Entanglement geometry constrains quantum circuit scalability and hardness

A new arXiv paper explores the relationship between entanglement geometry and the scalability of quantum computations. The research demonstrates that while certain quantum circuit families, like those using matrix product states (MPS) and tree tensor networks (TTN), can be cut with low overhead, they remain efficiently simulable classically. This finding suggests that asymptotic quantum advantage is unlikely within these specific families. The paper also highlights that achieving hardness and trainability in MPS circuits requires conflicting depth regimes, but proposes using magic states instead of entanglement as a hardness resource to overcome this limitation. AI

IMPACT This research may inform the development of more efficient quantum algorithms and hardware by clarifying the fundamental constraints on quantum computation scalability and hardness.

RANK_REASON The cluster contains a single academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• Clifford+T circuits
• Maria Gragera Garces
• matrix product state
• Tree Tensor Network State with Variable Tensor Order: An Efficient Multireference Method for Strongly Correlated Systems

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