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Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver
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AI
DOI：10.1039/D5DD00138B.png)
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Abstract
En 中文
Quantum computing is entering a transformative phase with the emergence of logical quantum processors; which hold the potential to tackle complex problems beyond classical capabilities. While significant progress has been made; applying quantum algorithms to real-world problems remains challenging. Hybrid quantum-classical techniques have been explored to bridge this gap; but they often face limitations in expressiveness; trainability; or scalability. In this work; we introduce conditional Generative Quantum Eigensolver (conditional-GQE); a context-aware quantum circuit generator powered by an encoder–decoder transformer. Focusing on combinatorial optimization; we train our generator for solving problems with up to 10 qubits; exhibiting nearly perfect performance on new problems. By leveraging the high expressiveness and flexibility of classical generative models; along with an efficient preference-based training scheme; conditional-GQE provides a generalizable and scalable framework for quantum circuit generation. Our approach advances hybrid quantum-classical computing and contributes to accelerate the transition toward fault-tolerant quantum computing.
Keywords:
quantum computing
hybrid quantum-classical algorithms
generative models
combinatorial optimization
quantum circuit generation
Journal
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5.6
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981
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