Date:
Location:
Summerlee Science Complex 1511
SPEAKER: NINGPING CAO, PhD Candidate in Mathematics
ABSTRACT:
Quantum state tomography is a daunting challenge of experimental quantum computing even in moderate system size. One way to boost the efficiency of state tomography is via local measurements on reduced density matrices, but the reconstruction of the full state thereafter is hard. Here, we present a machine learning method to recover the full quantum state from its local information, where a fully-connected neural network is built to fulfill the task with up to seven qubits. In particular, we test the neural network model with a practical dataset, that in a 4-qubit nuclear magnetic resonance system our method yields global states via 2-local information with high accuracy. Our work paves the way towards scalable state tomography in large quantum systems.
