zero-shot-quantum-nas

name	zero-shot-quantum-nas
description	Zero-shot Quantum Neural Architecture Search methodology for VQA circuit optimization without classical search loop. Use when: (1) designing variational quantum circuits, (2) optimizing quantum architecture without expensive search, (3) reducing classical overhead in VQA, (4) NISQ-era algorithm design, (5) quantum machine learning circuit selection.

Zero-shot Quantum Neural Architecture Search

Core Idea

Replace classical architecture search loops with a zero-shot approach that evaluates quantum circuit expressibility and trainability analytically, eliminating the need for costly iterative evaluation on quantum hardware.

Methodology

Step 1: Expressibility-Trainability Analysis

Evaluate candidate VQA circuits using:

• Gradient variance as trainability proxy (low variance = barren plateau)
• State space coverage as expressibility measure
• Fisher information for parameter sensitivity

Step 2: Analytical Circuit Ranking

Rank architectures without execution:

1. Compute expressibility via Haar measure distance
2. Estimate trainability via gradient norm distribution
3. Filter out circuits in barren plateau regime
4. Select Pareto-optimal expressibility-trainability tradeoff

Step 3: Hardware-Aware Selection

Match selected architecture to target hardware:

• Gate depth vs. coherence time
• Connectivity requirements vs. hardware topology
• Native gate set compatibility

Activation Keywords

• zero-shot quantum architecture search
• quantum NAS
• VQA circuit design
• variational circuit optimization
• quantum architecture without search
• 零样本量子架构搜索
• 量子神经架构搜索

Error Handling

• If gradient estimation fails: use parameter-shift rule instead of finite difference
• If hardware constraints reject architecture: fall back to next Pareto-optimal candidate

References

• arXiv:2605.27410 - Zero-shot Quantum Neural Architecture Search