arXiv:2408.03085v3 Announce Type: replace-cross Abstract: As the most central and computationally intensive component of deep neural networks, the execution efficiency of matrix multiplication directly determines the training and inference performance of models. Harnessing the parallel processing capabilities afforded by quantum superposition and entanglement to reshape matrix multiplication implementations has become a promising entry point for optimising underlying quantum arithmetic logic and improving the operational efficiency of quantum circuits. This paper proposes a universal quantum m
The continuous drive for more efficient AI computation, coupled with advancements in quantum computing research, makes the exploration of quantum algorithms for core AI operations like matrix multiplication timely.
Improving the execution efficiency of matrix multiplication, a fundamental component of deep neural networks, through quantum methods could dramatically enhance AI model training and inference capabilities.
This research suggests a potential pathway to significantly faster and more energy-efficient AI computations in the future, if quantum computers can scale and reliably execute complex algorithms.
- · Quantum computing hardware developers
- · AI research labs
- · High-performance computing sector
- · Semiconductor industry (long term)
- · Traditional high-performance CPU/GPU manufacturers (if quantum fully scales)
- · AI models constrained by classical compute
- · Energy-intensive data centers (potentially, long term)
More efficient matrix multiplication could accelerate deep learning model development and deployment.
Quantum advantage in AI computations could drive further investment and innovation in quantum hardware and software.
A fully realized quantum AI could potentially achieve capabilities beyond what is feasible with classical computing, leading to new scientific discoveries and technological paradigms.
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Read at arXiv cs.LG