Superpositional Gradient Descent Achieves Faster Convergence and Lower Loss Than AdamW in Large Language Model Training

The challenge of training increasingly complex artificial intelligence models demands ever more efficient optimisation techniques, and researchers are now exploring the potential of quantum-inspired methods to accelerate this process. Ahmet Erdem Pamuk, Emir Kaan Özdemir, and Şuayp Talha Kocabay, working independently, present a novel approach called Superpositional Gradient Descent, which links the updating of model parameters with the quantum principle of superposition through carefully designed circuit perturbations. This work establishes a new mathematical framework and implements it within standard deep learning tools, demonstrating that Superpositional Gradient Descent not only converges faster than established methods like AdamW, but also achieves lower final error rates on both synthetic tasks and large language model fine-tuning. By successfully bridging quantum computing and deep learning, this research offers a promising pathway towards harnessing quantum principles to improve the performance and efficiency of artificial intelligence.

Easily trained with classical optimisation techniques like AdamW to improve convergence and generalisation, quantum-inspired methods nonetheless leave unexplored the mechanisms by which they enhance classical training. They present a mathematical framework and implement hybrid quantum-classical circuits in PyTorch and Qiskit. On synthetic sequence classification and large-scale language model fine-tuning, SGD converges faster and yields lower final loss than AdamW. Despite promising results, scalability and hardware constraints currently limit adoption.

Superpositional Optimizers and Quantum Transformer Details

This research presents a comprehensive investigation into quantum-inspired optimisation techniques for machine learning. The study details a novel approach, demonstrating a strong understanding of both quantum computing and deep learning principles. The research also introduces a Quantum Transformer architecture, incorporating a parameterised quantum circuit into the attention mechanism. Comprehensive experiments were conducted on classification tasks and large-scale language model fine-tuning, showcasing the versatility of the proposed approach.

However, demonstrating performance improvements with metrics like accuracy, F1-score, perplexity, or BLEU score, and comparing these results to baseline methods, is crucial. Conducting ablation studies to understand the contribution of each component, such as quantum circuit depth or the number of qubits, would further strengthen the findings. Addressing the scalability of the approach, particularly the computational cost of quantum simulation, is also important. Exploring error mitigation techniques used in the quantum simulations would enhance the robustness of the results. Providing theoretical justification for the observed improvements, potentially linking the approach to existing quantum machine learning algorithms, would add further depth.

A clear discussion of the limitations of the proposed approach and potential future research directions would also be valuable. The research represents a promising step towards integrating quantum concepts into machine learning, with the potential to lead to more efficient and accurate models. This work introduces quantum-inspired perturbations into the parameter update process, aiming to improve exploration of the loss landscape and achieve faster convergence. The team implemented hybrid quantum-classical circuits using PyTorch and Qiskit, demonstrating a practical pathway for leveraging quantum concepts within existing deep learning frameworks. Experiments reveal that SGD consistently converges faster and achieves lower final loss compared to the widely used AdamW optimiser.

This improvement stems from the ability of quantum-inspired perturbations to help the optimisation process escape poor local minima and identify better solutions within the complex, high-dimensional parameter space of large language models. The research demonstrates that SGD effectively explores the loss landscape, leading to enhanced model performance across various tasks. Measurements confirm that the implemented hybrid quantum-classical circuits function as intended, seamlessly integrating quantum computations with classical neural network training. The team successfully tested the approach on synthetic sequence classification tasks and large-scale language model fine-tuning, consistently observing improvements in both convergence speed and final model accuracy. Specifically, the results demonstrate a significant reduction in the number of iterations required to reach a desired level of performance, indicating a more efficient optimisation process. Researchers successfully demonstrated that by injecting quantum-inspired perturbations into the gradient update process, models converge faster and achieve lower final loss compared to conventional methods like AdamW. This improvement was observed across both synthetic sequence classification tasks and large-scale language model fine-tuning, suggesting the potential for broader applicability. The team attributes this enhanced performance to the ability of quantum-inspired techniques to effectively navigate complex loss landscapes and escape suboptimal local minima by simultaneously exploring multiple parameter configurations.

While the results are promising, the authors acknowledge current limitations related to scalability and the computational demands of simulating quantum circuits. Future research will focus on addressing these challenges by exploring more sophisticated quantum circuit designs and developing implementations suitable for execution on actual quantum processors. This ongoing work aims to unlock the full potential of quantum-inspired optimisation, potentially offering significant benefits for training neural networks even before the widespread availability of large-scale quantum computers. The findings represent a significant step towards bridging the gap between quantum computing and deep learning, paving the way for innovative approaches to model optimisation.