Noisy Intermediate Scale Quantum Device

Noisy Intermediate-Scale Quantum (NISQ) devices, with their limited qubit count and susceptibility to noise, are driving research into hybrid classical-quantum algorithms. Current efforts focus on developing and improving variational quantum algorithms (VQAs), including those employing techniques like the parameter-shift rule and simultaneous perturbation stochastic approximation (SPSA), for applications such as solving linear systems of equations, machine learning tasks (including quantum reinforcement learning and natural language processing), and mitigating the "barren plateaus" problem that hinders optimization. These advancements aim to unlock the potential of NISQ devices for practical applications while simultaneously furthering our understanding of quantum computation and its limitations.