Financial datasets possess high dimensionality and non-linear structure that challenge classical models in providing necessary forecastings. This work propose a hybrid Quantum-Enhanced Support Vector Machine (QSVM) that encodes technical indicators via a fixed ZZFeatureMap and computes a quantum kernel utilising a trainable Real Amplitude circuit and a classical SVM. The study adopts rigorous statistical validation with 10 independent runs, stratified with 5-fold CV, mean with 95% confidence intervals and uses McNemar’s test for paired significance. Realistic resource analysis and optional NISQ noise settings are included with qubits, circuit depth, two-qubit gates and shots. On a combined financial dataset, including S&P 500 stocks, cryptocurrencies, forex pairs and FRED economic indicators, QSVM achieves modest but statistically significant gains over tuned classical baselines, with advantages widening at high dimensionality. Results clarify simulation overheads and expected noise-induced degradation, supporting a cautious and reproducible assessment of QSVM for financial forecasting.
Financial datasets possess high dimensionality and non-linear structure that challenge classical models in providing necessary forecastings. This work propose a hybrid Quantum-Enhanced Support Vector Machine (QSVM) that encodes technical indicators via a fixed ZZFeatureMap and computes a quantum kernel utilising a trainable Real Amplitude circuit and a classical SVM. The study adopts rigorous statistical validation with 10 independent runs, stratified with 5-fold CV, mean with 95% confidence intervals and uses McNemar’s test for paired significance. Realistic resource analysis and optional NISQ noise settings are included with qubits, circuit depth, two-qubit gates and shots. On a combined financial dataset, including S&P 500 stocks, cryptocurrencies, forex pairs and FRED economic indicators, QSVM achieves modest but statistically significant gains over tuned classical baselines, with advantages widening at high dimensionality. Results clarify simulation overheads and expected noise-induced degradation, supporting a cautious and reproducible assessment of QSVM for financial forecasting.
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.
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 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.
Predicting the stock market has been an uphill battle with the non-linear, intricate, and highly unpredictable nature of the market. Classic machine learning and deep learning techniques have performed but lack scalability, computational cost, and nuance in capturing market patterns. Quantum computing (QC) has been a game-changing instrument for dealing with massive financial data and tuning predictive models for the past several years. Here, we are demonstrating the application of quantum machine learning (QML) methods, including quantum support vector machines (QSVM), variational quantum circuits (VQC), and quantum Boltzmann machines (QBM), to stock market prediction. Employing superposition, entanglement, and parallelism in quantum computation, we will improve prediction precision, improve feature optimisation, and strengthen trend analysis of the market. Our approach utilises quantum-enhanced financial modelling to handle large volumes of historical stock data and sentiment analysis within a high-dimensional Hilbert space. The findings show that quantum models surpass classical models in capturing market trends, computation time reduction, and managing intricate correlations. Although quantum hardware is in its nascent stage, this research proves the promise of quantum computing as a disruptor in financial forecasting.
Abstract Support vector machines (SVMs) are widely used for classification, but become computationally expensive as datasets grow and data become high-dimensional or nonlinearly separable. Quantum support vector machines (QSVMs) enhance the traditional SVMs by replacing conventional feature mappings with quantum-implemented embeddings that project data into high-dimensional Hilbert spaces, potentially improving expressive power and efficiency. However, training QSVMs on current noisy intermediate-scale quantum (NISQ) devices remains challenging, as constructing the quantum kernel matrix requires repeated circuit executions and scales quadratically with dataset size. We propose NQ-SVM , a hybrid quantum-classical method that incorporates the Nystr\"om approximation to enhance the scalability of QSVMs. By selecting a small set of landmark points, the Nystr\"om method yields a low-rank kernel approximation, reducing complexity from (O(n2)) to (O(nm)), while preserving the quantum advantages in handling high-dimensional, non-linear data. Extensive simulations demonstrate that NQ-SVM achieves comparable classification accuracy to classical SVMs and full QSVMs, while substantially reducing training time and kernel evaluation costs. These results underscore the potential of NQ-SVM as a scalable and practical hybrid framework for near-term quantum machine learning (QML).
Abstract Quantum computing has demonstrated a potential advantage in advancing artificial intelligence and machine learning applications. Both disciplines leverage higher-dimensional computation that relies on complex linear algebra operations. Quantum Support Vector Machines (QSVMs) uses quantum computation principles to enhance classification performance over traditional SVMs. This paper studies QSVMs applied to the Iris and Wine datasets by exploring various quantum encoding methods, including amplitude encoding, ZZFeatureMap, and PauliFeatureMap, within IBM’s Qiskit framework. These encoding methods enable QSVMs to utilize quantum phenomena like superposition and entanglement to transform classical data into quantum states to increase computational efficiency and accuracy in classification tasks. Numerical results indicate that QSVMs with amplitude encoding with combination of ZZFeatureMap achieve higher classification accuracy than traditional techniques. Also, this study assess the current performance of Noisy Intermediate-Scale Quantum (NISQ) devices to highlight the need for further optimization and advancements in quantum data encoding approches. These findings provide important insights into the application of QSVMs and their potential to address a range of classification problems effectively.