Quantum learning theory is a new and very active area of research at the intersection of
quantum computing and machine learning. Important breakthroughs in the past two years …

The accumulation of physical errors,–prevents the execution of large-scale algorithms in
current quantum computers. Quantum error correction promises a solution by encoding k …

Topological quantum memory can protect information against local errors up to finite error
thresholds. Such thresholds are usually determined based on the success of decoding …

The manipulation of topologically ordered phases of matter to encode and process quantum
information forms the cornerstone of many approaches to fault-tolerant quantum computing …

Logical qubits can be protected from decoherence by performing quantum error-correction
(QEC) cycles repeatedly. Algorithms for fault-tolerant QEC must be compiled to the specific …

The scaling barriers currently faced by both quantum networking and quantum computing
technologies ultimately amount to the same core challenge of distributing high-quality …

Quantum computers have the potential to provide exponential speedups over their classical
counterparts. Quantum principles are being applied to fields such as communications …

The intensive pursuit for quantum advantage in terms of computational complexity has
further led to a modernized crucial question of when and how will quantum computers …

Scaling up quantum computers to attain substantial speedups over classical computing
requires fault tolerance. Conventionally, protocols for fault-tolerant quantum computation …

Gauging a finite subgroup of a global symmetry can map conventional phases and phase
transitions to unconventional ones. In this work, we study, as a concrete example, an …