Explore dissipative quantum neural networks in this 31-minute lecture by Kerstin Beer from Macquarie University. Delve into the design of quantum neural networks for fully quantum learning tasks, a crucial challenge in the era of quantum technology. Learn how these networks function in a feed-forward manner, representing a true quantum equivalent to classical neural networks and capable of universal quantum computation. Discover the training process using fidelity as a cost function and examine the benchmarking for learning unknown unitary operations. Gain insights into the potential of quantum machine learning and its applications in near-term quantum computers, including fault tolerance, benchmarking, quantum advantage, and quantum algorithms.