Superpositional Gradient Descent: Harnessing Quantum Principles for
  Model Training
Large language models (LLMs) are increasingly trained with classical
optimization techniques like AdamW to improve convergence and generalization.
However, the mechanisms by which quantum-inspired methods enhance classical
training remain underexplored. We introduce Superpositional Gradient Descent
(SGD), a novel optimizer linking gradient updates with quantum superposition by
injecting quantum circuit perturbations. We present a mathematical framework
and implement hybrid quantum-classical circuits in PyTorch and Qiskit. On
synthetic sequence classification and large-scale LLM fine-tuning, SGD
converges faster and yields lower final loss than AdamW. Despite promising
results, scalability and hardware constraints limit adoption. Overall, this
work provides new insights into the intersection of quantum computing and deep
learning, suggesting practical pathways for leveraging quantum principles to
control and enhance model behavior.
690ae7b8d70e173c8452907b
Ahmet Erdem Pamuk
claimed_verified
2025-11-10T09:32:19.680Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/66a607f8d187b6539c2be8df/ESa2EVg0_pnCWgeQd6_bq.jpeg
Ahmet Erdem Pamuk
ahmeterdempmk
user
ahmeterdempmk
690ae7b8d70e173c8452907c
Emir Kaan Özdemir
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2025-11-10T21:03:17.117Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ARG1zztkWM3P1rO0oZZd8.png
Emir Kaan Özdemir
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emirkaanozdemr
690ae7b8d70e173c8452907d
Şuayp Talha Kocabay
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2025-11-05T16:24:26.781Z
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Şuayp Talha Kocabay
suayptalha
user
suayptalha
Superpositional Gradient Descent: Harnessing Quantum Principles for
  Model Training
Large language models (LLMs) are increasingly trained with classical
optimization techniques like AdamW to improve convergence and generalization.
However, the mechanisms by which quantum-inspired methods enhance classical
training remain underexplored. We introduce Superpositional Gradient Descent
(SGD), a novel optimizer linking gradient updates with quantum superposition by
injecting quantum circuit perturbations. We present a mathematical framework
and implement hybrid quantum-classical circuits in PyTorch and Qiskit. On
synthetic sequence classification and large-scale LLM fine-tuning, SGD
converges faster and yields lower final loss than AdamW. Despite promising
results, scalability and hardware constraints limit adoption. Overall, this
work provides new insights into the intersection of quantum computing and deep
learning, suggesting practical pathways for leveraging quantum principles to
control and enhance model behavior.
Superpositional Gradient Descent, a quantum-inspired optimizer, improves convergence and reduces final loss in large language model training compared to AdamW.
Superpositional Gradient Descent SGD quantum superposition quantum circuit perturbations hybrid quantum-classical circuits PyTorch Qiskit sequence classification large-scale LLM fine-tuning