Google's Strategies for Reducing Qubit Error Rates

A quantum computer that learns from its own errors while it's computing. That's the framing in a recent paper from Google Quantum AI and Google DeepMind on reinforcement learning control of quantum error correction. Large quantum processors drift. The standard fix is to halt the computation and recalibrate, which won't scale to algorithms expected to run for days or weeks. The authors ask whether QEC can calibrate itself from the data it already produces. The idea: repurpose error detection events as a training signal for a reinforcement learning agent that continuously tunes the physical control parameters (pulse amplitudes, detunings, DRAG coefficients, CZ parameters, and so on). Rather than optimizing logical error rate directly, which is expensive and global, the agent minimizes average detector-event rate, a cheap local proxy whose gradient is approximately aligned with the gradient of LER in the small-perturbation regime. The results on a Willow superconducting processor: - On distance-5 surface and color codes, RL fine-tuning after conventional calibration and expert tuning yields about 20% additional LER suppression - Against injected drift, RL steering improves logical stability 2.4x, rising to 3.5x when decoder parameters are also steered - New record logical error per cycle: 7.72(9)×10⁻⁴ for a distance-7 surface code (with the AlphaQubit2 decoder) and 8.19(14)×10⁻³ for a distance-5 color code (with Tesseract) - In simulation, the framework scales to a distance-15 surface code with roughly 40,000 control parameters, with a convergence rate that is independent of system size The broader takeaway: calibration and computation may not need to be separate phases. If detector statistics can carry enough information to steer a large control stack online, fault tolerance becomes less about pausing to retune and more about a processor that keeps learning while it computes. Worth noting that the current experiments rely on short repeated memory circuits, so real-time steering during a single long logical algorithm (where exploration noise would affect the computation directly) remains future work. Paper: https://lnkd.in/gVQXnpzZ

Google Quantum AI Demonstrates Three Dynamic Surface Codes, Advancing Fault-Tolerant Quantum Computing Introduction Quantum computers promise exponential gains but remain constrained by extreme fragility: qubits are easily disrupted by noise, making error correction the central challenge of the field. Google Quantum AI has now taken a major step toward practical fault tolerance by successfully implementing three dynamic versions of the surface code—one of the most promising quantum error-correction frameworks. Key Developments • The team realized three distinct dynamic surface code circuits—hex, iSWAP, and walking—originally proposed in theoretical work by co-author Matt McEwen. • Their experiments validate that multiple circuit variations can work on real hardware, expanding pathways for adapting error-correction codes to specific device architectures. • Hex circuit: Recompiles the surface code onto a hexagonal grid, reducing connectivity requirements from four neighbors to three. This simplifies fabrication and achieved 2.15× better error suppression. • iSWAP circuit: Replaces CZ gates with iSWAP gates, which are easier to execute and avoid leakage errors. Though they introduce CPHASE errors, the team showed strong performance even on hardware optimized for CZ gates, achieving 1.56× error suppression. • Walking circuit: Allows qubits to exchange roles, effectively “walking” logical information across the chip. This helps isolate and clean leakage errors and offers a new method for routing logical qubits, delivering 1.69× better suppression. • All three implementations successfully detected and corrected noise without disturbing quantum information, confirming the practicality of dynamic constructions. Scientific Significance • This is the strongest evidence yet that dynamic surface codes—adapted to hardware constraints—can function reliably in real quantum devices. • The team also introduced a simplified “detector budgeting” technique, enabling easier analysis of how specific error sources impact logical performance. • The work opens new avenues for designing codes tailored to imperfect hardware, enabling better yield and robustness as systems scale. • Upcoming experiments will explore even more advanced dynamic circuits, including those based on the LUCI framework for routing around faulty qubits. Why This Matters Reliable quantum error correction is the linchpin for large-scale quantum computing. Google’s demonstration shows that error-correcting codes can be adapted dynamically to real hardware constraints—unlocking higher performance, easier fabrication, and more flexible architectures. This progress accelerates the roadmap toward fault-tolerant quantum systems capable of solving real-world scientific and industrial problems. I share daily insights with 34,000+ followers across defense, tech, and policy. If this topic resonates, I invite you to connect and continue the conversation. Keith King https://lnkd.in/gHPvUttw

Google Unveils Willow: A Leap Forward in Quantum Computing Google Quantum AI has introduced Willow, a cutting-edge quantum chip designed to address two of the field’s most significant challenges: error correction and computational scalability. Willow, fabricated in Google’s Santa Barbara facility, achieves state-of-the-art performance, marking a pivotal step toward realizing a large-scale, commercially viable quantum computer. It gets way geekier from here – but if you’re with me so far… Exponential Error Reduction Julian Kelly, Director of Quantum Hardware at Google, emphasized Willow’s ability to exponentially reduce errors as the system scales. Utilizing a grid of superconducting qubits, Willow demonstrated a historic breakthrough in quantum error correction. By expanding arrays from 3×3 to 5×5 and then 7×7 qubits, researchers cut error rates in half with each iteration. This achievement, referred to as being “below threshold,” signifies that larger quantum systems can now exhibit fewer errors, a challenge pursued since Peter Shor introduced quantum error correction in 1995. The chip also achieved “beyond breakeven” performance, where arrays of qubits outperformed the lifetimes of individual qubits, which is key to ensuring the feasibility of practical quantum computations. Ten Septillion Years in Five Minutes Willow’s computational capabilities were validated using the Random Circuit Sampling (RCS) benchmark, a rigorous test of quantum supremacy. According to Google’s estimates, Willow completed a task in under five minutes that would take a modern supercomputer ten septillion years—a timescale exceeding the age of the universe. This achievement underscores the rapid, double-exponential performance improvements of quantum systems over classical alternatives. While the RCS benchmark lacks direct commercial applications, it remains a critical indicator of quantum computational power. Kelly noted that surpassing classical systems on this benchmark solidifies confidence in the broader potential of quantum technology. Building Toward Practical Applications Google’s roadmap aims to bridge the gap between theoretical quantum advantage and real-world utility. The team is now focused on achieving “useful, beyond-classical” computations that solve practical problems. Applications in drug discovery, battery design, and AI optimization are among the potential breakthroughs quantum computing could unlock. Willow’s advancements in quantum error correction and computational scalability highlight its transformative potential. As Kelly explained, “Quantum algorithms have fundamental scaling laws on their side,” making quantum computing indispensable for tasks beyond the reach of classical systems. Quantum computing is still years away, but this is an exciting milestone. Considering the remarkable rate of technological improvement we’re experiencing right now, practical quantum computing (and quantum AI) may be closer than we think. -s

Google's Willow chip shows that quantum error correction is starting to work. Just "starting", because while the ~1e-3 error rate reached by Willow is good, it has been achieved by others without error correction. So, how do we get error rates we couldn't reach with physical qubits alone? Easy: you "just" add more qubits in your logical qubit. But because there are errors on two dimensions in quantum computing, a 2D-structure (the surface code) is usually required to correct errors. This means that increasing protection against errors causes the number of qubits to grow quickly. With a surface code, protecting against 1 error at a time during an error correction cycle requires 17 qubits. 2 errors at a time? 49 qubits. 3 errors at a time? 97 qubits. This is the max Willow could achieve. This quadratic scaling leads Google to expect that reaching a 1e-6 error rate on a Willow-like chip will require some 1457 physical qubits (protecting against 13 errors at a time). And this is the reason why Alice & Bob is going for cat qubits instead. By reducing error correction from a 2D to a 1D problem, cat qubits make the scaling of error rates much more favorable. Even with the simplest error correction code (a repetition code), correcting one error at a time only requires 5 qubits. 2 errors? 9 qubits. 3 errors? 13 qubits. 13 errors? This is just 53 qubits instead of 1457! This situation is summarized in the graph below. It is taken from our white paper (link in the 1st comment) and I added a point corresponding to the biggest Willow experiment. Now, to be fair, Alice & Bob still needs to release the results of even a 5-qubit experiment. But when this is done, there is a fair chance the error rates will quickly catch up with those achieved by Google or others, because so few additional qubits are required to improve error rates. There are big challenges on both sides. Mastering cat qubits is hard. Scaling chips is hard. But consistent progress is being made on both sides too. Anyway, I can't wait for the moment when I can add the Alice & Bob equivalent of the Willow experiment on the chart below. And for once, I hope it will be up and to the left!

One of the biggest challenges in quantum computing has always been error correction. Unlike classical computers, where errors are rare and manageable, quantum systems are incredibly sensitive. Even the tiniest disturbance can disrupt a calculation. For decades, scientists feared that error correction might require so much effort that it would outweigh the benefit of the computation itself—a roadblock for practical quantum computing. This week, Google announced a major breakthrough with its new #Willow chip, showing that error correction doesn’t have to diverge. They demonstrated that their system can perform calculations with 105 qubits, while simultaneously using error correction to manage and stabilize the system. For the first time, the overhead required for error correction scales in a manageable way as the system grows. Here’s why it’s game-changing: • 70 physical qubits are allocated to error correction for every logical qubit in the system, making the calculations reliable without overwhelming the computational capacity. • It proves quantum systems can become reliable at scale, bringing us closer to real-world applications like drug discovery, clean energy breakthroughs, and revolutionary materials design. • The Willow chip has already shown it can handle complex calculations that today’s fastest supercomputers couldn’t solve in the entire lifetime of the universe. Even Elon Musk couldn’t help but react, commenting “Wow” on X when the news dropped. This marks a turning point for quantum computing—it’s no longer just theoretical. The pieces are falling into place for a future where these machines solve humanity’s toughest problems. #AI #quantum