Entanglement Diagnostics for Efficient Quantum Computation

Abstract

We consider information spreading measures in randomly initialized variational quantum circuits and introduce entanglement diagnostics for efficient variational quantum/classical computations. We establish a robust connection between entanglement measures and optimization accuracy by solving two eigensolver problems for Ising Hamiltonians with nearest-neighbor and long-range spin interactions. As the circuit depth affects the average entanglement of random circuit states, the entanglement diagnostics can identify a high-performing depth range for optimization tasks encoded in local Hamiltonians. We argue, based on an eigensolver problem for the Sachdev-Ye-Kitaev model, that entanglement alone is insufficient as a diagnostic to the approximation of volume-law entangled target states and that a large number of circuit parameters is needed for such an optimization task.

v2: 16 pages, fully revised for journal submission

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• Barren plateaus in quantum tensor network optimization
• Entanglement entropy production in Quantum Neural Networks
• Efficient estimation of trainability for variational quantum circuits
• Quantum Energy Landscape and VQA Optimization
• Quantum Chaos and Circuit Parameter Optimization
• Characterizing randomness in parameterized quantum circuits through expressibility and average entanglement
• Batched Line Search Strategy for Navigating through Barren Plateaus in Quantum Circuit Training