Artificial intelligence is evolving raising significant challenges in terms of power consumption and processing speed. Deep Neural Networks (DNNs) on digital hardware require continuous data transfer between processor and memory, a problem known as the Von Neumann bottleneck. Bio-inspired computing systems, such as memristor devices arranged in a crossbar configuration, are alternative hardware architectures that can implement neural networks embedding weights in their conductance values. This configuration allows information to be stored and processed simultaneously, overcoming traditional bottlenecks. In parallel, the lack of cryogenic electronics introduces other challenges in the quantum computing field, where qubits need to be placed at cryogenic temperature to reduce thermal noise.