A new rigorous Gibbs sampling method is given for bosonic models by proving that their dissipative generators have positive spectral gaps, enabling efficient quantum preparation of thermal states for Bose-Hubbard Hamiltonians.
Optimizing random local Hamiltonians by dissipation
3 Pith papers cite this work. Polarity classification is still indexing.
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Efficient learning algorithms for energy estimation imply that stable quantum algorithms cannot prepare low-energy states in systems exhibiting the quantum overlap gap property, as proven for a sparsified quantum p-spin model.
Quantum algorithms achieve exponential fast-forwarding for structured Lindbladian dynamics and coherence-dependent exponential speedup in Gibbs state property estimation.
citing papers explorer
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Simulating Thermal Properties of Bose-Hubbard Models on a Quantum Computer
A new rigorous Gibbs sampling method is given for bosonic models by proving that their dissipative generators have positive spectral gaps, enabling efficient quantum preparation of thermal states for Bose-Hubbard Hamiltonians.
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Quantum Glassiness From Efficient Learning
Efficient learning algorithms for energy estimation imply that stable quantum algorithms cannot prepare low-energy states in systems exhibiting the quantum overlap gap property, as proven for a sparsified quantum p-spin model.
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Exponential Lindbladian fast forwarding and exponential amplification of certain Gibbs state properties
Quantum algorithms achieve exponential fast-forwarding for structured Lindbladian dynamics and coherence-dependent exponential speedup in Gibbs state property estimation.