5 papers
Operator Score Matching for Learning Quantum Hamiltonians
Shreya Shukla, Abhijith Jayakumar, Andrey Y. Lokhov
Learning quantum Hamiltonians from low-temperature thermal state measurements is a fundamental problem in quantum physics. Scalability of existing methods is limited by the complex…
Computationally sufficient statistics for Ising models
Abhijith Jayakumar, Shreya Shukla, Marc Vuffray +2
Learning Gibbs distributions using only sufficient statistics has long been recognized as a computationally hard problem. On the other hand, computationally efficient algorithms fo…
Efficient Learning of Lattice Gauge Theories with Fermions
Shreya Shukla, Yukari Yamauchi, Andrey Y. Lokhov +2
We introduce a learning method for recovering action parameters in lattice field theories. Our method is based on the minimization of a convex loss function constructed using the S…
qa-FLoRA: Data-free query-adaptive Fusion of LoRAs for LLMs
Shreya Shukla, Aditya Sriram, Milinda Kuppur Narayanaswamy +1
The deployment of large language models for specialized tasks often requires domain-specific parameter-efficient finetuning through Low-Rank Adaptation (LoRA) modules. However, eff…
Learning of Statistical Field Theories
Shreya Shukla, Abhijith Jayakumar, Andrey Y. Lokhov
Recovering microscopic couplings directly from data provides a route to solving the inverse problem in statistical field theories, one that complements the traditional-often comput…