Quantum machine learning for chemistry and physics
Machine learning (ML) has emerged as a formidable force for identifying hidden but
pertinent patterns within a given data set with the objective of subsequent generation of …
pertinent patterns within a given data set with the objective of subsequent generation of …
Quantum state tomography with conditional generative adversarial networks
Quantum state tomography (QST) is a challenging task in intermediate-scale quantum
devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the …
devices. Here, we apply conditional generative adversarial networks (CGANs) to QST. In the …
Modern applications of machine learning in quantum sciences
In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …
advances in the application of machine learning methods in quantum sciences. We cover …
From architectures to applications: A review of neural quantum states
H Lange, A Van de Walle, A Abedinnia… - arxiv preprint arxiv …, 2024 - arxiv.org
Due to the exponential growth of the Hilbert space dimension with system size, the
simulation of quantum many-body systems has remained a persistent challenge until today …
simulation of quantum many-body systems has remained a persistent challenge until today …
Measurement-based feedback quantum control with deep reinforcement learning for a double-well nonlinear potential
Closed loop quantum control uses measurement to control the dynamics of a quantum
system to achieve either a desired target state or target dynamics. In the case when the …
system to achieve either a desired target state or target dynamics. In the case when the …
Neural-network quantum state tomography
D Koutný, L Motka, Z Hradil, J Řeháček… - Physical Review A, 2022 - APS
We revisit the application of neural networks to quantum state tomography. We confirm that
the positivity constraint can be successfully implemented with trained networks that convert …
the positivity constraint can be successfully implemented with trained networks that convert …
Classification and reconstruction of optical quantum states with deep neural networks
We apply deep-neural-network-based techniques to quantum state classification and
reconstruction. Our methods demonstrate high classification accuracies and reconstruction …
reconstruction. Our methods demonstrate high classification accuracies and reconstruction …
Gradient-descent quantum process tomography by learning Kraus operators
We perform quantum process tomography (QPT) for both discrete-and continuous-variable
quantum systems by learning a process representation using Kraus operators. The Kraus …
quantum systems by learning a process representation using Kraus operators. The Kraus …
Efficient quantum state tomography with convolutional neural networks
Modern day quantum simulators can prepare a wide variety of quantum states but the
accurate estimation of observables from tomographic measurement data often poses a …
accurate estimation of observables from tomographic measurement data often poses a …
Multimodal deep representation learning for quantum cross-platform verification
Cross-platform verification, a critical undertaking in the realm of early-stage quantum
computing, endeavors to characterize the similarity of two imperfect quantum devices …
computing, endeavors to characterize the similarity of two imperfect quantum devices …