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 …

We review the mathematical speed limits on quantum information processing in many-body
systems. After the proof of the Lieb–Robinson Theorem in 1972, the past two decades have …

We report an accurate and efficient classical simulation of a kicked Ising quantum system on
the heavy hexagon lattice. A simulation of this system was recently performed on a 127-qubit …

We study the problem of learning a Hamiltonian H to precision ε, supposing we are given
copies of its Gibbs state ρ=\exp(-βH)/Tr(\exp(-βH)) at a known inverse temperature β. Anshu …

Estimating expectation values is a key subroutine in quantum algorithms. Near-term
implementations face two major challenges: a limited number of samples required to learn a …

Several quantum hardware platforms, while being unable to perform fully fault-tolerant
quantum computation, can still be operated as analogue quantum simulators for addressing …

The thermal or equilibrium ensemble is one of the most ubiquitous states of matter. For
models comprised of many locally interacting quantum particles, it describes a wide range of …

Despite fundamental interests in learning quantum circuits, the existence of a
computationally efficient algorithm for learning shallow quantum circuits remains an open …

Calculating the equilibrium properties of condensed-matter systems is one of the promising
applications of near-term quantum computing. Recently, hybrid quantum-classical time …

Many quantum algorithms rely on the measurement of complex quantum amplitudes.
Standard approaches to obtain the phase information, such as the Hadamard test, give rise …