Reservoir computing is a machine learning paradigm that exploits dynamical systems to solve temporal tasks. This technique finds applications in fields as diverse as weather forecasting,  stock market prediction, and communications. Similar to other unconventional computing paradigms inspired by the capabilities of the human brain, reservoir computing is concerned with hardware implementations that aim to overcome the challenges of digital computation. In this context, quantum reservoir computing is the case where quantum dynamical systems are used as reservoirs. In this talk, I will introduce the field and give a brief overview of state-of-the-art experimental implementations.