Google Willow is a real 105-physical-qubit superconducting research processor, announced on December 9, 2024. Its most important result is not a general speed record: the Nature-reported experiment showed that enlarging a surface-code memory reduced its logical error rate, a below-threshold result that makes scalable quantum error correction more plausible. Willow is not a consumer chip, a general-purpose computer, or an openly rentable cloud processor. As of August 16, 2026, physical access remains restricted to approved groups.
What is Google Willow?
Willow is a quantum-processing chip developed by Google Quantum AI and fabricated at Google’s facility in Santa Barbara. It uses superconducting qubits and is one component of a larger system that also includes cryogenic hardware, control electronics, calibration software, real-time decoding and circuit tools. Google announced the processor on December 9, 2024.
Calling Willow a “chip” does not mean it can operate like a desktop CPU. The processor must be cooled to extremely low temperatures and connected to specialized control and measurement systems. Its purpose is research: improving hardware quality, testing error-correction methods and developing the architecture needed for future fault-tolerant machines.
Google’s hardware overview describes that full-stack approach at Google Quantum AI’s quantum-computing hardware overview.
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Why quantum error correction is the main story
Physical qubits versus logical qubits
Willow’s headline count is 105 physical qubits. Physical qubits are noisy components: gates can fail, measurements can be wrong, energy can leak out of the intended states, and environmental disturbances can corrupt information. A logical qubit is an encoded unit built from multiple physical qubits plus repeated checks and a decoder.
Consequently, 105 physical qubits does not mean 105 reliable, general-purpose logical qubits. The usable logical count depends on physical error rates, the error-correcting code, connectivity, decoder speed, ancilla and measurement requirements, circuit depth and the reliability target.
What “below threshold” means
Surface-code error correction has a threshold. Above that threshold, adding error-correction overhead does not suppress errors effectively. Below it, increasing the code size can make the encoded logical qubit more reliable even though more physical qubits are being used.
That second behavior is the crucial milestone. The Nature paper reports that Willow’s distance-5 and distance-7 surface-code memories operated below threshold: the larger code had a lower logical error rate. This is progress toward fault-tolerant quantum computing, not proof that Willow is already a fault-tolerant computer.
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What the Nature experiment demonstrated
The peer-reviewed Nature paper describes surface-code memories integrated with a real-time decoder on the 105-qubit processor. The experiment primarily tested quantum memory—preserving encoded quantum information—not a long, useful algorithm running on many logical qubits.
The paper reports that the logical memory lifetime exceeded the lifetime of Willow’s best physical qubit by a factor of 2.4 ± 0.3. The full paper is also available through PMC. Google’s explanation of the threshold result is available in its quantum-error-correction overview.
A longer-lived encoded memory is an important engineering result, but a useful fault-tolerant computer still requires many more logical qubits, lower logical error rates, reliable operations throughout deep circuits, fast decoding and a system that can be fabricated, cooled, controlled and calibrated at much larger scale.
The five-minute benchmark—and its limits
Willow also ran a random circuit sampling (RCS) benchmark in under five minutes. RCS uses deliberately chosen random quantum circuits and checks whether the processor produces the expected output distribution. Google estimated that simulating the corresponding task on a leading classical supercomputer would take approximately 1025 years.
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That comparison applies to this particular benchmark, under Google’s stated assumptions. It is not a universal speed rating and does not mean Willow can perform an ordinary business, scientific or artificial-intelligence workload in five minutes. Google has explicitly said that RCS has not demonstrated practical commercial applications.
Read the benchmark claim in context in Google’s Willow announcement and its specification sheet.
Published Willow specifications
Google lists separate configurations optimized for quantum-error-correction (QEC) experiments and RCS. They should not be treated as one undifferentiated performance score.
| Metric | QEC configuration | RCS configuration |
|---|---|---|
| Physical qubits | 105 | 103 used for the RCS result |
| Average connectivity | 3.47; typically four-way | Not stated separately |
| Single-qubit gate error | 0.035% ± 0.029% | 0.036% ± 0.013% |
| Two-qubit gate error | CZ: 0.33% ± 0.18% | iSWAP-like: 0.14% ± 0.052% |
| Measurement error | Repetitive: 0.77% ± 0.21% | Terminal: 0.67% ± 0.51% |
| Mean T1 | 68 ± 13 microseconds | 98 ± 32 microseconds |
| System rate | 909,000 surface-code cycles per second | 63,000 circuit repetitions per second |
| RCS circuit | Not applicable | Depth 40; XEB fidelity 0.1% |
| Google’s classical comparison | Not applicable | Under five minutes versus approximately 1025 years |
These figures describe particular measurements and configurations, not a single “speed” number. Connectivity limits can require routing and extra operations, while error-correction cycles consume qubits and measurement resources.
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The published evidence supports using Willow as a research platform for:
- Quantum-error-correction and logical-memory experiments.
- Hardware characterization, calibration and quantum-control research.
- Benchmarking and testing circuits under realistic device constraints.
- Research into quantum algorithms and future fault-tolerant architectures.
There is no demonstrated broad commercial workload in the cited material. Willow does not replace CPUs, GPUs or classical supercomputers for ordinary software, and the RCS result is not evidence of a commercial quantum advantage.
Can the public use Willow?
Not through an open public interface to the physical processor. As of August 16, 2026, Google says hardware access is restricted to approved groups. Typical requirements include a Google account, a Google Cloud project, Quantum Computing Service/API configuration, project permissions and, in many cases, an approved Google sponsor. Google’s current documentation says billing information is not required at this time; that is a current policy, not a permanent pricing promise.
Google’s Willow Early Access Program targeted a selected group of research partners. Its listed 2026 submission deadline was May 15, 2026, and the page says selected applicants had been notified. Experiment-specific restrictions are described in the 2026 proposal instructions.
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Access and authentication details are documented at Google’s access page, with service concepts at Google Quantum Computing Service.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to experiment with a virtual Willow processor
Individuals can use Google’s Quantum Virtual Machine (QVM), which locally simulates a noisy processor using Willow calibration and noise data. This is useful for learning and circuit preparation, but it is not a connection to the physical chip and cannot establish a physical-device result.
- Install Google’s Cirq tools by following the Cirq introduction.
- Set up the QVM according to Google’s Quantum Virtual Machine instructions.
- Use the virtual processor identifier
willow_pink. - Run circuits locally and inspect how Willow-based noise affects their outputs.
A minimal setup begins with:
import cirq
import cirq_google
import qsimcirq
processor_id = "willow_pink"Simulation size and runtime depend on the computer or cloud resources used; the QVM page does not state a dedicated subscription price.
Willow myths and facts
| Claim | What the evidence supports |
|---|---|
| “Willow has 105 logical qubits.” | Willow has 105 physical qubits. Logical-qubit capacity is a separate, much harder measurement. |
| “Willow solved a useful problem in five minutes.” | Google reported an under-five-minute RCS benchmark, not a practical customer workload. |
| “Below threshold means the machine is fault tolerant.” | Below-threshold scaling is a step toward fault tolerance; it does not complete the engineering task. |
| “Anyone can submit jobs through Google Cloud.” | Physical hardware access remains restricted to approved groups. |
| “Consumers can buy the Willow chip.” | Willow is a research processor, not a retail semiconductor product. |
What happens next?
The practical test of Willow’s significance is whether Google and other researchers can scale the same trend: more physical qubits producing more reliable logical qubits. That requires larger processors, improved fabrication yield, lower logical error rates, faster and better decoders, manageable wiring and cryogenics, and enough logical qubits to run useful algorithms at meaningful depth.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesGoogle’s current result makes that path more credible, but it does not guarantee a timetable or a commercial outcome. The strongest evidence today is a below-threshold quantum-memory demonstration, not a broadly useful quantum computer.
The bottom line
Willow matters because it demonstrated the error-correction behavior a scalable quantum computer needs: increasing the surface-code size reduced the logical error rate. Its 105 physical qubits, RCS benchmark and restricted research access should be understood in that context. Willow is an important research milestone toward fault-tolerant quantum computing—not a consumer product, an open cloud service or a replacement for conventional computers today.
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