Generative quantum combinatorial optimization by means of a novel conditional generative quantum eigensolver
This work addresses the problem of limited expressiveness and scalability in hybrid quantum-classical computing for combinatorial optimization, representing an incremental advancement.
The paper tackled the challenge of applying quantum algorithms to real-world combinatorial optimization problems by introducing a conditional Generative Quantum Eigensolver, which achieved nearly perfect performance on problems with up to 10 qubits.
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.