Quantum Leap?
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I recently hosted a fireside chat at the Excellence in Energy conference. The topic was, flavor of the year, AI.
One of the questions from the audience cited Google’s Willow chip and asked whether that has potential to impact AI. If you’ve not been following along, Google recently announced a new quantum chip that offers an advance in error management. Since errors are one of the larger roadblocks to scaling up quantum to practical workloads any advances here are welcome.
This is a fascinating question. Here’s my take:
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Quantum computing still has a long way to go from the lab to regular users, particularly in adapting it to workloads that ‘matter’. It’s a completely different programing paradigm and only suited to particular types of problems. We can’t just deploy a python script onto a quantum chip and see it run faster.
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When quantum computing goes mainstream, it will likely be embedded in products that hide that complexity from most developers. This is not new: most developers interacting with OpenAI have no idea how they would build the LLM themselves. That’s the power of a good abstraction.
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As I’ve wrote last month, evolution shows that AI is possible at far lower power draw than current techniques. We’re missing multiple technical breakthroughs to drive down power demand.
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Quantum computing has already shown that it is capable of generating results that would take classical computers many years to replicate. Many years = power, therefore, in a sense, quantum computing shows us a far more power efficient way of computing.
So, it’s a reasonable bet that current complex AI approaches will be adapted to work on quantum platforms, driven by economic forces of lowered power consumption and cost.
As a coda: back in 1989, Penrose published “The Emperor’s New Mind” which posited that human consciousness is non algorithmic, instead a result of quantum effects. It’s fascinating to me that if we do implement AI on quantum computing we’re not disproving that hypothesis.