Neural networks trained via supervised learning on simulated noisy measurements can mitigate unknown noise in quantum state tomography for pure and mixed states.
Neural network state estimation for full quantum state tomography
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abstract
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.
fields
quant-ph 2verdicts
UNVERDICTED 2representative citing papers
Two neural network architectures achieve state-of-the-art performance in quantum state tomography for pure and mixed states by incorporating class information.
citing papers explorer
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Error-mitigated quantum state tomography using neural networks
Neural networks trained via supervised learning on simulated noisy measurements can mitigate unknown noise in quantum state tomography for pure and mixed states.
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Optical Quantum Mixed-State Reconstruction With Multiple Deep Learning Approaches
Two neural network architectures achieve state-of-the-art performance in quantum state tomography for pure and mixed states by incorporating class information.