Superpositional Gradient Descent: Harnessing Quantum Principles for Model Training
Ahmet Erdem Pamuk, Emir Kaan Özdemir, Şuayp Talha Kocabay
2025-11-14
Summary
This paper explores a new way to train large language models, the kind powering things like chatbots, by borrowing ideas from quantum physics. It investigates if using concepts from quantum mechanics can make the training process better than current standard methods.
What's the problem?
Currently, large language models are trained using optimizers like AdamW, which work well but can still be slow and sometimes don't find the absolute best solution. We don't fully understand *why* these methods work, and there's a potential to improve them. The paper asks if we can improve training by incorporating principles from quantum computing, something that hasn't been thoroughly investigated.
What's the solution?
The researchers developed a new optimizer called Superpositional Gradient Descent, or SGD. This method uses the idea of 'quantum superposition' – think of a bit being both 0 and 1 at the same time – to slightly change the way the model learns. They built this into existing software like PyTorch and Qiskit, which are tools used for machine learning and quantum computing. They then tested it on both simple tasks and on actually fine-tuning large language models, comparing it to the standard AdamW optimizer.
Why it matters?
This research is important because it suggests that quantum computing concepts could be practically used to improve how we train AI models. While there are still challenges with making this work on a large scale due to the limitations of current quantum computers, it opens up a new avenue for making AI training faster and more efficient, potentially leading to better AI systems in the future.
Abstract
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.