Manual experimental design fails to capture complex interactions in microspheric material synthesis. Automating multi-objective optimization overcomes the bottleneck of slow, sequential trial-and-error discovery.

Suboptimal coordination between classical control signals and quantum hardware operations creates execution bottlenecks. Improving timing and resource allocation reduces decoherence errors and increases computational throughput.

The presence of interfering solvents and catalyst residues prevents precise measurement of target compounds like retinyl. Reducing measurement error enables reliable quality control in complex chemical mixtures.