Quantum Enabled-Algorithms and Market Prediction and Execution for Ultra-Low-Latency High-Frequency Trading in Global Markets

The ultra-low-latency decision-making process and sophisticated predictive technology are the key to staying competitive in the rapidly evolving global markets with the continuous fading of timeframes in affected markets. Classical algorithms are efficient but have limitations in scaling, speed, and non-linear market applications. This study introduces a quantum inspired model that integrates an encoding of quantum data, quantum approximate optimization, and quantum neural networks to generate real-time trading strategies. With quantum parallelism, the system processes volumes of financial data quickly and optimizes portfolio allocations to enhance predictive accuracy during times of volatility. A hybrid quantum-classical execution pipeline reduces latency during the linkage of algorithmic computations to global trading infrastructures. The framework offers more adaptability, higher risk-adjusted returns, and increased throughput compared to current statistical, machine learning, and quantum-inspired models. Experimental results demonstrate significant performance gains, with execution latency of 5.3 ms, prediction accuracy of 94.6 %, F1‑score of 93.8 %, portfolio efficiency of 0.91, throughput of 3,200 trades per second, and a Sharpe ratio of 1.56.