Prospects of quantum-classical optimization for digital design

Abstract The optimal design of digital systems can be complicated by non-linearities that arise from discrete optimization processes, and conventional methods often attain suboptimal results. Quantum computing may be an interesting technology to overcome that issue, but so far its effectiveness has been addressed from a most abstract perspective. This paper tackles the digital-design problem from a practical viewpoint, and derives universal criteria for assessing whether, and how much, quantum technologies can improve over conventional methods: the advantage is expressed in terms of computing time required to attain optimal design. The general framework is applied to two modern problems, i.e., training support vector machines and building vector quantizers. The experimental comparison between conventional and quantum methods addresses the actual performances of the designed systems in the case of vector quantization. Empirical evidence matches theoretical expectations and supports the design method’s consistency.