The Role of Quantum Computing in Generative AI
These models enable Generative AI to produce diverse content—text, images, audio, and video—based on user prompts, exemplifying the transformative capabilities of this technology.
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Quantum computing merges principles of physics, mathematics, and computer science, harnessing concepts like superposition and quantum interference to perform complex computations far more efficiently than classical computers. While traditional computing relies on bits, quantum computing utilizes qubits, enabling it to tackle intricate problems at unprecedented speeds.
Artificial Intelligence (AI) is a branch of computer science focused on creating machines capable of learning, reasoning, and acting in ways similar to humans. AI applications can recognize objects, understand and respond to human language, learn from experience, and generate detailed recommendations, thus minimizing the need for human intervention.
At the forefront of AI advancements are Large Language Models (LLMs), which are trained on extensive datasets to facilitate natural communication. These models enable Generative AI to produce diverse content—text, images, audio, and video—based on user prompts, exemplifying the transformative capabilities of this technology.
Currently, Generative AI applications rely on traditional hardware like CPUs and GPUs. Intel® Core™ Ultra processors and Intel® Arc™ A-series graphics provide robust platforms for LLM inference. NVIDIA’s GB200 NVL2 platform, featuring the Grace™ Blackwell Superchip, combines powerful GPUs and CPUs to deliver significantly faster real-time inference for LLMs, with configurations scaling up to a 72-GPU system.
The convergence of quantum computing and Generative AI offers exciting possibilities. Quantum computing can dramatically accelerate operations, enhancing deep learning and neural networks to produce unique data samples swiftly. This integration fosters creativity and collaboration while addressing optimization challenges, speeding up learning and prediction processes.
By combining Quantum AI with traditional AI approaches, researchers can effectively leverage the strengths of both. Traditional algorithms can preprocess data and manage outputs from quantum calculations, while quantum circuits can transform classical data into quantum states for advanced information representation and feature extraction.
(The Author Dhaval Mehta is Program Chair, BCA, at Navrachana University)
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