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Supercharging AI With the Power of Quantum Computing
How can we supercharge artificial intelligence? Through the power of quantum computing and its potential to pave the way for a more sustainable and efficient future with AI.
3 Min Read
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Artificial intelligence has become an integral part of our lives, powering many things from voice assistants to self-driving cars. And while AI has been transformational across many functions, its rampant growth has sparked conversations around how model training and deploying can affect sustainability.
At our company, we’re studying this sustainability issue closely as we undertake our own rapid rollout and scaling of AI and help clients do the same. Our initial findings have shown that AI’s biggest contribution to increasing emissions will likely come from its routine use. While the energy to train models has been a primary focus, for corporate users, model inferencing, the process of user prompting and model responses, will likely have a greater impact.
That’s where quantum computing could help. Through its ability to speed up training AI models, improve processes, and analyze data more effectively, quantum computing has the potential to make AI more efficient and be one of the latest innovations that could help pave the way for a more sustainable and efficient future with AI.
Harnessing the Potential of AI and Quantum Computing Together
By using quantum physics and quantum computing to operate AI systems, we can perform complex calculations significantly faster than traditional computational techniques possibly at a fraction of the energy consumption.
The ability for quantum computing to execute certain algorithms with fewer steps can result in substantial energy savings, particularly for AI applications with high computational demands. Because of this, quantum computing has the potential to transform AI in multiple ways:
Improved optimization: Quantum computers can perform complex calculations and solve optimization problems much faster than classical computers. This can help AI systems find optimal solutions more efficiently, such as improving the parameters of deep learning models or finding the most effective strategies in reinforcement learning.
Enhanced machine learning algorithms: Quantum computing can enable the development of new machine learning algorithms that are specifically designed to take advantage of quantum properties. These algorithms can potentially provide more accurate results and faster training times compared to classical machine learning algorithms.
Faster data analysis: Quantum computers can process and analyze large datasets more quickly, allowing AI systems to make faster decisions and predictions. This can lead to AI models that require fewer resources and are more sustainable.
Efficient simulation: Quantum computers can simulate physical systems more accurately and efficiently than classical computers, which can benefit AI applications that require simulations, such as drug discovery, material science, and climate modeling. This enables researchers to explore a much larger space of possibilities and make more informed decisions.
Enhanced cryptography: Quantum computers can potentially break many of the current cryptographic techniques that secure data and communications. However, quantum computing can also offer new cryptographic methods that are more secure, making AI systems more resilient to attacks and keeping the privacy and integrity of data.
As AI evolves, the energy required to train and maintain it will only increase, making the need for enhanced efficiency even more important. Quantum computing is still in its infancy, and practical, large-scale quantum computers are not yet widely available. However, as quantum technology matures and becomes more accessible, it will play a pivotal role in making AI more sustainable.
Embracing this integration is not only a step toward a more efficient AI landscape, but it can also help companies be better equipped to achieve their sustainability goals and targets, ultimately driving business value and reducing risk. While it may take time for this integration to materialize, I encourage leaders to prioritize investing in research and development now to start exploring with the technology, as well as help your people develop the skills needed to take this next jump into the digital future. It will be worth the wait.
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