The article describes how Discrete Time Quantum Walks (DTQWs) provide a useful means for recreating-quantum physical behaviour on graph structures, focusing on the effects of ballistic propagating effects and destructive interference, Methods were developed to simulate DTQWs using classical methods to visualise the changes in the probability distribution of the walker over time, specifically focusing on the Distal Path of the walker. A hybrid quantum machine learning model has been developed which combines the quantum features of quantum walk dynamics with classic technical indicators such as Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD), to enable the classification of financial trends. The classification model was trained on a synthetically balanced dataset created using Synthetic Minority Over-sampling Technique SMOTE and performance was assessed using standard metrics such as accuracy, precision and recall. The simulation results were evaluated and visualised in terms of their probability distributions. A successful model has been found which produces similar forecasting results compared with those achieved by normal benchmark methods for stock movement and portfolio construction.
The article describes how Discrete Time Quantum Walks (DTQWs) provide a useful means for recreating-quantum physical behaviour on graph structures, focusing on the effects of ballistic propagating effects and destructive interference, Methods were developed to simulate DTQWs using classical methods to visualise the changes in the probability distribution of the walker over time, specifically focusing on the Distal Path of the walker. A hybrid quantum machine learning model has been developed which combines the quantum features of quantum walk dynamics with classic technical indicators such as Relative Strength Index (RSI) and Moving Average Convergence Divergence (MACD), to enable the classification of financial trends. The classification model was trained on a synthetically balanced dataset created using Synthetic Minority Over-sampling Technique SMOTE and performance was assessed using standard metrics such as accuracy, precision and recall. The simulation results were evaluated and visualised in terms of their probability distributions. A successful model has been found which produces similar forecasting results compared with those achieved by normal benchmark methods for stock movement and portfolio construction.
Traditional machine learning shares several benefits with quantum information processing field. The study of machine learning with quantum mechanics is called quantum machine learning. Data clustering is an important tool for machine learning where quantum computing plays a vital role in its inherent speed up capability. In this paper, a hybrid quantum algorithm for data clustering (quantum walk-based hybrid clustering (QWBHC)) is introduced where one-dimensional discrete time quantum walks (DTQW) play the central role to update the positions of data points according to their probability distributions. A quantum oracle is also designed and it is mainly implemented on a finite [Formula: see text]-regular bipartite graph where data points are initially distributed as a predefined set of clusters. An overview of a quantum walk (QW) based clustering algorithm on 1D lattice structure is also introduced and described in this paper. In order to search the nearest neighbors, a unitary and reversible DTQW gives a quadratic speed up over the traditional classical random walk. This paper also demonstrates the comparisons of our proposed hybrid quantum clustering algorithm with some state-of-the-art clustering algorithms in terms of clustering accuracy and time complexity analysis. The proposed quantum oracle needs [Formula: see text] queries to mark the nearest data points among clusters and modify the existing clusters. Finally, the proposed QWBHC algorithm achieves [Formula: see text] performance.
Quantum finance, as a key application scenario of quantum computing, showcases multiple significant advantages of quantum machine learning over traditional machine learning methods. This paper first aims to overcome the limitations of the hidden quantum Markov model (HQMM) in handling continuous data and proposes an innovative method to convert continuous data into discrete-time sequence data. Second, a hybrid quantum computing model is developed to forecast stock market trends. The model was used to predict 15 stock indices from the Shanghai and Shenzhen Stock Exchanges between June 2018 and June 2021. Experimental results demonstrate that the proposed quantum model outperforms classical algorithmic models in handling higher complexity, achieving improved efficiency, reduced computation time, and superior predictive performance. This validation of quantum advantage in financial forecasting enables the practical deployment of quantum-inspired prediction models by investors and institutions in trading environments. This quantum-enhanced model empowers investors to predict market regimes (bullish/bearish/range-bound) using real-time data, enabling dynamic portfolio adjustments, optimized risk controls, and data-driven allocation shifts.
Financial markets, particularly equity indices like the SP 500, exhibit complex, nonlinear, and frequently chaotic behaviors driven by a combination of macroeconomic, geopolitical, and psychological factors. Traditional machine learning techniques, despite their notable success in modeling market trends, often encounter performance limitations when dealing with high-dimensional feature spaces and intricate historical dependencies. Quantum computing offers a promising approach for addressing these challenges through its ability to represent and process information in superposition states. This study proposes and implements a quantum neural network (QNN) architecture that utilizes angle encoding to embed carefully engineered features, including volatility measures, momentum indicators, and lagged price signals, into quantum states. These encoded features are processed through a customized quantum circuit comprising three variational layers that combine linear entanglement patterns with single-qubit rotation operations. The training process is conducted via gradient-based optimization through the PennyLane framework, operating within a noiseless simulation environment to establish theoretical performance bounds. The experimental results demonstrate that the QNN achieves an accuracy of 81.3% on the held-out test set in simulation, outperforming traditional baseline models including Long Short-Term Memory networks, Random Forest classifiers, and Support Vector Machines under identical conditions. The model exhibits balanced precision (0.83) and recall (0.79). While the experiments were conducted in simulation due to current quantum hardware limitations, the findings illustrate the potential of quantum-enhanced algorithms for financial forecasting applications. Keywords Quantum Neural Networks, Financial Forecasting, SP 500, Variational Quantum Circuits, Time Series Prediction.
Abstract In this study, the potential of quantum machine learning (QML) techniques based on trainable quantum circuits was explored for vector boson identification at the large hadron collider (LHC). Specifically, the compact muon solenoid (CMS) experiment dataset was employed to reconstruct the Z boson through the muon–antimuon ( μ+μ− μ + μ - ) decay channel using variational quantum circuits (VQC). To examine the effect of data structure on QML performance, various preprocessing strategies were applied, including different train/test splits, feature selection, dimensionality reduction, and class balancing techniques. The dataset was evaluated under two train/test configurations, namely a balanced split (70:30) and an imbalanced split (80:20), in order to examine the effect of class distribution on QML outcomes. Feature selection based on Random Forest (RF) was used to extract the most informative variables, while principal component analysis (PCA) was utilized to reduce input dimensionality and optimize qubit usage. To mitigate class imbalance, resampling techniques such as the Synthetic Minority Over-sampling Technique (SMOTE), SMOTE combined with edited nearest neighbors (SMOTEENN), and SMOTE with Tomek Links (SMOTETomek) were implemented. A comparative evaluation using stratified cross-validation was conducted to assess model performance and generalization ability across different configurations. The findings indicated that integrating PCA with resampling methods substantially improves the generalization capacity of the VQC model, especially in imbalanced settings. Among all configurations, SMOTE and SMOTEENN delivered the highest classification performance, boosting sensitivity to the minority class and enhancing model stability. These results highlight the significance of data structure, feature reduction, and resampling in classical quantum (CQ) data processing for high energy physics applications.
Accurate modeling of the temporal evolution of asset prices is crucial for understanding financial markets. We explore the potential of discrete-time quantum walks to model the evolution of asset prices. Return distributions obtained from a model based on the quantum walk algorithm are compared with those obtained from classical methodologies. We focus on specific limitations of the classical models, and illustrate that the quantum walk model possesses great flexibility in overcoming these. This includes the potential to generate asymmetric return distributions with complex market tendencies and higher probabilities for extreme events than in some of the classical models. Furthermore, the temporal evolution in the quantum walk possesses the potential to provide asset price dynamics.
Financial time series forecasting remains one of the most challenging tasks due to its noisy, non-stationary, and stochastic nature. The paper represents the empirical evaluation of a multi-input Convolutional Neural Network regarding stock market trend classification. The novelty of our approach is based on feature engineering: transforming one-dimensional technical indicators (namely, 20-day simple moving average and Bollinger Bands) into two-dimensional image representations by means of Quantum Gramian Angular Field encoding technique. This is developed by leveraging a simulated quantum circuit mapping time series into the feature space designed to preserve the temporal dynamics. The produced QGAF images are fed into CNN in parallel, where the model is trained to classify whether the stock will go up or down the following day. The performance evaluation is conducted on a historical dataset of US major equities. On one hand, empirical results manifest enormous overfitting and provide modest test accuracy of54.2%. At the same time, a deep analysis provided including precision and recall metrics testifies about strong bias of the model. In this way, the study confirms the QGAF encoding pipeline as suitable for feature engineering technique and creates an important empirical baseline for the future. The main contribution of the paper is given by the critical, in-depth evaluation of this hybrid quantum-classical, multi-input architecture, outlining the enormous challenges still to be overcome when applying such techniques to financial data.