Following the Nobel Prize in Physics awarded to our own Michel Devoret alongside John Clarke, & John Martinis, Nature Magazine opened access to the full collection of their work which laid the foundation for modern superconducting qubit-based quantum computing.
About us
From conducting fundamental research to influencing product development, our research teams have the opportunity to impact technology used by billions of people every day. We aspire to make discoveries that impact everyone, and sharing our research and tools to fuel progress in the field is fundamental to our approach.
- Website
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https://research.google/
External link for Google Research
- Industry
- Technology, Information and Internet
- Company size
- 1,001-5,000 employees
Updates
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Today on the blog learn about XR Blocks, an open source framework to author #XR + #AI applications on #AndroidXR with WebXR →https://goo.gle/476sYWD You can also check out over 20 live demos at xrblocks.github.io!
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We are excited to share that FireSat is one of TIME's Best Inventions of 2025! A collaboration between the Earth Fire Alliance, Muon Space & Google Research, it’s an AI-powered satellite constellation designed for high-resolution, early wildfire detection. Read the TIME article →https://goo.gle/4n7JjjW Learn more about FireSat →https://goo.gle/3Ws5SVo
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Our latest research in quantum computing introduces the use of "color codes" for quantum error correction. This method demonstrates a more resource-efficient approach compared to the surface code, requiring fewer physical qubits and enabling faster logical gate operations → https://goo.gle/3JGcnRq
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Today's common voice search technologies are focused on the question, "What words were said?" What if we could answer a more powerful question: "What information is being sought?" Introducing the new Speech-to-Retrieval (S2R) model in today’s blog →https://goo.gle/4qepWZc
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Ever had a perfect image in mind that a text-to-image model just couldn't capture? Our new reinforcement learning agent, PASTA, turns image generation into a collaborative conversation, learning your style to bring your vision to life. It works with you to progressively refine images, eliminating the need for endless trial-and-error prompt refinement to reach a desirable image. Learn how it works: goo.gle/486MhRL
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The key to elevating a good photo often lies in selective image adjustments: brightening a subject in the foreground, enhancing the sky, or making the color of a jacket pop. Yet, isolating specific elements with existing tools can be tricky, especially on mobile devices. Now, we have made object-based image adjustments quick and easy with the new Object Brush feature in Snapseed. This tool lets you edit objects intuitively, allowing you to simply draw a stroke on the object you want to edit and then adjust how you want it to look, separate from the rest of the image. Learn about the technology behind this new feature at: https://goo.gle/46Shwhe
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Learn about our research prototype LLM-powered personal health agent that analyzes various data modalities, including data from wearable devices, to offer evidence-based health insights and to provide a personalized coaching experience. Read more →https://goo.gle/4nRugeM
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In today’s Google Research Blog post, we demonstrate how an LLM-powered coding agent can help discover new mathematical structures that push the boundaries of our understanding of complexity theory (a sub-field of theoretical computer science). We use AlphaEvolve, a system developed by Google DeepMind for iterative code evolution. Starting with populations of code snippets, AlphaEvolve evaluated the structures produced by them, and used an LLM to morph the most successful snippets toward better solutions. This approach led to two new results in complexity theory: 1) improving the state-of-the-art for the limit on our ability to approximate the outcome of the Maximum Cut problem for 4 slices, and 2) tightening the bounds on the average-case hardness of certifying properties of random graphs. Importantly, these improved proof elements can be automatically verified by a computer program. Read all about it at: https://goo.gle/3WhbCRI
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