7 papers
MetaTT: A Global Tensor-Train Adapter for Parameter-Efficient Fine-Tuning
Javier Lopez-Piqueres, Pranav Deshpande, Archan Ray +3
We present MetaTT, a Tensor Train (TT) adapter framework for fine-tuning of pre-trained transformers. MetaTT enables flexible and parameter-efficient model adaptation by using a si…
New Improvements in Solving Large LABS Instances Using Massively Parallelizable Memetic Tabu Search
Zhiwei Zhang, Jiayu Shen, Niraj Kumar +1
Low Autocorrelation Binary Sequences (LABS) is a particularly challenging binary optimization problem which quickly becomes intractable in finding the global optimum for problem si…
On the Equivalence between Classical Position Verification and Certified Randomness
Fatih Kaleoglu, Minzhao Liu, Kaushik Chakraborty +4
Gate-based quantum computers hold enormous potential to accelerate classically intractable computational tasks. Random circuit sampling (RCS) is the only known task that has been a…
GPU-Parallelizable Randomized Sketch-and-Precondition for Linear Regression using Sparse Sign Sketches
Tyler Chen, Pradeep Niroula, Archan Ray +3
A litany of theoretical and numerical results have established the sketch-and-precondition paradigm as a powerful approach to solving large linear regression problems in standard c…
Certified randomness using a trapped-ion quantum processor
Minzhao Liu, Ruslan Shaydulin, Pradeep Niroula +29
While quantum computers have the potential to perform a wide range of practically important tasks beyond the capabilities of classical computers, realizing this potential remains a…
Applications of Certified Randomness
Omar Amer, Shouvanik Chakrabarti, Kaushik Chakraborty +8
Certified randomness can be generated with untrusted remote quantum computers using multiple known protocols, one of which has been recently realized experimentally. Unlike the ran…