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Google's Quantum AI team reveals Willow, a groundbreaking 105-qubit chip that marks the company’s first successful implementation of an error-corrected surface code qubit, pushing quantum supremacy further.
Yesterday, I landed in Santa Clara for the Q2B (Quantum 2 Business) conference, where I’ll be speaking on "Quantum Algorithms in 2024: How Should We Feel?" and closing the event with an Ask-Us-Anything session alongside John Preskill. If you’re attending, feel free to drop by and say hi!
Coinciding with Q2B, Google’s Quantum AI team officially announced their latest achievement: Willow, a new 105-qubit superconducting chip. This announcement is significant for several reasons:
Google's Willow is a major step forward in the field of quantum computing. Here’s a breakdown of what’s changed and why it matters:
These improvements in coherence time and gate fidelity are significant because they directly impact the reliability and performance of quantum circuits.
Google used Willow to perform several impressive demonstrations:

The announcement has garnered significant attention:
Yesterday, I saw numerous comments on Twitter, Hacker News, and other platforms asking for my take on the announcement. For those who have been following quantum computing closely over the past few years, this news is not entirely surprising. Since Google’s original quantum supremacy milestone in 2019, they’ve consistently improved their qubits' quality and quantity.
However, seeing these advancements come to fruition is still incredibly gratifying. It’s a clear sign that we are making steady progress toward practical quantum computing.
Google's Willow represents a significant leap forward in the field of quantum computing. With its enhanced error correction capabilities and increased qubit count, it paves the way for more complex and reliable quantum algorithms. As we move into 2024, these developments will likely continue to shape the landscape of quantum technology.
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↗ https://scottaaronson.blog/?p=8525&utm_source=tldrai
About the author
Kai built ML infrastructure at a Bay Area startup before developing an obsession with transformer architectures and inference optimisation that eventually pulled him out of product work entirely. A stint at a compute research lab sharpened his instinct for what actually matters in a model release versus what is marketing. He writes from the inside — from the perspective of someone who has debugged the systems he is describing at three in the morning. He is allergic to hype and instinctively drawn to the unglamorous plumbing questions that everyone else skips over.
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24 December 2024
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