In this paper, we present a versatile and novel framework for antenna array synthesis and optimization by leveraging simulated bifurcations of nonlinear oscillators. The desired antenna radiation profile is recast into an Ising Hamiltonian form, where the optimal antenna configuration is found by solving the ground-state solution of the Hamiltonian function. At the core of this framework is the quantum-inspired simulated bifurcation algorithm, which enables the efficient computation of ground-state solutions. We demonstrate the performance of the proposed work across a wide range of antenna synthesis scenarios, including array thinning, super-resolution beamforming, weighted beamforming, and beam shaping. The results highlight its potential as a powerful tool for addressing complex optimization problems in the field of antenna arrays.

We present a novel and flexible method to optimize the phase response of reflective metasurfaces towards a desired scattering profile. The scattering power is expressed as a spin-chain Hamiltonian using the radar cross section formalism. For metasurfaces reflecting an oblique plane wave, an Ising Hamiltonian is obtained. Thereby, the problem of achieving the scattering profile is recast into finding the ground-state solution of the associated Ising Hamiltonian. To rapidly explore the configuration states, we encode the Ising coefficients with quantum annealing algorithms, taking advantage of the fact that the adiabatic evolution efficiently performs energy minimization in the Ising model. Finally, the optimization problem is solved on the D-Wave 2048-qubit quantum adiabatic optimizer machine for binary phase as well as quadriphase reflective metasurfaces. Even though the work is focused on the phase modulation of metasurfaces, we believe this approach paves the way to fast optimization of reconfigurable intelligent surfaces that are mod- ulated in both amplitude and phase for multi-beam generation in and beyond 5G/6G mobile networks.