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 …

Quantum computers can execute algorithms that dramatically outperform classical
computation. As the best-known example, Shor discovered an efficient quantum algorithm …

There has been recent promising experimental and theoretical evidence that quantum
computational tools might enhance the precision and efficiency of physical experiments …

The Petz recovery channel plays an important role in quantum information science as an
operation that approximately reverses the effect of a quantum channel. The pretty good …

In learning theory, the VC dimension of a concept class ℒ is the most common way to
measure its" richness." A fundamental result says that the number of examples needed to …

We analyze the complexity of learning $ n $-qubit quantum phase states. A degree-$ d $
phase state is defined as a superposition of all $2^ n $ basis vectors $ x $ with amplitudes …

In recent years, rapid developments of quantum computer are witnessed in both the
hardware and the algorithm domains, making it necessary to have an updated review of …

The concept of antidistinguishability of quantum states has been studied to investigate
foundational questions in quantum mechanics. It is also called quantum state elimination …

We approach the hidden subgroup problem by performing the so-called pretty good
measurement on hidden subgroup states. For various groups that can be expressed as the …

It has been known for some time that graph isomorphism reduces to the hidden subgroup
problem (HSP). What is more, most exponential speedups in quantum computation are …