Executable specifications make software assumptions explicit and checkable, yet their role in quantum information and machine learning (QIML) remains underexplored. We argue that executable specifications should be treated as first-class artifacts for independent verification and validation (IV&V) of QIML systems. Static analysis, testing, simulation, and runtime checking can provide evidence about QIML behavior, while Design by Contract shows that selected quantum-state, measurement, and statistical properties can be expressed as checkable conditions. However, adapting existing methods for specification inference and assessment from conventional and machine-learning software to QIML raises several challenges, notably limited evidence for deriving generalizable specifications, cross-stage and probabilistic behavior, repository-scale reasoning across hybrid workflows, and weak behavioral oracles. We characterize these challenges and identify research directions for QIML corpora, specification semantics, repository-level analysis, and specification assessment.

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