Diffusion-Driven Synthesis of Parameterized Quantum Circuits for Efficient Hybrid Deep Learning Pipelines

Hybrid quantum–classical deep learning increasingly relies on Parameterized Quantum Circuits (PQCs) as differentiable feature layers, yet practical deployment remains constrained by manual ansatz design, low-yield architecture search, and instability under hardware connectivity and shot-noise limits. This study introduces a diffusion-driven PQC synthesis framework that generates discrete gate-token architectures via conditional denoising, enforcing feasibility through constraint-aware masking and shaping candidate quality through preference-weighted training and screening-based selection. Under matched compute budgets across baselines, the proposed method achieves a higher valid-circuit yield and concentrates candidates in favorable performance–complexity regions. In comparative evaluation, diffusion-driven synthesis improves average downstream task performance to 0.80 with reduced variance (±0.02) while lowering average circuit complexity to 62 relatives to hand-designed ansätze (0.76 ± 0.03, complexity 82), evolutionary search (0.73 ± 0.04, complexity 97), and random sampling (0.69 ± 0.05, complexity 88). Screening efficiency increases through a higher screening-to-top-kkk yield, reducing wasted training runs attributable to infeasible connectivity or stalled optimization. Ablation results indicate that constraint masking is the primary driver of feasibility and candidate yield, while conditioning improves task alignment and preference weighting reduces performance variance by concentrating sampling probability on high-potential motifs. Overall, the findings demonstrate that diffusion-guided generation can serve as a scalable synthesis mechanism for producing deployable PQCs that improve hybrid learning performance without inflating circuit depth or two-qubit gate counts.