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Improved algorithms for learning quantum Hamiltonians, via flat polynomials
July 05, 2024 Β· Declared Dead Β· π Annual Conference Computational Learning Theory
"No code URL or promise found in abstract"
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Authors
Shyam Narayanan
arXiv ID
2407.04540
Category
quant-ph: Quantum Computing
Cross-listed
cs.DS,
cs.LG
Citations
5
Venue
Annual Conference Computational Learning Theory
Last Checked
5 months ago
Abstract
We give an improved algorithm for learning a quantum Hamiltonian given copies of its Gibbs state, that can succeed at any temperature. Specifically, we improve over the work of Bakshi, Liu, Moitra, and Tang [BLMT24], by reducing the sample complexity and runtime dependence to singly exponential in the inverse-temperature parameter, as opposed to doubly exponential. Our main technical contribution is a new flat polynomial approximation to the exponential function, with significantly lower degree than the flat polynomial approximation used in [BLMT24].
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