International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 08 | Aug 2024
p-ISSN: 2395-0072
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Quantum Neuromorphic Computing for Viable and Sustainable Generative AI Ambrish Kumar Balasubramanian1, M.Tech. - Artificial Intelligence and Machine Learning, BITS Pilani. Senior Systems Engineer, Infosys Limited, Chennai, Tamil Nadu, India ---------------------------------------------------------------------***--------------------------------------------------------------------human brain, mimicking neural structures through spiking Abstract - Quantum Neuromorphic Computing, an
neural networks (SNNs). Neuromorphic chips such as Intel's Loihi and IBM's True North implement these networks in hardware, enabling real-time, energy-efficient processing. Unlike traditional digital computing, which relies on binary logic gates, neuromorphic computing uses neurons and synapses that communicate via spikes (electrical impulses), reducing power consumption and latency.
innovative fusion of quantum computing and neuromorphic engineering, holds the promise of revolutionizing generative AI by improving both computational efficiency and sustainability. This paper explores the fundamental principles of quantum neuromorphic computing, its potential to address the growing energy demands of generative AI models and provides a detailed exploration of implementation methodologies. By leveraging quantum mechanical phenomena such as superposition, entanglement, and tunnelling within neuromorphic architectures, this approach aims to reduce the computational burden and power consumption of AI systems. Practical coding examples and visual illustrations are included to aid understanding and stimulate further interdisciplinary research in this transformative field.
2.2. Quantum Computing: Quantum computing leverages the principles of quantum mechanics to perform computations. Quantum bits, or qubits, can exist in a superposition of states (0 and 1 simultaneously) and exhibit entanglement, where the state of one qubit can depend on another regardless of distance. Quantum computers promise exponential speed-ups for tasks such as factoring large numbers, searching unsorted databases, and simulating quantum systems.
Key Words: quantum neuromorphic computing, generative AI models, neuromorphic architectures
2.3. Generative AI Models: Generative AI models, including GANs, VAEs, and LLMs, create new data instances resembling a given dataset. These models are computationally intensive, requiring large-scale parallel processing capabilities, significant memory, and substantial energy resources for both training and inference.
1.INTRODUCTION The rapid advancement of artificial intelligence, particularly generative AI models like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and large language models (LLMs) such as GPT, has led to a dramatic increase in computational requirements. These models, which require enormous amounts of data and computational resources for training and inference, present significant sustainability challenges due to their energy consumption and carbon footprint.
2.4. The Need for Quantum Neuromorphic Computing: Current generative AI models are constrained by the computational and energy limitations of classical computing architectures. Quantum neuromorphic computing combines the speed and parallelism of quantum mechanics with the low-power characteristics of neuromorphic computing. This hybrid approach aims to build AI systems that are both powerful and energy-efficient, addressing the sustainability challenges posed by the next generation of AI models.
To mitigate these challenges, researchers are exploring new computing paradigms beyond traditional transistor-based architectures. Quantum Neuromorphic Computing, a hybrid approach combining quantum computing principles with neuromorphic hardware, offers the potential for both high computational power and energy efficiency. This paper investigates the potential of quantum neuromorphic computing to make generative AI viable and sustainable, proposing specific implementation strategies, highlighting applications, and discussing future directions.
3. QUANTUM NEUROMORPHIC COMPUTING 3.1. Definition and Key Principles: Quantum Neuromorphic Computing integrates quantum mechanical principles with neuromorphic computing architectures. The objective is to use quantum systems to simulate neural networks, leveraging the strengths of both paradigms. Key principles include:
2. BACKGROUND 2.1. Neuromorphic Computing: Neuromorphic computing takes inspiration from the structure and functioning of the
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