A Quantum Model of Supply and Demand

Luís Tarrataca, Andreas Wichert

Huilin Zhao

Abstract:Traditional supply and demand theory has significant limitations in the digital economy scenario,making it difficult to adapt to typical characteristics such as unlimited supply and demand overlap of digital goods.This study innovatively proposes the theory of quantum supply and demand field,integrating the core principles of quantum mechanics to systematically reshape the basic assumptions and analytical framework of supply and demand theory.Research accurately characterizes the strategic decision-making behavior of suppliers and demanders using quantum states,and transmits market supply and demand fluctuations through quantum fields,breaking through the traditional theory's reliance on physical contact or linear conduction.The core breakthrough lies in abandoning the traditional single equilibrium perspective and shifting towards probability distribution equilibrium analysis,constructing a complete mathematical model that includes supply side and demand side quantum states,and establishing equilibrium conditions based on quantum inner product derivation.Multi case empirical analysis has verified the effectiveness and applicability of this theory,which can accurately explain core issues such as the superposition characteristics of supply and demand in the digital economy,non local correlations between markets,and provide new analytical tools for market analysis,policy formulation,and corporate strategic decision-making in the digital economy.At the same time,it promotes the innovative evolution of economic research paradigms,expands the application boundaries and research dimensions of supply and demand theory. Keywords:quantum supply-demand field theory;Quantum states on the supply and demand side;Demand side quantum state;Probability distribution equilibrium

Christian Aspalter

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.

Dimitri O. Ledenyov, Viktor O. Ledenyov

Sunil Kumar Sehrawat, Pushan Kumar Dutta, Ashima Bhatnagar Bhatia +1 more

This chapter explores the application of quantum machine learning (QML) techniques for demand prediction in supply chain networks. Traditional demand forecasting methods often struggle to capture the intricate dynamics and uncertainties present in modern supply chains. By leveraging the computational power and probabilistic nature of quantum computing, coupled with the flexibility and adaptability of machine learning algorithms, organizations can enhance the accuracy and efficiency of their demand prediction processes. This chapter provides an overview of QML methodologies tailored specifically for demand prediction in supply chain networks, highlighting their advantages over classical approaches. Through case studies and practical examples, the chapter demonstrates how QML can enable organizations to make more informed decisions, optimize inventory levels, and improve overall supply chain performance.