Do Quantum Computers Have Applications in Machine Learning and Combinatorial Optimization

Explore the potential applications of quantum computers in machine learning and combinatorial optimization through this comprehensive lecture by Jens Eisert from Freie Universität Berlin. Delve into recent developments in quantum computing, including experimental implementations of random circuit sampling and quantum simulators. Examine the comparative power of classical and quantum learners for generative modeling within the probably approximately correct framework. Investigate the PAC learnability of output distributions from near-term local quantum circuits and the impact of T-gates on Clifford circuits. Analyze the potential for quantum advantages in solving NP-hard combinatorial optimization problems. Gain insights into the limitations and possibilities of near-term quantum computing, including quantum error mitigation, noise effects in variational quantum algorithms, and the exploitation of symmetry in NISQ devices.