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compositional-quantum-heuristics

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.

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UpdatedJune 4, 2026 at 02:00

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compositional-quantum-heuristics

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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

Compositional Assembly: Build large quantum models from smaller, trainable subcomponents.
Group-Invariant Loss Functions: Construct loss functions invariant under group actions, introducing symmetry-induced inductive bias for improved gradient behavior and generalization.
Permutation-Equivariant QGNNs: Design quantum graph neural networks that respect graph permutation symmetry for max-clique detection.
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

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hiyenwong

hiyenwong/ai_collection

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name

compositional-quantum-heuristics

description

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

Compositional Assembly: Build large quantum models from smaller, trainable subcomponents.
Group-Invariant Loss Functions: Construct loss functions invariant under group actions, introducing symmetry-induced inductive bias for improved gradient behavior and generalization.
Permutation-Equivariant QGNNs: Design quantum graph neural networks that respect graph permutation symmetry for max-clique detection.
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