Środowiskowe Seminarium z Informacji i Technologii Kwantowych

Machine learning has recently developed novel approaches, mimicking the synapses of the human brain to achieve similarly efficient learning strategies. Such an approach retains the universality of standard methods, while attempting to circumvent their excessive requirements, which hinder their scalability. In this landscape, quantum (or quantum inspired) algorithms may bring enhancement. However, high-performing neural networks invariably display nonlinear behaviours, which poses a challenge to quantum platforms, given the intrinsically linear evolution of closed systems. One possible architecture which can overcome this issue is the use of quantum memristors.

In this talk, I will begin by introducing the general concept of neuromorphic computing, then discuss how photonic quantum memristors fit within this framework, and finally present experimental results demonstrating neuromorphic computing with a photonic quantum memristor (based on arXiv:2504.18694.).