We present a classical algorithm for estimating expectation values of arbitrary observables
on most quantum circuits across all circuit architectures and depths, including those with all …

Optimized power system planning and operation are core to delivering a low-cost and high-
reliability transition path to net-zero carbon emissions. The major technological changes …

Quantum computing is gaining popularity across a wide range of scientific disciplines due to
its potential to solve long-standing computational problems that are considered intractable …

Quantum computers promise to enhance machine learning for practical applications.
Quantum machine learning for real-world data has to handle extensive amounts of high …

Many-body Green's functions encode all the properties and excitations of interacting
electrons. While these are challenging to be evaluated accurately on a classical computer …

We provide a noisy intermediate-scale quantum framework for simulating the dynamics of
open quantum systems, generalized time evolution, non-linear differential equations and …

Quantum computing is an emerging technology with potentially far-reaching implications for
national prosperity and security. Understanding the timeframes over which economic …

Understanding the capabilities of classical simulation methods is key to identifying where
quantum computers are advantageous. Not only does this ensure that quantum computers …

Quantum optimization methods use a continuous degree-of-freedom of quantum states to
heuristically solve combinatorial problems, such as the MAX-CUT problem, which can be …

Semidefinite programs are optimization methods with a wide array of applications, such as
approximating difficult combinatorial problems. One such semidefinite program is the …