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 computers are increasing in size and quality but are still very noisy. Error
mitigation extends the size of the quantum circuits that noisy devices can meaningfully …

Mixed-integer linear programming (MILP) has become a cornerstone of operations research.
This is driven by the enhanced efficiency of modern solvers, which can today find globally …

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

Geometric quantum machine learning based on equivariant quantum neural networks
(EQNNs) recently appeared as a promising direction in quantum machine learning. Despite …

We present a quantum algorithm for portfolio optimization. We discuss the market data input
of asset prices, the processing of such data via quantum operations, and the output of …

Quantum enhanced optimization of classical cost functions is a central theme of quantum
computing due to its high potential value in science and technology. The variational …

We develop a hardware-efficient ansatz for variational optimization, derived from existing
ansatzes in the literature, that parametrizes subsets of all interactions in the cost Hamiltonian …

The real-time simulation of large many-body quantum systems is a formidable task, that may
only be achievable with a genuine quantum computational platform. Currently, quantum …

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 …