Machine Learning Pave The Way For Development of Quantum Computers

  • 10 Nov 2021 09:50
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Machine Learning Pave The Way For Development of Quantum Computers

From graphic processing units to quantum computers, every futuristic innovation relies on the development of machine learning chips. With the ever-increasing volume of data and demand for real-time analysis, AI-supported applications must be equally efficient. The conventional CPUs fell short as they process each task sequentially. On the contrary, if significant performance improvements are made, especially in the context of deep learning, multiple tasks can be performed without latency.

According to analysis, the global machine learning chip market is expected to reach $37.84 billion by 2025, growing at a CAGR of 40.8% from 2018 to 2025. This is because of the emergence of quantum computing, adoption of artificial intelligence (AI), and surge in demand for smart homes & smart cities. 

Machine learning chips open lucrative opportunities

More and more companies and start-up founders are recognizing the opportunities offered by machine learning chips and working on supplementary semiconductor solutions. For instance, Graphcore, a UK-based firm has developed an Intelligence Processing Unit (IPU), which would accelerate machine learning and AI applications. China is currently on top when it comes to developing machine learning chip start-ups. Several companies have acquired billion-dollar funding from their investors to develop a neural network processor chip for smartphones.

On the other hand, neuromorphic chips are on the verge of becoming the next big thing in the machine learning chip industry. The architecture of neuromorphic chips mimics the way the human brain works when it comes to understanding and comprehension. Moreover, it offers a distinctive feature of separation between data memory and processor unit. Such chips were introduced in 2017 and its incorporation has exponentially increased as they showed that their autonomously learning rate is 1 million times better than the third-generation of neural networks. Moreover, they are more energy-efficient than their conventional counterparts.

However, developing machine learning chips that offer improved performance and unprecedented speed is not the main challenge. Offering machine learning chips at an affordable cost is a much bigger task. Thus, market players have focused on developing domain-specific, embedded chips, specially designed to fulfill deep learning requirements. Such embedded hardware can be effective in performing tasks including semantic segmentation and complex object detection. However, today’s high-end GPUs are not completely suitable for AI and machine learning applications, which further boosts the demand for machine learning chips.

Machine learning chips for the future

Developing quantum computers is the future. It is a beacon for taking a quantum leap for AI technology. Thus, the tech giants such as IBM, Microsoft, and Google and autonomous car manufacturing companies have invested big bucks in this technology. As quantum computer can perform every calculation for all states at the same time. This multitasking demands exceptional processing powers. With the use of machine learning chips, quantum computers can operate at much higher speeds . However, the technology is still in its initial stage and there is a long way to go for bringing quantum computers to reality. But the development of machine learning chips is the crucial aspect of the future of quantum computers.


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Emma Ritchie By, Emma Ritchie
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