Neural network state estimation for full quantum state tomography
This work addresses the challenge of efficient state estimation in quantum computing, representing an incremental improvement in method.
The paper tackles the problem of full quantum state tomography by introducing a neural network estimation model, achieving the most efficient computational complexity among existing algorithms and demonstrating accuracy and scalability in numerical tests.
An efficient state estimation model, neural network estimation (NNE), empowered by machine learning techniques, is presented for full quantum state tomography (FQST). A parameterized function based on neural network is applied to map the measurement outcomes to the estimated quantum states. Parameters are updated with supervised learning procedures. From the computational complexity perspective our algorithm is the most efficient one among existing state estimation algorithms for full quantum state tomography. We perform numerical tests to prove both the accuracy and scalability of our model.