Phase-coded radar waveform AI-based augmented engineering and optimal design by Quantum Annealing
Abstract
The Integrated Side Lobe Ratio (ISLR) problem we consider here consists in finding optimal sequences of phase shifts in order to minimize the mean squared cross-correlation sidelobes of a transmitted radar signal and a mismatched replica. Given a finite set of quantized phase shifts, the exponentially increasing number of possible sequences leads to long-time processing and approximated results. Currently, ISLR does not seem to be easier than the general polynomial unconstrained binary problem, which is NP-hard. Our contribution is to implement this problem on a Quantum Annealer, a kind of Quantum Computer designed to solve quadratic optimization problems with binary variables. We cast the problem in a suitable formulation to be run on the available DWave quantum device and discuss the performances. Our work shows two limiting factors for scalability: A hardware related one and constraint implementation in the QUBO formulation. More broadly, this study enlightens the limits and the potential of Adiabatic Quantum Computation in terms of speedup and high-scaled resolution of a class of combinatory optimization problems.
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