Google Uses AI Reinforcement Learning For Quantum Error Correction

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Google Uses AI Reinforcement Learning For Quantum Error Correction

Jeff Burt

Jeff<br>Burt

Published<br>mon 20 Jul 2026 // 15:52 UTC

Nvidia scientists and their counterparts at a range of academic, scientific, and quantum computing institutions late last year published a research paper about the ongoing convergence of AI and quantum computing, and how each will play increasingly important roles in the development of the other.<br>“There is ample intuition to motivate exploring AI as a breakthrough tool for [quantum computing],” the researchers wrote in the paper published in Nature Communications in December. “The inherent nonlinear complexity of quantum mechanical systems makes them well-suited to the high-dimensional pattern recognition capabilities and inherent scalability of existing and emerging AI techniques.”

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This AI-quantum partnership is a central part of the larger classical-quantum hybrid datacenters that will be standard operating environments as quantum computing matures. It manifests itself in myriad ways, such as classical AI handling data cleaning, encoding, and optimization while the quantum system performs calculations.<br>A key area is the use of AI in solving the challenge of error correction in quantum computing. Qubits are notoriously fragile and can essentially fall apart – or decohere – due to environment factors around them, from noise and temperature to light and the activity of other qubits. This can lead to errors, and error-prone quantum systems are neither practical nor useful.<br>AI For QEC<br>A lot of work is being done to see how AI – both generative and agentic – can help address error correction in quantum systems. For example, scientists from the University of Pennsylvania and Hao Tang of Peking University in China addressed the issue in a research paper early last year. Nvidia in April ran out Ising, a family of open source AI models that the vendor said can provide up to 2.5 times faster performance and three times higher accuracy for the decoding process for quantum error correction. Nvidia co-founder and chief executive officer Jensen Huang said that “with Ising, AI becomes the control plane – the operating system of quantum machines – transforming fragile qubits to scalable and reliable quantum-GPU systems.”

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The same month, researchers with Amazon Web Services, Quantum Elements, the University of Southern California, and Harvard University wrote about their use of AI digital twin technology and a cloud-based HPC system to create and run simulation models to advance research into quantum error correction.<br>In another paper, IBM researchers in June outlined a large-language model (LLM)-based framework that can sort through thousands of code variations used to solve the error correction problem to find the right one for a particular situation. It’s “one example of the growing two-way interplay between quantum computing and classical AI, where each is beginning to inform and accelerate the other,” they wrote.<br>This month, a group of researchers led by Volodymyr Sivak, a research scientist with Google Quantum AI, said that one of AI’s features – its ability to learn from experience – could be used to enable quantum systems to run for longer periods of time – days, weeks, or even months – without having to stop their calculations to adjust calibrations when errors are detected.<br>The reinforcement learning (RL) method the researchers detailed in a study published in Nature, illustrated below, uses the encoded information that the quantum computers already collect as they monitor for errors to run them through an AI system that can learn from the errors and make adjustments to the quantum system’s control settings and operating parameters based on what it found.

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Fault-tolerant quantum error correction relies on errors leaving detectable signatures that can be detected, signaling that an error has occurred in a particular region of the circuit. A decoder can then look at the detection events to suss out likely error patterns. After the decoder corrects the logical state, the remaining error is quantified using the logical error rate (LER), which the researchers said is the principal way of measuring the quality of the quantum error rate process.

Reinforcement learning has been used successfully in other areas of complex computing, from robotics to refining the behavior of LLMs, the researchers noted.<br>Keeping The Quantum Computers Running<br>In this case, the method addresses a challenge that quantum computers face when it comes to error. When errors are detected, the systems have to be periodically stopped and recalibrated, interrupting the calculations they were running, which can hobble their ability to run the kinds of complex, long-term jobs they’ll be asked to do as the industry moves into the era of practical commercial...