Clinical trials are necessary for assessing the safety and efficacy of treatments. However,
trial timelines are severely delayed with minimal success due to a multitude of factors …

Quantum computing has emerged as a powerful computational paradigm capable of solving
problems beyond the reach of classical computers. However, today's quantum computers …

Merging machine learning with the power of quantum computing holds great potential for
data-driven decision making and the development of powerful models for complex datasets …

Combinatorial optimization is a challenging problem applicable in a wide range of fields
from logistics to finance. Recently, quantum computing has been used to attempt to solve …

We introduce and implement GraphDD: an efficient method for real-time, circuit-specific,
optimal embedding of dynamical decoupling (DD) into executable quantum algorithms. We …

The quantum approximate optimization algorithm (QAOA) is a quantum heuristic for
combinatorial optimization that has been demonstrated to scale better than state-of-the-art …

Simulations of collisions of fundamental particles on a quantum computer are expected to
have an exponential advantage over classical methods and promise to enhance searches …

Recent work [Sachdeva et al.] presented an iterative hybrid quantum variational optimization
algorithm designed by Q-CTRL and executed on IBM gate-based quantum processing units …

We present Benchpress, a benchmarking suite for evaluating the performance and range of
functionality of multiple quantum computing software development kits. This suite consists of …