We provide practical and powerful schemes for learning properties of a quantum state using
a small number of measurements. Specifically, we present a randomized measurement …

We prove that random quantum circuits on any geometry, including a 1D line, can form
approximate unitary designs over $ n $ qubits in $\log n $ depth. In a similar manner, we …

Monitored quantum dynamics—unitary evolution interspersed with measurements—has
recently emerged as a rich domain for phase structure in quantum many-body systems away …

Learning about physical systems from quantum-enhanced experiments can outperform
learning from experiments in which only classical memory and processing are available …

We generalize the classical shadow tomography scheme to a broad class of finite-depth or
finite-time local unitary ensembles, known as locally scrambled quantum dynamics, where …

Classical shadows are a powerful method for learning many properties of quantum states in
a sample-efficient manner, by making use of randomized measurements. Here we study the …

Exploiting near-term quantum computers and achieving practical value is a considerable
and exciting challenge. Most prominent candidates as variational algorithms typically aim to …

We develop a classical shadow tomography protocol utilizing the randomized measurement
scheme based on hybrid quantum circuits, which consist of layers of two-qubit random …

Hybrid tensor networks (hTNs) offer a promising solution for encoding variational quantum
states beyond the capabilities of efficient classical methods or noisy quantum computers …

As a hybrid of artificial intelligence and quantum computing, quantum neural networks
(QNNs) have gained significant attention as a promising application on near-term, noisy …