7 papers
Honeycombs and Sums of Hermitian Matrices, Revisited
Ankur Moitra, Alexander Postnikov, Dora Woodruff
We give a new proof of the celebrated theorem of Knutson and Tao that the spectra of triples of Hermitian matrices exactly correspond to positions of boundary rays of h…
The tractability landscape of diffusion alignment: regularization, rewards, and computational primitives
Ankur Moitra, Andrej Risteski, Dhruv Rohatgi
Inference-time reward alignment asks how to turn a pre-trained diffusion model with base law into a sampler that favors a reward while remaining close to . Since there i…
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…
Precise Error Rates for Computationally Efficient Testing
Ankur Moitra, Alexander S. Wein
We revisit the fundamental question of simple-versus-simple hypothesis testing with an eye towards computational complexity, as the statistically optimal likelihood ratio test is o…