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