Computational models are an essential tool for the design, characterization, and discovery
of novel materials. Computationally hard tasks in materials science stretch the limits of …

In recent years, research in quantum computing has largely focused on two approaches:
near-term intermediate-scale quantum (NISQ) computing and future fault-tolerant quantum …

Combinatorial optimization is a broadly attractive area for potential quantum advantage, but
no quantum algorithm has yet made the leap. Noise in quantum hardware remains a …

Constrained optimization problems are ubiquitous in science and industry. Quantum
algorithms have shown promise in solving optimization problems, yet none of the current …

In this paper we consider the scalability of multi-angle QAOA with respect to the number of
QAOA layers. We found that MA-QAOA is able to significantly reduce the depth of QAOA …

Quantum alternating operator ansatz (QAOA) has a strong connection to the adiabatic
algorithm, which it can approximate with sufficient depth. However, it is unclear to what …

A bstract We construct a model of Pauli spin operators with all-to-all 4-local interactions by
replacing Majorana fermions in the SYK model with spin operators. Equivalently, we replace …

Federated learning has emerged as a viable distributed solution to train machine learning
models without the actual need to share data with the central aggregator. However, standard …

Until high-fidelity quantum computers with a large number of qubits become widely
available, classical simulation remains a vital tool for algorithm design, tuning, and …

Numerical algorithms to solve mathematical optimization problems efficiently are essential to
applications in many areas of engineering and computational science. To solve optimization …