Quantum-Optimized Adaptive Beamforming for MIMO Phased-Array Radar in Spectrum-Contested Environments

The electromagnetic spectrum is growing more and more cluttered with the advent of 5G/6G networks and constellations of satellites, and MIMO phased-array radar systems are under more signal processing pressure than ever. Working in such spectrum-contested environments entails the suppression of complex interference with a high-precision tracking of the target. Traditional adaptive beamforming methods are frequently faced with computational bottlenecks at high-dimensional antenna arrays, unable to converge in real time in dynamically evolving electronic warfare. The given paper suggests an implementation of a hybrid quantum-classical optimization framework based on the Variational Quantum Algorithms (VQAs) and the Quantum Approximate Optimization Algorithm (QAOA) to speed up the calculation of adaptive beamforming weights. Having reduced the Signal-to-Interference-Plus-Noise Ratio (SINR) maximization problem to a quantum Hamiltonian, we will prove that quantumenhanced solvers can explore the complicated optimization surfaces more efficiently than classical interior-point solvers. We have found that this hybrid scheme offers a significant reduction in null-steering latency and improved interference rejection. This paper provides a theoretical basis for implementing quantum accelerators in the next redesign of the cognitive radar architectures, so that they have operational superiority in congested and adversarial spectral domains.