The Learnability of Pauli Noise in Quantum Gate Characterization
Watch a 28-minute conference talk from the 18th Theory of Quantum Computation Conference (TQC 2023) exploring the characterization of learnable and unlearnable aspects of Pauli noise channels in quantum gates. Discover how cycle space and cut space in pattern transfer graphs determine what information can be learned about noise in Clifford gates, demonstrating the optimality of cycle benchmarking techniques. Follow along as experimental results from IBM's CNOT gate characterization are presented, including insights on state preparation noise and the challenges of unlearnable degrees of freedom. Learn about the limitations of assuming perfect initial state preparation and how physical constraints can help bound unlearnable parameters in quantum benchmarking algorithms.