Quantum End-to-End Learning for Contextual Combinatorial Optimization
This work addresses the challenge of decision-making under uncertainty in combinatorial optimization, offering a quantum approach that reduces parameter count and avoids NP-hard solver calls, though it is incremental as it builds on existing quantum and learning techniques.
The paper introduces QEL, the first quantum end-to-end learning framework for contextual combinatorial optimization, which achieves competitive performance with substantially fewer parameters than classical methods by leveraging quantum approximate optimization algorithms and a context re-uploading phase-separator.
Contextual combinatorial optimization (CCO) plays a critical role in decision-making under uncertainty, yet remains a significant challenge. We present Quantum End-to-End Learning (QEL), the first quantum computing-based end-to-end learning framework for CCO that leverages Quantum Approximate Optimization Algorithms. Inspired by the integration of state preparation and evolution in data re-uploading, we propose a context re-uploading phase-separator that jointly captures the complex relations among contexts, uncertain coefficients, and optimal solutions. This allows a contextual encoder to be seamlessly integrated within a quantum surrogate policy, enabling joint end-to-end training with a stationarity guarantee. Exploiting an optimization-aware structure grounded in physical principles that classical methods cannot readily leverage, our approach demonstrates practicality by directly training on task loss despite the discreteness and nonconvexity, while avoiding calls to NP-hard optimization solvers. QEL empirically achieves competitive performance while requiring substantially fewer parameters than classical benchmarks, highlighting its industrial-level potential for the future quantum era.