7 citations · 7 across the 2 of their papers we have counts for
6 papers
Learning quantum Hamiltonians at any temperature in polynomial time
Ainesh Bakshi, Allen Liu, Ankur Moitra +1
We study the problem of learning a local quantum Hamiltonian given copies of its Gibbs state at a known inverse temperature . Anshu,…
Structure learning of Hamiltonians from real-time evolution
Ainesh Bakshi, Allen Liu, Ankur Moitra +1
We study the problem of Hamiltonian structure learning from real-time evolution: given the ability to apply for an unknown local Hamiltonian $H = \sum_{a = 1}^…
A Dobrushin condition for quantum Markov chains: Rapid mixing and conditional mutual information at high temperature
Ainesh Bakshi, Allen Liu, Ankur Moitra +1
A central challenge in quantum physics is to understand the structural properties of many-body systems, both in equilibrium and out of equilibrium. For classical systems, we have a…
Learning the closest product state
Ainesh Bakshi, John Bostanci, William Kretschmer +5
We study the problem of finding a (pure) product state with optimal fidelity to an unknown -qubit quantum state , given copies of . This is a basic instance of a fundame…
High-Temperature Gibbs States are Unentangled and Efficiently Preparable
Ainesh Bakshi, Allen Liu, Ankur Moitra +1
We show that thermal states of local Hamiltonians are separable above a constant temperature. Specifically, for a local Hamiltonian on a graph with degree , its G…
Model Stealing for Any Low-Rank Language Model
Allen Liu, Ankur Moitra
Model stealing, where a learner tries to recover an unknown model via carefully chosen queries, is a critical problem in machine learning, as it threatens the security of proprieta…