5 papers
Flow-based Phase-space Tomography of Continuous-variable Quantum States
Owen Dugan, Rumen Dangovski, Peter Y. Lu +1
Continuous-variable quantum state tomography is limited by the cost of resolving non-Gaussian structure in high-dimensional phase space. We introduce QST-Flow, a quantum state tomo…
Active learning for photonic crystals
Ryan Lopez, Charlotte Loh, Rumen Dangovski +1
Active learning for photonic crystals explores the integration of analytic approximate Bayesian last layer neural networks (LL-BNNs) with uncertainty-driven sample selection to acc…
A Comparative Analysis of LLM Adaptation: SFT, LoRA, and ICL in Data-Scarce Scenarios
Bernd Bohnet, Rumen Dangovski, Kevin Swersky +4
The remarkable capabilities of Large Language Models (LLMs) often need to be tailored for specific applications, requiring the integration of new knowledge or the acquisition of ne…
Multimodal Foundation Models for Material Property Prediction and Discovery
Viggo Moro, Charlotte Loh, Rumen Dangovski +7
Artificial intelligence is transforming computational materials science, improving the prediction of material properties, and accelerating the discovery of novel materials. Recentl…
QuanTA: Efficient High-Rank Fine-Tuning of LLMs with Quantum-Informed Tensor Adaptation
Zhuo Chen, Rumen Dangovski, Charlotte Loh +3
We propose Quantum-informed Tensor Adaptation (QuanTA), a novel, easy-to-implement, fine-tuning method with no inference overhead for large-scale pre-trained language models. By le…