In this study, we focus on estimating financial crashes within a network of small and medium enterprises (SMEs) that are customers of Yapi Kredi Bank. These SMEs have complex financial relationships involving receivables and payables among each other. We developed a quadratically constrained quadratic program as a mathematical optimization model aiming at identifying significant vulnerabilities and potential failure points in this network. By defining key parameters such as crossholding ratios, self-holding fractions, asset ownership proportions, and failure cost functions, we capture the intricate dependencies between these entities. The model is inherently nonlinear due to the quadratic terms and constraints, reflecting the real-world complexities of financial networks. Solving this NP-hard problem using classical methods is challenging; therefore, we leverage quantum computing technology to efficiently explore the solution space and produce decision variables that indicate the likelihood of financial distress for each SME in the network. The difficulty of this problem lies in the potential domino effect, where a single delayed payment can trigger subsequent financial distress among connected entities, leading to a cascading collapse within the financial network. Classical computing solutions for such problems would take an impractical amount of time, estimated at 13.7 billion years. In contrast, using quantum computing, our optimization model solves the problem in approximately 7 seconds. Using financial data from 2019, our model successfully identified 90.04 million TL out of 107 million TL of non-performing loans. Similarly, using data from 2020, it identified 669.92 million TL out of 726 million TL of non-performing loans. Overall, the model accurately predicted 91.2.
In this study, we focus on estimating financial crashes within a network of small and medium enterprises (SMEs) that are customers of Yapi Kredi Bank. These SMEs have complex financial relationships involving receivables and payables among each other. We developed a quadratically constrained quadratic program as a mathematical optimization model aiming at identifying significant vulnerabilities and potential failure points in this network. By defining key parameters such as crossholding ratios, self-holding fractions, asset ownership proportions, and failure cost functions, we capture the intricate dependencies between these entities. The model is inherently nonlinear due to the quadratic terms and constraints, reflecting the real-world complexities of financial networks. Solving this NP-hard problem using classical methods is challenging; therefore, we leverage quantum computing technology to efficiently explore the solution space and produce decision variables that indicate the likelihood of financial distress for each SME in the network. The difficulty of this problem lies in the potential domino effect, where a single delayed payment can trigger subsequent financial distress among connected entities, leading to a cascading collapse within the financial network. Classical computing solutions for such problems would take an impractical amount of time, estimated at 13.7 billion years. In contrast, using quantum computing, our optimization model solves the problem in approximately 7 seconds. Using financial data from 2019, our model successfully identified 90.04 million TL out of 107 million TL of non-performing loans. Similarly, using data from 2020, it identified 669.92 million TL out of 726 million TL of non-performing loans. Overall, the model accurately predicted 91.2.
In highly connected financial networks, the failure of a single institution can cascade into additional bank failures. This systemic risk can be mitigated by adjusting the loans, holding shares, and other liabilities connecting institutions in a way that prevents cascading of failures. We are approaching the systemic risk problem by attempting to optimize the connections between the institutions. In order to provide a more realistic simulation environment, we have incorporated nonlinear/discontinuous losses in the value of the banks. To address scalability challenges, we have developed a two-stage algorithm where the networks are partitioned into modules of highly interconnected banks and then the modules are individually optimized. We developed a new algorithms for classical and quantum partitioning for directed and weighed graphs (first stage) and a new methodology for solving Mixed Integer Linear Programming problems with constraints for the systemic risk context (second stage). We compare classical and quantum algorithms for the partitioning problem. Experimental results demonstrate that our two-stage optimization with quantum partitioning is more resilient to financial shocks, delays the cascade failure phase transition, and reduces the total number of failures at convergence under systemic risks with reduced time complexity.
A key problem in financial mathematics is the forecasting of financial crashes: If we perturb asset prices, will financial institutions fail on a massive scale? This was recently shown to be a computationally intractable (NP-hard) problem. Financial crashes are inherently difficult to predict, even for a regulator which has complete information about the financial system. In this Rapid Communication, we show how this problem can be handled by quantum annealers. More specifically, we map the equilibrium condition of a toy-model financial network to the ground-state problem of a spin-1/2 quantum Hamiltonian with two-body interactions, i.e., a quadratic unconstrained binary optimization problem. The equilibrium market values of institutions after a sudden shock to the network can then be calculated via adiabatic quantum computation and, more generically, by quantum annealers. Our procedure could be implemented on near-term quantum processors, thus providing a potentially more efficient way to assess financial equilibrium and predict financial crashes.
The integration of artificial intelligence (AI) and quantum computing is poised to redefine the landscape of financial risk modeling and enterprise decision-making systems. This paper investigates the synergistic potential of these transformative technologies, emphasizing the development of hybrid AI-quantum algorithms to address the increasing complexity of modern financial systems. Traditional risk modeling methodologies often face significant limitations in capturing intricate market dynamics and accounting for real-time decision-making constraints. By leveraging quantum computing's unparalleled computational capabilities, particularly its ability to handle high-dimensional optimization problems, AI-powered quantum algorithms present a paradigm shift in financial risk prediction and mitigation. The research elaborates on key applications, including portfolio optimization, fraud detection, and credit risk analysis, demonstrating how quantum-enhanced AI algorithms achieve superior performance in terms of accuracy, efficiency, and scalability compared to classical approaches. The study begins by elucidating the theoretical underpinnings of hybrid AI-quantum systems, detailing their algorithmic structures and computational advantages. Quantum-inspired AI techniques, such as quantum neural networks and quantum-enhanced support vector machines, are examined for their ability to process vast datasets with unparalleled speed and precision. Portfolio optimization is analyzed as a case study, showcasing how quantum algorithms excel in minimizing risk while maximizing returns within a multidimensional constraint environment. Similarly, advanced fraud detection systems are explored, where hybrid models significantly improve anomaly detection rates by incorporating quantum-enhanced pattern recognition. The paper also delves into credit risk analysis, emphasizing how AI-quantum solutions can predict default probabilities with unprecedented accuracy, thereby supporting financial institutions in managing systemic risks more effectively. Despite these advancements, the integration of AI and quantum computing into financial ecosystems poses substantial challenges. The research discusses issues such as algorithmic scalability, error mitigation in quantum computations, and the resource-intensive nature of quantum hardware. Furthermore, the implementation of these technologies within the existing fintech landscape is fraught with obstacles, including interoperability with classical systems, regulatory compliance, and the high costs associated with quantum infrastructure. Addressing these challenges requires a multidisciplinary approach, combining expertise in quantum mechanics, AI, and financial engineering to develop robust, scalable solutions. The paper also examines the broader implications of AI-quantum integration for enterprise decision systems. By enabling real-time analysis of volatile markets, these technologies empower organizations to make informed, data-driven decisions, thereby enhancing operational resilience and competitive advantage. Furthermore, the ethical considerations and regulatory frameworks governing the deployment of such advanced systems are critically analyzed, emphasizing the need for transparency, fairness, and accountability in algorithmic decision-making. The findings presented in this study underscore the transformative potential of AI-powered risk modeling in quantum finance. By bridging the gap between theoretical advancements and practical implementations, this research contributes to the growing body of knowledge on hybrid AI-quantum systems and their applications in the financial domain. Ultimately, the integration of AI and quantum computing represents a pivotal development in enterprise decision systems, offering unprecedented opportunities to address the complexities of financial risk management in an increasingly interconnected and uncertain world.
The accelerating complexity of global supply chains, particularly among small and medium-sized enterprises (SMEs), presents challenges that exceed the capabilities of conventional computational models. Quantum computing, with its inherent ability to process vast solution spaces through superposition and entanglement, offers a transformative approach to supply chain optimization. This study evaluates the feasibility and potential impact of applying quantum and hybrid quantum–classical algorithms—such as the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing—to logistics scheduling, demand forecasting, and inventory management in SMEs. It critically analyzes algorithmic efficiency, scalability, and cost–benefit ratios within limited computational infrastructures. The research integrates insights from operations research, quantum algorithm design, and management science to identify viable entry points for SME adoption. Findings suggest that while quantum computing remains in the NISQ (Noisy Intermediate-Scale Quantum) stage, near-term applications through cloud-based hybrid platforms could significantly enhance decision accuracy, resource allocation, and resilience against supply chain disruptions.
Quantum computing has emerged as a revolutionary computational paradigm capable of solving complex optimization and probabilistic problems that are computationally expensive for classical computers. The financial industry, characterized by large-scale datasets, uncertainty, stochastic behavior, and multidimensional optimization challenges, is increasingly exploring quantum computing to enhance decision-making processes. This paper presents a comprehensive investigation into the applications of quantum computing in financial modeling, emphasizing portfolio optimization, derivative pricing, risk assessment, fraud detection, and algorithmic trading. Unlike conventional computational techniques that suffer from exponential complexity when processing high-dimensional financial data, quantum algorithms leverage superposition, entanglement, and quantum parallelism to improve computational efficiency and solution quality. The study discusses fundamental quantum computing concepts, analyzes existing financial modeling frameworks, and examines how quantum algorithms such as Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Quantum Monte Carlo (QMC), Grover’s Search Algorithm, and Quantum Machine Learning (QML) contribute to solving real-world financial problems. Furthermore, the paper reviews recent research developments, identifies implementation challenges associated with noisy intermediate-scale quantum (NISQ) devices, and highlights future opportunities for hybrid quantum-classical financial systems. The findings demonstrate that quantum computing has significant potential to transform computational finance by improving prediction accuracy, accelerating optimization tasks, and enabling scalable financial analytics for next-generation intelligent financial ecosystems.
In modern financial information systems, the growing complexity of credit risk assessment for small and medium-sized enterprises (SMEs) creates increasing demands for highly accurate, scalable, and computationally efficient predictive models.Traditional credit scoring approaches based on classical statistical methods and conventional neural networks often demonstrate limited performance when processing nonlinear financial dependencies, noisy datasets, and highdimensional feature spaces.These limitations become especially significant under restricted training conditions, where the amount of labeled financial data is limited and rapid model convergence is required for practical deployment in banking and risk management environments.In such conditions, hybrid quantum-classical machine learning has emerged as a promising direction that combines the representational capabilities of classical neural networks with the computational potential of quantum variational circuits.This paper proposes a hybrid quantum-classical neural network architecture designed for SME credit risk assessment under limited training conditions.The proposed model integrates classical dense layers with a variational quantum circuit that performs nonlinear feature transformation through quantum embedding, parameterized quantum gates, and entanglement operations.Particular attention is devoted to efficient data encoding and optimization of variational circuit depth in order to reduce training complexity while preserving predictive stability.