Parameterised graph theory applied to tensor networks for simulation and learning

Researchers have applied parameterised graph theory to tensor networks, exploring how graph parameters influence the complexity of simulating and learning these networks. The study demonstrates that cutwidth and tree-cutwidth can bound the overhead for representing tensor-network states as matrix product states or tree tensor networks. Additionally, new graph-dependent bounds have been derived for the sample and computational complexity of tensor-network state tomography, extending existing learning algorithms to more complex network structures. AI

IMPACT This research could lead to more efficient methods for simulating and learning complex quantum states, potentially impacting future AI architectures that leverage tensor network principles.

RANK_REASON The cluster contains a single academic paper detailing new theoretical research in a specialized area of physics and computer science. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

• Bakshi
• Cramer
• Lin
• Markov
• matrix product state
• Parameterised graph theory
• Tensor Networks
• Tree Tensor Network State with Variable Tensor Order: An Efficient Multireference Method for Strongly Correlated Systems

• paper
• infra

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