Classical shadows enable us to learn many properties of a quantum state ρ with very few
measurements. However, near-term and early fault-tolerant quantum computers will only be …

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 …

We propose to use wave function overlaps obtained from a quantum computer as inputs for
the classical split-amplitude techniques, tailored and externally corrected coupled cluster, to …

Simulations of quantum chemistry and quantum materials are believed to be among the
most important applications of quantum information processors. However, realizing practical …

The recent advances in quantum information processing, sensing, and communications are
surveyed with the objective of identifying the associated knowledge gaps and formulating a …

Quantum-enhanced auxiliary field quantum Monte Carlo (QC-AFQMC) uses output from a
quantum computer to increase the accuracy of its classical counterpart. The algorithm …

Quantum computing requires a universal set of gate operations; regarding gates as
rotations, any rotation angle must be possible. However a real device may only be capable …

Successful implementations of quantum technologies require protocols and algorithms that
use as few quantum resources as possible. However, many important quantum operations …

Shallow quantum circuits are believed to be the most promising candidates for achieving
early practical quantum advantage—this has motivated the development of a broad range of …

Efficiently learning expectation values of a quantum state using classical shadow
tomography has become a fundamental task in quantum information theory. In a classical …