Entanglement Diagnostics for Efficient Quantum Computation
This work addresses the challenge of efficient quantum computation for researchers in quantum computing, though it is incremental in refining diagnostic tools.
The authors tackled the problem of optimizing variational quantum circuits by linking entanglement measures to optimization accuracy, showing that entanglement diagnostics can identify effective circuit depths for local Hamiltonian tasks but are insufficient for volume-law entangled states, which require many parameters.
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.