Quantum annealing is a quantum computing approach widely used for optimization and
probabilistic sampling problems. It is an alternative approach designed due to the limitations …

A comprehensive coverage of the state-of-the-art in quantum machine learning (QML)
methodologies, with a unique perspective on their applications for wireless communications …

This work solves 3SAT, a classical NP-complete problem, on a CMOS-based Ising hardware
chip with all-to-all connectivity. The paper addresses practical issues in going from …

Quantum-approximate optimization algorithm (QAOA) is promising in Noisy Intermediate-
Scale Quantum (NISQ) computers with applications for NP-hard combinatorial optimization …

Quantum Annealing (QA)-accelerated MIMO detection is an emerging research approach in
the context of NextG wireless networks. The opportunity is to enable large MIMO systems …

Exploiting (near-) optimal MIMO signal processing algorithms in the next generation (NextG)
cellular systems holds great promise in achieving significant wireless performance gains in …

Quantum annealing (QA) that encodes optimization problems into Hamiltonians remains the
only near-term quantum computing paradigm that provides sufficient many qubits for real …

Quantum annealing (QA) is proposed for vector perturbation precoding (VPP) in multiple
input multiple output (MIMO) communications systems. The mathematical framework of VPP …

Multiple-input multiple-output (MIMO) is a key ingredient of next-generation wireless
communications. Recently, various MIMO signal detectors based on deep learning …

Maximum-likelihood multiuser detection incurs a large computational complexity, and its low-
complexity detection scheme suffers from a performance loss, where this tradeoff is …