Sequential Hamiltonian Assembly: Enhancing the training of combinatorial optimization problems on quantum computers

• URL: http://arxiv.org/abs/2408.04751v1
• Date: Thu, 8 Aug 2024 20:32:18 GMT
• Title: Sequential Hamiltonian Assembly: Enhancing the training of combinatorial optimization problems on quantum computers
• Authors: Navid Roshani, Jonas Stein, Maximilian Zorn, Michael Kölle, Philipp Altmann, Claudia Linnhoff-Popien,
• Abstract summary: 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. We propose Sequential Hamiltonian Assembly (SHA) to address this issue and facilitate parameter training for quantum applications using global loss functions.
• Score: 4.385485960663339
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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