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first_img Google stated that the Willow chip reduces magical state errors by approximately 40 times

The Google Quantum AI team published a paper in Science, announcing that a technique called magic state cultivation reduces errors by about 40 times on the Willow processor, producing logical fidelity of 99.99% for T states. Magic states are one-time resources required to run useful quantum programs, consumed with each use; according to estimates cited by Google, the cost of producing magic states can account for up to 95% of a quantum computer's total capacity, leaving very limited space for actual computation.The previous standard approach was magic state distillation proposed by physicists Sergei Bravyi and Alexei Kitaev in 2004, which required a large number of qubit factories dedicated solely to this task. Google researchers proposed a cheaper cultivation route in 2024, which behaves more like a filter: injecting rough magic states into a small area of the chip, running a series of checks on them, discarding all attempts found to be defective, and allowing the passers to gradually expand. In the experiments, only about 8% of attempts passed all checks, meaning that more than 90% of attempts had to be discarded for each clean state retained.The cultivation technology is one of the supports for Gidney's 2025 estimate that RSA-2048 encryption could be broken by fewer than a million noisy qubits, a reduction of 20 times from the 20 million estimated in 2019. This experiment used up to 54 of the 105 qubits in Willow, with the paper noting that breaking the RSA-2048 key requires magic states with an error rate of about one in ten billion, which is about a million times cleaner than this result.

first_img Google DeepMind's First Audit: AI Did Not Accelerate Its Own Development, 75% of Code Written by AI

Google DeepMind released the first comprehensive empirical assessment of automation in machine learning research and development in the Gemini 4 Argon security report. The conclusion is that AI has not accelerated the R&D of AI itself, despite the fact that AI has written most of the code delivered by the lab's engineers. Researcher Haydn Belfield, who participated in the assessment, stated on X that there is currently no significant end-to-end ML R&D acceleration, so the risks of AI-driven ML R&D are currently low.The team built an internal index, GCI v1, which synthesizes the scores of 123 AI models across 92 benchmarks into a single value. The cutting-edge Gemini capabilities are improving at a stable rate of about 0.1 GCI points per month, with Gemini 1.5 Pro approaching 0.5 points by the end of 2024, Gemini 3.8 Flash slightly above 2.9 points by September 2026, and Argon about 3.3 points above the trend line. However, Belfield noted that this is due to increased computing power and organizational investment, rather than AI-driven acceleration.Over 75% of the code produced by DeepMind is written by AI, and the proportion of fully delegated code merge requests increased from 7.5% to 11.9% between April and September, but about 88% of requests still require multiple rounds of human guidance or manual writing. A survey of 12 senior researchers showed that automation is highest in clearly defined, verifiable execution tasks and lowest in areas such as open science concepts, new experimental designs, and unstable diagnostic training.

first_img Google launched Playground, where you can generate playable games by entering text descriptions

On October 7, Google launched the web platform Playground, where users can describe game ideas in natural language and obtain playable, shareable game versions without writing code. Users can also continue to adjust physical effects, rules, characters, or scenes through dialogue and share their creations via links. This tool is currently open to users over 18 in the United States, with creation permissions tiered according to the user's Google AI subscription level.Games in Playground can run in browsers on mobile phones and laptops. Creators can set their works to private or publish them to the community gallery, and all games must pass a security review, with some categories supporting leaderboards and multiplayer modes. Google refers to it as "an early experiment," and the announcement did not disclose pricing or specify which AI models power the tool.The upgrade path points to Unity Spark, and Google stated that Playground will soon integrate this tool to meet the needs of creators requiring professional-level mechanics and high-fidelity 3D. In a joint statement, Google and Unity announced that Spark will launch later this year, with closed testing starting soon. Previously, DeepMind released the Genie model in 2024, and in January, Google opened Project Genie based on Genie 3 to users of the AI Ultra subscription in the United States.

first_img Google released the Nano Banana 2.1 image model, with the developer call price being about half of the previous generation

Google released its latest image generation and editing model, Nano Banana 2.1, on October 6, and it has been launched in the Gemini app, AI mode of Google Search, Google Ads, as well as developer tools like Google AI Studio, Flow, and Stitch. This model can generate images from text descriptions and edit existing photos.Google stated that this upgrade focuses on three aspects: visual design, mask-based editing (where only the marked area changes), and subject consistency, meaning that characters or objects retain their original features after multiple edits. In the overall preference test for text-to-image generation, Nano Banana 2.1 scored 1050 ELO points, higher than Nano Banana 2's 990 points and Nano Banana Pro's 935 points. The model can handle up to 14 reference images simultaneously, track 4 characters and 10 objects, and output up to 4K resolution with an aspect ratio of up to 8:1. Developers can also set the thinking time before generation and enable fact-checking based on Google Search and Google Image Search.When called through the Google Developer API, the standard 1K image is priced at $0.0336, about half of Nano Banana 2's $0.067; the 4K image is $0.0756, while the previous generation was $0.151, and bulk tasks can enjoy a 50% discount. The above performance data comes from Google's own testing.

first_img Google Vids Free Open 1080p AI Video Generation

On September 23, Google announced that any user with a Google account can generate 1080p HD videos for free in its browser-based video editor Google Vids, accessible at vids.new. This feature is based on the Gemini Omni 1.1 Flash model and can transform text prompts, photos, audio, or existing clips into new videos. Previously, such generative capabilities mostly required a paid subscription to use.Google has also added several editing controls that previously required paid tools: scene extension can lengthen clips while maintaining consistency in lighting, characters, and scenes; users can set precise clip durations to align with scripts or voiceovers; and old AI clips with lower resolutions can be upgraded to 1080p without needing to regenerate. Each video segment generated by this tool embeds Google DeepMind's SynthID invisible watermark, which is not visible to the naked eye but can be read by machines. Users can check whether a segment was generated by AI using the SynthID Detector.The free quota varies by account type: personal Google accounts receive a standard quota, paid AI packages can unlock more, and Workspace Business and Enterprise subscribers receive a larger generation pool and team management controls. Gemini Omni will be launched as a paid feature for Google AI Plus, Pro, and Ultra subscribers in May 2026, with free access previously limited to YouTube Shorts and YouTube Create.
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