Quantum

The Prevalence of Straight Shots in Control Space for Reaching Optimal Quantum Performance

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Based on a feed excerpt; full article content has not been acquired. Source evidence retrieved 22/10/2026, 04:13:24 UTC.

Briefings

A new non-stochastic algorithm (STSA) identifies a large class of straight-shot optimization paths in quantum control landscapes, where movement along the path corresponds to a less demanding climb up the landscape. The study analyzed over 10,000 distinct paths across state-to-state transitions, general observables, and unitary transformations, finding that nearest-neighbor distances decrease with sample size, suggesting high prevalence. While the algorithm is not yet high-efficiency, the findings open future advances in accelerating straight-shot discovery.

Key points

• A non-stochastic straight-shot search algorithm (STSA) was introduced to identify a large class of straight-shot optimization paths in quantum control landscapes.
• Analysis of over 10,000 distinct paths revealed that nearest-neighbor distances decrease as sample size increases, suggesting the prevalence of straight shots in control space.

Context

Gradient-based algorithms typically follow steepest ascent paths to maximize quantum objectives, whereas this paper explores straight shots where movement corresponds to a less demanding climb up the landscape. Existing studies identified straight shots but required computationally intensive stochastic methods; this work provides a non-stochastic approach to locate massive numbers of such paths.

Limitations

The STSA is not a high-efficiency algorithm in its present form. The study focuses on locating massive numbers of straight solutions rather than optimizing them. The conjecture about connected neighborhoods on landscape level sets remains unverified, and the algorithm's efficiency for accelerating future discovery is not confirmed.

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