Abstract Quantum state preparation is a vital process in quantum computing and quantum information processing. The ability to accurately and reliably prepare specific quantum states is essential for a variety of applications. One of the promising applications of quantum computers is quantum simulation. This requires preparing a quantum state that represents the system we’re trying to simulate. This research introduces a novel simulation algorithm, the multi-Split-Steps Quantum Walk (multi-SSQW), designed to learn and load complicated probability distributions using parameterized quantum circuits (PQC) with a variational solver on classical simulators. The multi-SSQW algorithm is a modified version of the split-steps quantum walk, enhanced to incorporate a multi-agent decision-making process, rendering it suitable for modeling financial markets. The study provides theoretical descriptions and empirical investigations of the multi-SSQW algorithm to demonstrate its promising capabilities in probability distribution simulation and financial market modeling. Taking advantage of quantum computation, the multi-SSQW model's complex financial distributions and scenarios with high accuracy, providing valuable insights and mechanisms for financial analysis and decision-making. The multi-SSQW’s key benefits include its modeling flexibility, stable convergence, and instantaneous computation. These advantages underscore its rapid modeling and prediction potential in dynamic financial markets.
Abstract Quantum state preparation is a vital process in quantum computing and quantum information processing. The ability to accurately and reliably prepare specific quantum states is essential for a variety of applications. One of the promising applications of quantum computers is quantum simulation. This requires preparing a quantum state that represents the system we’re trying to simulate. This research introduces a novel simulation algorithm, the multi-Split-Steps Quantum Walk (multi-SSQW), designed to learn and load complicated probability distributions using parameterized quantum circuits (PQC) with a variational solver on classical simulators. The multi-SSQW algorithm is a modified version of the split-steps quantum walk, enhanced to incorporate a multi-agent decision-making process, rendering it suitable for modeling financial markets. The study provides theoretical descriptions and empirical investigations of the multi-SSQW algorithm to demonstrate its promising capabilities in probability distribution simulation and financial market modeling. Taking advantage of quantum computation, the multi-SSQW model's complex financial distributions and scenarios with high accuracy, providing valuable insights and mechanisms for financial analysis and decision-making. The multi-SSQW’s key benefits include its modeling flexibility, stable convergence, and instantaneous computation. These advantages underscore its rapid modeling and prediction potential in dynamic financial markets.
In recent years, there has been an emerging trend of combining two innovations in computer science and physics to achieve better computation capability. Exploring the potential of quantum computation to achieve highly efficient performance in various tasks is a vital development in engineering and a valuable question in sciences, as it has a significant potential to provide exponential speedups for technologically complex problems that are specifically advantageous to quantum computers. However, one key issue in unleashing this potential is constructing an efficient approach to load classical data into quantum states that can be executed by quantum computers or quantum simulators on classical hardware. Therefore, the split-step quantum walks (SSQW) algorithm was proposed to address this limitation. We facilitate SSQW to design parameterized quantum circuits (PQC) that can generate probability distributions and optimize the parameters to achieve the desired distribution using a variational solver. A practical example of implementing SSQW using Qiskit has been released as open-source software. Showing its potential as a promising method for generating desired probability amplitude distributions highlights the potential application of SSQW in option pricing through quantum simulation.
Abstract We present a novel Adaptive Distribution Generator (ADG) that leverages a quantum walks–based approach to generate high precision and efficiency of target probability distributions. Our method integrates variational quantum circuits with discrete-time quantum walks(DTQWs)—specifically, split-step quantum walks(SSQWs) and their entangled extensions—to dynamically tune coin parameters and drive the evolution of quantum states toward desired distributions. This enables accurate one-dimensional probability modeling for applications such as financial simulation and the generation of structured two-dimensional patterns, exemplified by digit representations (0–9). Implemented within the CUDA-Q framework, our approach exploits GPU acceleration to significantly reduce computational overhead and improve scalability relative to conventional methods. Extensive benchmarks demonstrate that our Quantum Walk–Based Adaptive Distribution Generator (QWs-based ADG) achieves high simulation fidelity and bridges the gap between theoretical quantum algorithms and practical high-performance computation.
The growing complexity, interdependencies, and rapid fluctuations inherent in modern financial markets create substantial challenges for accurate forecasting, portfolio optimization, and risk management. Conventional machine learning techniques, while powerful, often face limitations in capturing nonlinear relationships and processing high-dimensional datasets efficiently. Quantum machine learning (QML) has emerged as a promising paradigm that leverages quantum computing principles to enhance predictive modeling in finance. This study presents a comprehensive investigation into the application of QML methods—including variational quantum circuits, quantum kernel estimation, and quantum-enhanced support vector machines—for financial time-series prediction and asset price classification. We propose a hybrid quantum-classical framework that integrates quantum feature mapping with classical optimizers to improve model expressiveness and convergence. Empirical experiments are conducted using historical stock market data and synthetic datasets to benchmark QML approaches against established classical models such as long short-term memory networks and gradient boosting machines. The results demonstrate that QML techniques can achieve superior prediction accuracy and lower computational latency under certain data regimes, particularly when dealing with small-to-medium-sized datasets and high feature correlations. Additionally, the study examines scalability considerations, hardware constraints of near-term quantum devices, and the interpretability of quantum model outputs in financial decision-making contexts. The findings underscore the transformative potential of quantum machine learning as an innovative tool for advancing predictive analytics in finance and provide practical insights into how financial institutions can begin integrating QML capabilities into their workflows.
The continuous growth of financial markets dictates the need for its participants to seek new approaches to financial analysis to gain competitive advantages, including through the use of new approaches in the field of computing. Quantum computing can be used as a tool for obtaining these advantages over competitors. In particular, Monte Carlo modeling, although widely used in financial risk management, requires significant computing resources due to the large number of scenarios required to obtain an accurate result. To optimize this approach, quantum amplitude estimation algorithms are used, which accelerate this process if pre-calculated probability distributions are used to initialize input quantum states. However, in the absence of these distributions in existing approaches to this topic, they are generated numerically using classical computing, which completely negates the advantage of the quantum approach. This article proposes a solution to this problem by using quantum computing, including for the generation of probability distributions. The article discusses the creation of quantum circuits for modeling the evolution of risk factors over time for capital flows, interest rates, and credit risks, and presents the combination of these models with quantum amplitude estimation algorithms as an example of using the obtained algorithms for credit risk management. In conclusion, the article analyzes the possibility of using the obtained circuits in financial analysis.
The integration of quantum computing into financial systems marks a transformative leap in risk management, security, and market operations. Unlike classical computing, quantum algorithms can process vast datasets and complex models at unprecedented speeds, enabling real-time risk analytics, rapid fraud detection, and optimized trading strategies. This study explores the emerging field of quantum-driven finance, focusing on its potential to enhance predictive accuracy, reduce systemic vulnerabilities, and accelerate decision-making in volatile markets. Applications such as quantum-enhanced Monte Carlo simulations, quantum machine learning for anomaly detection, and optimization in algorithmic trading are examined alongside challenges in scalability, cybersecurity, and regulatory adaptation. By evaluating current advancements and future prospects, this research highlights quantum computing's role in shaping a secure, intelligent, and high-velocity global financial ecosystem.
Quantum computing is emerging as a transformative technology in financial portfolio optimization, offering the potential to process complex calculations at unprecedented speeds. This study explores the impact of quantum computing on financial decision-making through a case-based qualitative approach, analyzing real-world applications in leading financial institutions such as JPMorgan Chase, Goldman Sachs, and BBVA. The research highlights how quantum algorithms, including Quantum Approximate Optimization Algorithm (QAOA), Quantum Monte Carlo (QMC), and quantum annealing, are being utilized for risk management, asset pricing, and credit risk assessment. Findings indicate that while quantum computing enhances computational efficiency, scenario analysis, and optimization models, its widespread adoption is hindered by hardware limitations, high error rates, and regulatory uncertainties. The study emphasizes the growing relevance of hybrid quantum-classical models as a practical solution for near-term applications. Future advancements in quantum hardware, algorithm development, and financial cryptography are expected to drive broader adoption in financial markets. This research contributes to the ongoing discourse on quantum finance by providing empirical insights into the challenges and opportunities associated with quantum computing in portfolio optimization. The study concludes that while quantum computing is not yet fully commercialized, its long-term implications for financial risk assessment, high-frequency trading, and decision-making are profound.