Quantum computing is a game-changing technology for global academia, research centers
and industries including computational science, mathematics, finance, pharmaceutical …

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 …

Deep learning has been shown to be able to recognize data patterns better than humans in
specific circumstances or contexts. In parallel, quantum computing has demonstrated to be …

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 …

It is a critical challenge to simultaneously achieve high interpretability and high efficiency
with the current schemes of deep machine learning (ML). The tensor network (TN), a well …

Quantum process tomography is an experimental technique to fully characterize an
unknown quantum process. Standard quantum process tomography suffers from …

The ability to efficiently simulate random quantum circuits using a classical computer is
increasingly important for developing noisy intermediate-scale quantum devices. Here, we …

In scenarios where a large amount of data needs to be learned, incremental learning can
make full use of old knowledge, significantly reduce the computational cost of the overall …

Quantum state tomography (QST) is a fundamental technique in quantum information
processing (QIP) for reconstructing unknown quantum states. However, the conventional …

Bosonic variational quantum circuits (VQCs) are crucial for information processing in
microwave cavities, trapped ions, and optical systems, widely applicable in quantum …