Quantum Walk

Quantum walks are quantum analogs of classical random walks, offering potential advantages in computation by leveraging quantum interference and superposition. Current research focuses on developing quantum walk algorithms for diverse applications, including disease gene prioritization, optimization problems (e.g., using Metropolis-Hastings methods and addressing non-convex landscapes), and machine learning tasks like graph classification and reinforcement learning. These algorithms often utilize continuous-time quantum walks or variations thereof, sometimes incorporating stochastic gradients for efficiency. The potential impact lies in achieving speedups over classical methods for computationally challenging problems across various scientific disciplines and technological domains.