Sequential Hamiltonian Assembly: Enhancing the Training of Combinatorial Optimization Problems on Quantum Computers

Roshani, Navid; Stein, Jonas ; Zorn, Maximilian; Kölle, Michael; Altmann, Philipp und Linnhoff-Popien, Claudia (April 2025): Sequential Hamiltonian Assembly: Enhancing the Training of Combinatorial Optimization Problems on Quantum Computers. ICAART 2024: 16th International Conference on Agents and Artificial Intelligence, Rome, Italy, 24.- 26. Februar 2024. Rocha, Ana Paula; Steels, Luc und Herik, Jaap van den (Hrsg.): In: Agents and Artificial Intelligence. 16th International Conference, ICAART 2024, Bd. 1 Cham: Springer Nature Switzerland. S. 252-271

Abstract

A central challenge in quantum machine learning is the design and training of parameterized quantum circuits (PQCs). Much like in deep learning, vanishing gradients pose significant obstacles to the trainability of PQCs, arising from various sources. One such source is the presence of non-local loss functions, which require the measurement of a large subset of qubits involved. To address this issue and facilitate parameter training for quantum applications using global loss functions, we propose Sequential Hamiltonian Assembly (SHA). SHA iteratively approximates the loss by assembling it from local components. To further demonstrate the feasibility of our approach, we extend our previous case study by introducing a new partitioning strategy, a new merger between QAOA and SHA, and an evaluation of SHA onto the Max-Cut optimization problem. Simulation results show that SHA outperforms conventional parameter training by 43.89% and the empirical state-of-the-art, Layer-VQE by 29.08% in the mean accuracy for Max-Cut. This paves the way for locality-aware learning techniques, mitigating vanishing gradients for a large class of practically relevant problems.

• Dokumententyp: Konferenzbeitrag (Paper)
• Keywords: Quantum machine learning ; Parameterized quantum circuits ; Combinatorial optimization
• Fakultät: Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen
• Themengebiete: 000 Informatik, Informationswissenschaft, allgemeine Werke > 004 Informatik
• ISBN: 978-3-031-87327-0
• Ort: Cham
• Sprache: Englisch