamic model, this study explores quantum panpsychism, nuclear proton autonomy, and the force-induced illusion of macroscopic solidity. By replacing static physical states with dynamic 4D fluid projections and attributing conscious agency, volition, and wave-field freedom to the isolated proton, the Priyanshi Nayak Framework offers a verifiable, elegant pathway toward understanding the latent consciousness governing atomic and molecular architectures.