This article proposes a quantum inspired Support Vector Machine (QISVM) framework that employs simulated quantum circuit features to address these challenges. To encapsulate such features, we use a four-qubit encoding protocol using X-axis rotation and entanglement created from Controlled-NOT (CNOT). Quantum inspired elements from simulated circuit output include entropy and probability of the most likely measurement state. These features, including time-based indicators and quantum inspired representations, are scaled using MinMax normalization to ensure consistent feature ranges and numerical stability during SVM training. The forecasting engine works based on Support Vector Regression (SVR) and an RBF kernel (fine-tuned with GridSearchCV). Moreover, we propose a dual learning architecture implementing SVR and SVC for regression-based forecasting and movement direction judgment. Quantum circuit simulations on batch level done using Qiskit AerSimulator and joblib-based caching which are scalable and reproducible but without quantum hardware. To prove this approach, we back-test it using Apple stock price financial time series datasets against both Auto Regressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) networks and Quantum LSTM (QLSTM) models. QISVM has superior forecasting performance as well as directional forecast capability with less training time and computational resources than hybrid, deep learning and others. Herein, the proposed framework provides feature-level diagnostic interpretability through its explicitly defined quantum-inspired features and permutation-based feature analysis.
This article proposes a quantum inspired Support Vector Machine (QISVM) framework that employs simulated quantum circuit features to address these challenges. To encapsulate such features, we use a four-qubit encoding protocol using X-axis rotation and entanglement created from Controlled-NOT (CNOT). Quantum inspired elements from simulated circuit output include entropy and probability of the most likely measurement state. These features, including time-based indicators and quantum inspired representations, are scaled using MinMax normalization to ensure consistent feature ranges and numerical stability during SVM training. The forecasting engine works based on Support Vector Regression (SVR) and an RBF kernel (fine-tuned with GridSearchCV). Moreover, we propose a dual learning architecture implementing SVR and SVC for regression-based forecasting and movement direction judgment. Quantum circuit simulations on batch level done using Qiskit AerSimulator and joblib-based caching which are scalable and reproducible but without quantum hardware. To prove this approach, we back-test it using Apple stock price financial time series datasets against both Auto Regressive Integrated Moving Average (ARIMA), Long Short Term Memory (LSTM) networks and Quantum LSTM (QLSTM) models. QISVM has superior forecasting performance as well as directional forecast capability with less training time and computational resources than hybrid, deep learning and others. Herein, the proposed framework provides feature-level diagnostic interpretability through its explicitly defined quantum-inspired features and permutation-based feature analysis.
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.
High-Frequency Financial Forecasting is important in algorithmic trading as it helps make decisions based on high-frequency data in milliseconds. This challenge has been found to be unsolvable using conventional machine learning approaches, such as classical Support Vector Machines (SVMs), Random Forest (RF), and deep learning models, which were not able to capture the nonlinear and high-dimensional patterns embedded in the high-frequency datasets, leading to poor prediction performance. This paper proposes a Supervised Quantum Support Vector Machine (QSVM) model, which is tailored for the high-frequency financial forecasting task. The QSVM can effectively encode the market information and enhance the predictability while maintaining the computational efficiency through quantum feature mapping and high-dimensional Hilbert space. This process includes encoding of tick-level market microstructure data, stock and futures prices, and technical indicators into quantum states and subsequent training using a quantum-classical hybrid optimization loop. Classical SVM, Random Forest, LSTM, and hybrid QML are used as the benchmarks for the proposed QSVM using forecasting accuracy, RMSE, MAPE, computational latency, and Sharpe ratio. The results obtained from the experiment show that the QSVM has better accuracy (91.2%), RMSE (0.018), and latency (22 ms) as well. Hence, the QSVM shows better performance when compared to other methods and is capable of modeling the markets that have a fast change in dynamics. High-dimensional quantum kernel plays a part in enhancing the ability to separate non-linearities. Therefore, the QSVM could be a potential solution to apply in algorithmic trading. The limitations are hardware constraints of NISQ and restrictions on the size of the dataset. Future directions of research will include the scalability of the model and the use of ensemble approaches.
Financial time-series analysis poses some difficulties, due to the non-stationarity of data, its distribution is heavy-tailed, the regime structure in financial time-series is latent, and there are few labelled training signals available.These models are highly sensitive to unexpected trade-offs and limited out-of-distribution generalisation, and unsupervised models, on their own, are unable to directly inform tradeable decisions.This paper proposes a framework named Quantum-Inspired Hybrid Supervised-Unsupervised Learning (QIHSL) consisting of three tightly-integrated modules: (1) Quantum-Inspired Feature Transformation (QFT) module that maps the financial features into amplitudeand phase-encoded qubit-state analogues, and generates the cross-feature interaction terms inspired by entanglement;(2) Quantum-Inspired K-Means (QI-KMeans) clustering module that uses the quantum fidelity based distance metric to learn the regime (bull, bear, sideways) of the data in an unsupervised manner; and (3) a Quantum-Inspired Support Vector Machine (QI-SVM) regime-gated ensemble for robust and regime-aware return-direction prediction with QI-SVMs that specialise in predicting the return direction in different market regimes.The experiments on a comprehensive benchmark dataset of 4,950 synthetic daily observations from the S&P 500 index, used to capture the statistical properties of equity markets, namely Student-t distributed returns (ν = 5), three-state regime switching and nineteen technical indicators, show that QIHSL achieves an accuracy of 0.4913, a precision of 0.5538, a recall of 0.2588, an F1-score of 0.3527 and an AUC of 0.5029 on the hold-out test set, outperforming the baseline SVM by 5.92 percentage points in precision.It represents the best of all six comparison methods (Table 2) and for all the architectures in the matched-condition comparison (Table 7) is an advantage (not a finding) of precision for two instances of the simulation and does not necessarily mean that they are more likely to forecast than others.The controlled ablation study (Table 3) also shows that a component-level benefit for quantum-inspired feature enrichment can be measured by the QFT module's accuracy of +2.0 pp and F1-score of +11.9 pp over the SVM baseline (A2 vs. A1).Five-fold stratified cross validation gives a stability of AUC = 0.4987 ± 0.0095.An ablation study shows that the QFT module contributes the largest single-step accuracy gain (+2.0 pp) and F1-score gain (+11.9 pp over A1); adding regime gating further improves accuracy (+0.95 pp) but reduces F1 (-0.0786) and AUC (-0.0123) relative to QFT alone.The reported AUC (around 0.5) is at chance level, and no pairwise McNemar comparison is significant at alpha = 0.05 (the smallest p = 0.2433), which is consistent with the almost efficient nature of the synthetic benchmark.Combined, results show no statistically relevant predictive value for the synthetic benchmark.The most interesting aspect of QIHSL is its highest precision (0.5538) of all methods compared, which could have some relevance to conservative trading, but not to proven practical forecast effectiveness.
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.
Prediction of stock price remains a challenging task because financial markets are dynamic and often non-linear, unpredictable patterns. To deal with such unpredictable data, the architecture that’s created must identify both time based trends and hidden patterns in data. In this study, we explore a hybrid modelling approach by combining both the classical deep learning techniques with quantum computing principles. Specifically, a Long Short-Term Memory (LSTM) is combined with a Quantum Neural Network (QNN) to understand the benefits of using quantum in time series forecasting. Rather than depending on a single quantum configuration, multiple feature maps and ansatz circuit designs were evaluated to identify the most suitable arrangement for the QLSTM model. Resultant framework blends ideas from both classical computing and quantum approaches to improve how we predict patterns over time in real-life scenarios.
In this paper, we present a reproducible benchmarking framework that systematically compares QML models with architecture-matched classical counterparts across three financial tasks: (i) directional return prediction on U.S. and Turkish equities, (ii) live-trading simulation with Quantum LSTMs versus classical LSTMs on the S\&P 500, and (iii) realized volatility forecasting using Quantum Support Vector Regression. By standardizing data splits, features, and evaluation metrics, our study provides a fair assessment of when current-generation QML models can match or exceed classical methods. Our results reveal that quantum approaches show performance gains when data structure and circuit design are well aligned. In directional classification, hybrid quantum neural networks surpass the parameter-matched ANN by \textbf{+3.8 AUC} and \textbf{+3.4 accuracy points} on \texttt{AAPL} stock and by \textbf{+4.9 AUC} and \textbf{+3.6 accuracy points} on Turkish stock \texttt{KCHOL}. In live trading, the QLSTM achieves higher risk-adjusted returns in \textbf{two of four} S\&P~500 regimes. For volatility forecasting, an angle-encoded QSVR attains the \textbf{lowest QLIKE} on \texttt{KCHOL} and remains within $\sim$0.02-0.04 QLIKE of the best classical kernels on \texttt{S\&P~500} and \texttt{AAPL}. Our benchmarking framework clearly identifies the scenarios where current QML architectures offer tangible improvements and where established classical methods continue to dominate.