Discovery of connectivity-trainability trade-off of IQP Circuits for Hamiltonian Optimization
cs.SI, cs.NA, math.NA, physics.data-an, quant-ph
Submitted: 2026-06-23
Updated: 2026-06-24
Comments: The data about the gradient variance (Fig. 2) should be check again, there are maybe some errors in that data
License: http://creativecommons.org/licenses/by/4.0/
The gist: Instantaneous Quantum Polynomial-time (IQP) circuits are promising candidates for near-term quantum advantage due to the conjectured classical hardness of their sampling task.
Terminology
Abstract
Instantaneous Quantum Polynomial-time (IQP) circuits are promising candidates for near-term quantum advantage due to the conjectured classical hardness of their sampling task. However, their capabilities for optimization remain largely unexplored. We present a systematic investigation of the performance and trainability of IQP circuits for Hamiltonian optimization. Our results reveal a trade-off between optimization performance and circuit connectivity, demonstrating that the circuit structure plays a key role in determining the ability of IQP circuits to reach low-energy states.
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