parameter-efficient-quantum-mtl

name	parameter-efficient-quantum-mtl
description	Parameter-efficient Quantum Multi-task Learning (QMTL) methodology. Replaces conventional task-specific linear heads with fully quantum prediction heads in hybrid architectures. Quantum head parameters scale linearly with task count vs quadratic for classical heads. Use when designing multi-task learning systems for medical imaging, NLP, or multimodal tasks with constrained parameter budgets.

Parameter-Efficient Quantum Multi-task Learning

Description

Framework for parameter-efficient quantum multi-task learning (QMTL) that replaces conventional task-specific linear heads with fully quantum prediction heads in a hybrid architecture. Demonstrates that quantum head parameter cost scales linearly with task count, while classical head cost scales quadratically. Evaluated on medical imaging, NLP, and multimodal benchmarks (arXiv:2604.13560).

Activation Keywords

• quantum multi-task learning
• 量子多任务学习
• parameter-efficient quantum
• QMTL
• quantum prediction head
• quantum MTL
• variational quantum multi-task

Core Concepts

The Problem: Parameter Explosion in Multi-task Learning

In standard hard-parameter-sharing MTL:

• Shared backbone: processes input → shared representation
• Task-specific heads: map shared representation → task predictions
• Problem: Task-specific parameters grow quadratically O(d × T) with tasks T and representation dimension d

The QMTL Solution

Replace task-specific linear heads with a hybrid quantum-classical architecture:

1. Shared VQC encoding: maps classical data to Hilbert space (task-independent)
2. Task-specific ansatz blocks: lightweight quantum blocks for localized adaptation
3. Linear scaling: quantum head parameters scale as O(T), not O(d × T)