The Limits of Quantum Optimization

The study demonstrates a complexity-dependent transition in algorithmic efficiency, wherein ordered systems maintain positive efficiency even with increasing size ($N$), while chaotic systems experience efficiency collapse-approaching zero at $N\geq 6$-suggesting an insufficient ancilla channel capacity to resolve scrambled gradient information as complexity increases.

New research reveals fundamental information-theoretic constraints governing the performance of variational quantum algorithms, impacting their ability to solve complex problems.