Impacting Financial Predictions & Security through Quantum Support Vector Machines, Quantum Approximate Optimization, and Quantum-Resistant Lattice Cryptography

The need for improved cybersecurity and predictive capabilities, particularly with the advent of quantum computing, has become highly critical in these financial systems. Indeed, classical cryptography and machine learning models are at their wit's end when confronted with tasks such as high-dimensional financial data and complex optimization, let alone quantum threats. In contrast, traditional methods involving Support Vector Machines, classical portfolio optimization, and conventional cryptographic systems lack computational efficiency and security in view of the ever-growing complexity and risks of financial scenarios. The paper now proposes an advanced approach of integrating QML techniques with Quantum-Resistant Cryptography in financial cybersecurity and predictive modeling. The key approaches are Quantum Support Vector Machines QSVM, for financial predictive modeling that increases classification accuracy by as much as 20% and improves computational efficiency by up to 50% due to quantum parallelism. QAOA is applied to portfolio optimization for its significant improvement of the Sharpe ratio by about 10-15% and 25% fast optimization time compared to classical methods. QPCA is then applied to reduce the dimensions of the data while preserving 95% of the variance in the data and decreasing the data dimensionality by 90%, hence improving efficiency in financial data analysis. Financial time series prediction will be done using QBM, which can yield an increase of up to 5-10% in the accuracy of the prediction and 30% faster convergence. Furthermore, the embedding of Quantum-Resistant Lattice-Based Cryptography brings financial transactions to a secure spot in the post-quantum era, hence 99.9% of security against quantum and classical attacks, at marginal computational overhead increase of about 10-15%. The proposed quantum-enhanced framework significantly outperforms its classical baselines by a large margin on predictive accuracy, optimization speed, and transaction security. This work, probably for the first time, will represent that with quantum technologies, we are at the threshold of a revolution in cybersecurity and financial modeling for much better and more secure financial systems.