compositional-quantum-heuristics

name

compositional-quantum-heuristics

description

Compositional quantum heuristics for mitigating barren plateaus in quantum machine learning. Assembles larger quantum models from smaller subcomponents with group-invariant loss functions introducing symmetry-induced inductive bias for improved gradient behavior. Use when: barren plateau mitigation, quantum graph neural networks, permutation-equivariant quantum models, recursive quantum-classical hybrid optimization, QIRO-inspired quantum heuristics, max-clique quantum detection, group-invariant quantum loss functions, symmetry-induced quantum inductive bias. Triggered by: compositional quantum circuits, barren plateau quantum ML, quantum graph neural network, permutation-equivariant QGNN, group-invariant loss quantum, recursive quantum optimization, QIRO quantum informed recursive optimization, max-clique quantum detection.

Compositional Quantum Heuristics

Mitigating barren plateaus by assembling larger quantum models from smaller subcomponents with symmetry-induced inductive bias.

Paper

arXiv: 2605.07611v1 — Compositional Quantum Heuristics for Max-Clique Detection by Tiffany Duneau, Colin Krawchuk, Anna Pearson (May 2026).

Core Approach

1. Compositional Assembly: Build large quantum models from smaller, trainable subcomponents.
2. Group-Invariant Loss Functions: Construct loss functions invariant under group actions, introducing symmetry-induced inductive bias for improved gradient behavior and generalization.
3. Permutation-Equivariant QGNNs: Design quantum graph neural networks that respect graph permutation symmetry for max-clique detection.
4. Recursive Hybrid Heuristic: Use trained quantum models to guide classical search, inspired by QIRO (Quantum-Informed Recursive Optimization).

Key Results

• Superior training gradients through symmetry-induced bias
• Generalization to larger, more complex problem instances
• Improved inference accuracy and scalability via recursive hybrid quantum-classical procedure
• Viable pathway to scalable quantum learning models that remain hard to simulate classically

When to Use

• Quantum ML model design suffering from barren plateaus
• Graph optimization problems (max-clique, max-cut, etc.)
• Building trainable quantum circuits with expressivity
• Hybrid quantum-classical recursive optimization workflows