Beyond ChatGPT: The next quantum leap for AI – The Financial Express
The Financial Express
By Ravi Singh
AI models like ChatGPT are impressive but they are still like race cars limited by narrow roads. In fact, they might not be as ground breaking as initially expected. While Generative AI can perform tasks like summarizing documents, writing lyrics and even singing in Eminem’s voice, it is still a long way from solving highly complex problems. Superintelligence, where AI surpasses human brilliance, still seems implausible. Convergence of AI with quantum computing promises to widen those roads into superhighways, enabling AI to tackle complex problems with ease. It is like AI on steroids.
Inventions such as microscopes and telescopes were revolutionary not just because of their novel technology but also because they acted as catalysts for further inventions. Similarly, quantum computing may act as an enabler for AI to reach the next level of disruption. AI models are based on Machine Learning using which they train themselves by sifting through large amounts of data, learn from it and make predictions and take decisions. However, these ML algorithms are limited by the available computational power. The current computational model, also called classical computing, is likely to hit its full capacity soon as transistors used in them approach the size of a single atom.
In classical computing, we use bits, which are like tiny switches that can be either in the “on” state (1) or the “off” state (0). These bits are the basic units of information. In quantum computing, we use qubits. Unlike bits, qubits can exist in multiple states at the same time, thanks to a phenomenon called superposition. This means a qubit can be both 0 and 1 simultaneously, allowing quantum computers to perform multiple calculations at once. As a result, quantum computers can solve highly complex problems which are beyond the reach of classical computers. Quantum Machine Learning (QML) leverages quantum algorithms to expedite machine learning tasks.
While QML is still in its early stages of development, it holds great promise for a wide range of practical applications. Modelling the behaviour of molecules and simulating chemical reactions is computationally intensive. QML can accelerate these simulations, leading to faster drug discovery and the development of new pharmaceuticals. Quantum computers can also help predict the properties of materials at the quantum level with high accuracy. This is valuable for designing new materials for applications in electronics and energy storage.
Many real-world problems involve finding the best solution among a vast number of possibilities. QML can be used to optimize complex systems, such as logistics and supply chain management, financial portfolios, and transportation networks, more efficiently than classical methods. It can also speed up the training of machine learning models, making it possible to train larger and more complex models in less time. This can lead to improvements in various machine learning applications, including natural language processing, image recognition, and recommendation systems.
Climate modelling requires extensive computational power to simulate the Earth’s climate accurately. QML can provide faster and more detailed climate models, aiding in climate change research and prediction. They can also optimize trading strategies and risk assessment models in the financial industry. They can be used for pricing complex financial derivatives more accurately.
QML can be applied to medical image analysis, genomics, and personalized medicine. It can help identify patterns in patient data to improve diagnosis and treatment plans. Quantum computers can enhance the processing of natural language, making it possible to develop more advanced language translation, sentiment analysis, and chatbot systems. Quantum computing can also assist in analysing large datasets from space telescopes and simulations of celestial bodies, contributing to our understanding of the universe.
Nonetheless, with these remarkable opportunities come substantial challenges and potential threats. Perhaps the most prominent concern is the potential for Quantum Computing to disrupt current encryption methods, posing severe privacy and security risks in the process. This raises ethical questions about who should have access to this technology and how its use should be regulated, especially in the context of decoding encrypted data.
Furthermore, the resource demands for Quantum Computing are substantial. Quantum systems must operate in extremely cold environments close to absolute zero temperature, necessitating significant energy, which makes them expensive and challenging to scale. This could result in unequal access.
The National Quantum Mission, initiated by the Government of India, aims to accelerate quantum technology across the nation. This mission targets the development of intermediate-scale quantum computers with 50-1000 physical qubits in various platforms like superconducting and photonic technology within 8 years. This initiative could position India to play a pivotal role in combining AI with Quantum Computing to address complex problems in sectors such as healthcare, education, and logistics.
The convergence of AI and quantum computing stands at the forefront of technology, promising to unlock new possibilities that were previously out of reach. From speeding up machine learning to revolutionizing scientific research, this convergence has the potential to reshape industries and enhance our quality of life.
As we venture further into this exciting intersection of AI and quantum computing, it’s imperative to prioritize responsible development and ethical considerations. With the right approach, we can harness the potential of these technologies for the betterment of society, ushering in a new era of innovation and discovery.
The author is writer, IRS officer
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