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DTSTART:19700308T020000
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DTSTAMP:20260730T152729Z
LOCATION:Mtg Room 201B
DTSTART;TZID=America/Los_Angeles:20260729T143400
DTEND;TZID=America/Los_Angeles:20260729T144700
UID:dac_DAC 2026_sess133_RESEARCH1966@linklings.com
SUMMARY:A Hamiltonian-Guided Pre-Trainer for Variational Quantum Algorithm
 s
DESCRIPTION:Taotao Zhao and He Li (Southeast University)\n\nThe optimizati
 on of variational quantum algorithms (VQAs) is notoriously challenging due
  to poor parameter initialization, which often traps optimizers in subopti
 mal local minima. Existing methods rely on a static guessing paradigm that
  is fundamentally limited. This paper presents a Hamiltonian-guided pre-tr
 ainer (HGP), a new approach that dynamically constructs a better starting 
 point. HGP iteratively refines parameters by performing exact global optim
 ization within low-dimensional subspaces. These subspaces are identified u
 sing a Hamiltonian-guided parameter blocking strategy, and the optimizatio
 n is achieved by reconstructing the analytic landscape from a few quantum 
 measurements via a Fast Fourier Transform. We evaluated HGP on canonical s
 pin models, where it consistently produced superior starting points for st
 andard optimizers. Ablation studies reveal Hamiltonian-guided parameter bl
 ocking reduces the initial energy error by nearly 30-fold versus the next 
 best benchmark. These results highlight the importance of Hamiltonian guid
 ed pre-training  for enhancing VQA performance.\n\nTopics: Design, Quantum
 \n\nTrack: DES6. Quantum Computing\n\nSession Chairs: Zichang He (Global T
 echnology Applied Research, JPMorgan Chase) and Hafiz  Mohammad Hasan Babu
  (University of Dhaka)\n\n
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