糖心原创

Organisers 

• Kay Brandner, School of Physics and Astronomy
• Mădălin Guţă, School of Mathematical Sciences 

Keynote Speaker 

• Matthias C. Caro, University of Warwick
  
  Learning Quantum States and Unitaries of Bounded Gate Complexity
  
  Abstract: While quantum state tomography is notoriously hard, most states hold little interest to practically-minded tomographers. Given that states and unitaries appearing in Nature are of bounded gate complexity, it is natural to ask if efficient learning becomes possible. In this work, we prove that to learn a state generated by a quantum circuit with G two-qubit gates to a small trace distance, a sample complexity scaling linearly in G is necessary and sufficient. We also prove that the optimal query complexity to learn a unitary generated by G gates to a small average-case error scales linearly in G. While sample-efficient learning can be achieved, we show that under reasonable cryptographic conjectures, the computational complexity for learning states and unitaries of gate complexity G must scale exponentially in G. We illustrate how these results establish fundamental limitations on the expressivity of quantum machine learning models and provide new perspectives on no-free-lunch theorems in unitary learning. Together, our results answer how the complexity of learning quantum states and unitaries relate to the complexity of creating these states and unitaries.

Local Speakers 

• Maël Bompais, School of Mathematical Sciences 
• Adam Gammon-Smith, School of Physics and Astronomy 
• Quentin Muller, School of Mathematical Sciences  
• Stephen Powell, School of Physics and Astronomy 
• Tianyi Yan, School of Physics and Astronomy