We study the performance of classical and quantum machine learning (ML) models in
predicting outcomes of physical experiments. The experiments depend on an input …

Nonstabilizerness, also known as “magic,” stands as a crucial resource for achieving a
potential advantage in quantum computing. Its connection to many-body physical …

Recent work has explored using the stabilizer formalism to classically simulate quantum
circuits containing a few non-Clifford gates. The computational cost of such methods is …

The precise control of complex quantum systems promises numerous technological
applications including digital quantum computing. The complexity of such devices renders …

Repeated local measurements of quantum many-body systems can induce a phase
transition in their entanglement structure. These measurement-induced phase transitions …

Many-body unitary dynamics interspersed with repeated measurements display a rich
phenomenology hallmarked by measurement-induced phase transitions. Employing …

Nonstabilizerness or magic resource characterizes the amount of non-Clifford operations
needed to prepare quantum states. It is a crucial resource for quantum computing and a …

The applications of random quantum circuits range from quantum computing and quantum
many-body systems to the physics of black holes. Many of these applications are related to …

Magic is a property of quantum states that enables universal fault-tolerant quantum
computing using simple sets of gate operations. Understanding the mechanisms by which …

We introduce a method to measure many-body magic in quantum systems based on a
statistical exploration of Pauli strings via Markov chains. We demonstrate that sampling such …