How will AI and quantum computing work together?

In this interview, Claudine Ogilvie, former CIO at Jetstar and CEO HivePix & ADAPT Advisor, explored the intersection of AI and quantum computing.

At Data & AI Edge, Claudine highlighted that while generative AI has made notable advances, its progress is hindered by the limitations of classical computing.

Quantum computing, with its capacity to manage extensive data and solve intricate problems rapidly, holds the potential to elevate AI’s capabilities significantly.

Claudine explained that quantum computers leverage qubits that can exist in multiple states at once, allowing for superposition and entanglement, which enable the swift resolution of complex problems.

However, scaling quantum computers presents substantial challenges due to issues like decoherence and the necessity for extensive cryogenic infrastructure.

Currently, quantum computers are in a noisy, intermediate stage, prone to errors and requiring advanced cooling systems.

Despite these hurdles, rapid advancements are anticipated, and significant breakthroughs could occur within the next five years.

Claudine revealed that quantum computing could revolutionise various fields, including drug discovery, climate modelling, and financial optimisation.

As these technologies mature, they are expected to integrate with classical computing, forming hybrid systems capable of addressing some of humanity’s most complex issues.

The discussion also examined the future landscape of quantum computing, predicting that initial advancements will result in a few dominant players, similar to the AI industry.

These players are likely to provide quantum computing as a service due to the high complexity and cost of building quantum computers.

National security concerns and the competitive race to develop quantum computers is also a critical factor.

There is optimism about achieving widespread access and reduced costs within 10-15 years. Quantum computing, driven by its potential to disrupt industries can address both significant threats and opportunities.

Key Takeaways:
Generative AI has made notable advances but is limited by classical computing, highlighting the need for quantum computing to enhance AI capabilities significantly.

Quantum computers leverage qubits capable of existing in multiple states simultaneously, enabling superposition and entanglement, which facilitate the rapid resolution of complex problems.

While scaling quantum computers presents challenges like decoherence and cryogenic infrastructure requirements, rapid advancements are expected within the next five years, potentially revolutionising fields such as drug discovery, climate modelling, and financial optimisation.

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