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Rank-Adaptive Matrix-Free Atomic Quantum State Tomography
Amirhossein Taherpour, Alireza Sadeghi, Georgios B. Giannakis
Quantum state tomography estimates an unknown density operator from measurement data. Dense reconstruction however, can be impractical for many-qubit systems because the Hilbert-sp…
Low-Rank Adaptation Redux for Large Models
Bingcong Li, Yilang Zhang, Georgios B. Giannakis
Low-rank adaptation (LoRA) has emerged as the de facto standard for parameter-efficient fine-tuning (PEFT) of foundation models, enabling the adaptation of billion-parameter networ…
Distributed Learning of Quantum State Tomography Robust to Readout Errors
Amirhossein Taherpour, Alireza Sadeghi, Georgios B. Giannakis
Scalable estimation of quantum states with readout errors is a central challenge in large multiqubit systems. Existing overlapping-tomography methods improve scalability by working…
Binomial Gradient-Based Meta-Learning for Enhanced Meta-Gradient Estimation
Yilang Zhang, Abraham Jaeger Mountain, Bingcong Li +1
Meta-learning offers a principled framework leveraging \emph{task-invariant} priors from related tasks, with which \emph{task-specific} models can be fine-tuned on downstream tasks…