6 citations · 10 across the 5 of their papers we have counts for
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…
Stochastic logic in biased coupled photonic probabilistic bits
Michael Horodynski, Charles Roques-Carmes, Yannick Salamin +4
Optical computing often employs tailor-made hardware to implement specific algorithms, trading generality for improved performance in key aspects like speed and power efficiency. A…
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…
TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
Zhuo Chen, Jacob McCarran, Esteban Vizcaino +2
Partial differential equations (PDEs) are instrumental for modeling dynamical systems in science and engineering. The advent of neural networks has initiated a significant shift in…
Photonic probabilistic machine learning using quantum vacuum noise
Seou Choi, Yannick Salamin, Charles Roques-Carmes +7
Probabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum n…