A Cirq-Enhanced Quantum Reinforcement Learning Model for High-Frequency Trading Optimization

The main goal of the proposed Quantum Reinforcement Learning (QRL) model that operates through Cirq framework serves to deliver competitive results in highfrequency trading environments in dynamic financial markets. Quantum parallel computing techniques provide to classical reinforcement learning methods a solution for their computational complexity and adaptability problems. The designed quantum-classical trading algorithm shows better market decisions along with quicker market reactions especially during periods of market volatility. The study investigates quantum-enhanced trading implementation methods and establishes three major research goals that include quantum scalability approaches, protecting quantum performance and building hybrid systems integrated with quantum and classical components.