Abstract Machine learning has achieved dramatic success over the past decade, with
applications ranging from face recognition to natural language processing. Meanwhile, rapid …

A fundamental distinction between many-body quantum states are those with short-and long-
range entanglement (SRE and LRE). The latter cannot be created by finite-depth circuits …

Quantum computing crucially relies on the ability to efficiently characterize the quantum
states output by quantum hardware. Conventional methods which probe these states …

We present a unified perspective on symmetry protected topological (SPT) phases in one
dimension and address the open question of what characterizes their phase transitions. In …

Topological phases are characterized by edge states confined near the boundaries by a
bulk energy gap. On raising temperature, these edge states are typically lost due to mobile …

Characterizing multipartite quantum systems is crucial for quantum computing and many-
body physics. The problem, however, becomes challenging when the system size is large …

Identifying topological properties is a major challenge because, by definition, topological
states do not have a local order parameter. While a generic solution to this challenge is not …

Quantum convolutional neural networks (QCNNs) have been introduced as classifiers for
gapped quantum phases of matter. Here, we propose a model-independent protocol for …

Long-range interactions can fundamentally alter properties in gapped topological phases
such as emergent massive edge modes. However, recent research has shifted attention to …

By constructing an exactly solvable spin model, we investigate the critical behaviors of
transverse-field Ising chains interpolated with cluster interactions, which exhibit various …