Seyyede Elham Mousavigharalari

We study how a phase parameter ϕ , encoded through a single-qubit H φ H gate sequence, is reflected in the quantum fisher information (QFI) under realistic noisy dynamics. Within a collision-model framework, a probe qubit interacts sequentially with algorithmically prepared reservoir ancillas, leading to a ϕ -dependent steady state from which F ϕ can be evaluated in closed form. In parallel, we perform pulse-resolved open-system simulations of the same gate sequence, using Gaussian-driven control motivated by transmon hardware, to obtain the corresponding pre-measurement density matrix. Despite the distinct physical descriptions, both approaches yield QFI profiles with similar phase dependence on the encoded phase. A quantitative comparison using profile-level similarity metrics further shows that the two descriptions identify the same phase-sensitive regions, although their absolute QFI contrasts differ. This consistency indicates that the steady state of a probe qubit interacting with algorithmically prepared ancillas can capture key features of the phase response observed in noisy device-level implementations. The underlying physical mechanism is the persistence of finite steady coherence in the asymptotic probe state, which retains the phase imprint of the prepared ancillas. Beyond conceptual insight, the steady-state framework provides a model-based, tomography-free diagnostic route for characterizing phase sensitivity. Possible uses in biased-noise error correction, hardware-aware compilation, and pulse-level optimization are therefore presented as future outlook directions.

The promising performance increase offered by quantum computing has led to the idea of applying it to neural networks. Studies in this regard can be divided into two main categories: simulating quantum neural networks with the standard quantum circuit model, and implementing them based on hardware. However, the ability to capture the non-linear behavior in neural networks using a computation process that usually involves linear quantum mechanics principles remains a major challenge in both categories. In this study, a hardware-efficient quantum neural network operating as an open quantum system is proposed, which presents non-linear behaviour. The model's compatibility with learning processes is tested through the obtained analytical results. In other words, we show that this dissipative model based on repeated interactions, which allows for easy parametrization of input quantum information, exhibits differentiable, non-linear activation functions.