Phyllis Wang will present her MSE talk "Implementation and Scaling of Quantum Cognitive Models" on Thursday, April 23rd, 2026 at 1pm in CS 301. Adviser: Margaret Martonosi, Reader: Raghav Pothukuchi All are welcome to attend Title: Implementation and Scaling of Quantum Cognitive Models Abstract: The computational aspect of cognitive models inspired by quantum mechanics poses unique considerations in their implementation, both through classical and quantum computing. We examine the quantum well-inspired Multi-Particle Multi-Well (MPMW) model for decision making to find how quantum cognitive techniques scale on various types of hardware and modeling approaches. This thesis explores an end-to-end implementation of the MPMW model, including the representation of quantum wells as the landscape becomes more discretized, addressing the challenge of memory bottlenecks that arise from representing quantum states. We further explore the computational and memory scaling limits of finding the eigenstates of the wells, and analyze the results of scaling on the model predictions. Finally, we look at the role of quantum computing and the possibility of scaling model sizes further through the use of the hybrid quantum-classical Variational Quantum Eigensolver algorithm.