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Google Paves Way for Earlier Access to Useful Quantum ComputingGoogle Paves Way for Earlier Access to Useful Quantum Computing

New Willow quantum chip reduces errors better as more qubits are added

Berenice Baker, Editor, Enter Quantum, co-editor AI Business

December 12, 2024

2 Min Read
Google's new Willow chip
Google

Google has released its latest quantum computing chip, Willow, which boasts error correction and performance that could make useful, large-scale quantum computers possible sooner than expected.

In tests, Willow performed a standard benchmark computation in less than five minutes that would take one of today’s fastest classical supercomputers 10 septillion (1025) years, more than the age of the Universe.

Willow has just 105 qubits compared with IBM’s latest quantum chip, Condor, which has 1,121. But Willow’s architecture promises to break the error correction barrier that troubles scaling up quantum – it reduces errors as more qubits are added.

This exponential reduction in errors is known as "below threshold" error correction, a milestone sought since 1995 that is considered a crucial step toward building practical, large-scale quantum computers.

Willow also improves other performance metrics, such as T1 times, which measure how long qubits can hold an excitation. Also known as longer coherence time, this property is essential for advancing quantum computing because it enhances the system's ability to maintain quantum states over time, improving reliability and scalability.

The next goal for Google is to demonstrate a “useful, beyond-classical” computation on Willow that has real-world applications. While previous experiments have focused on benchmarking against classical computers or simulating quantum systems, the aim is to tackle problems that are both beyond the capabilities of classical computers and relevant to practical use cases.

Related:New York Leverages AI for City Services, Security: Keynote at AI Summit New York

Google believes that quantum computing will be crucial for advancing AI as it could collect training data that is inaccessible to classical machines to train and optimize learning architectures and model systems where quantum effects are important.

This could help advance the development of new medicines, design more efficient batteries for electric cars and accelerate progress in fusion and other new energy alternatives in ways that aren't feasible on classical computers.

About the Author

Berenice Baker

Editor, Enter Quantum, co-editor AI Business, Informa TechTarget

Berenice is the editor of Enter Quantum and co-editor of AI Business. She has over 20 years of experience as a technology journalist, having previously worked at The Engineer and Global Defence Technology.

Before that, she worked as an IT consultant, fuelling her passion for technology and innovation. She graduated with one of the country's first-ever IT degrees so long ago it coincided with Tim Berners-Lee inventing the World Wide Web.

Berenice lives in north London with her cat Huxley. In her spare time, she enjoys going to music gigs, museums and galleries, dabbling in art and playing guitar (badly).

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