📋 IBM Crosses the Logical Qubit Threshold

IBM announced at its annual Quantum Summit in May 2026 that its Kookaburra processor, the successor to the Heron processor deployed in 2024, had successfully demonstrated operation with 5,000 logical qubits using a novel error-correction protocol the company calls the "gross code." Physical qubits, the fundamental units of quantum information, are inherently noisy and lose coherence on timescales of microseconds to milliseconds.

Logical qubits are constructed by encoding quantum information redundantly across multiple physical qubits, allowing errors to be detected and corrected in real time without collapsing the quantum state. The gross code is a surface-code variant that IBM claims achieves the same logical error rate as the standard surface code using approximately 30% fewer physical qubits per logical qubit.

The Kookaburra processor contains 12,300 superconducting transmon qubits fabricated on a chip integrating through-silicon vias and multi-layer wiring for dense control signal routing. Kookaburra achieves the 5,000 logical qubits through a gross-code arrangement requiring roughly 1,400 physical qubits per logical qubit, an improvement from earlier demonstrations requiring thousands of physical qubits per logical qubit.

Independent benchmarkers measured a logical error rate of approximately 10⁻¹⁰ per gate operation, which while still above the target 10⁻¹⁵ needed for running Shor's algorithm on cryptographically relevant problem sizes, represents a millionfold improvement over the physical error rates of individual qubits.

📋 Google Willow Crosses the Error Correction Threshold

Google Quantum AI's Willow processor, manufactured in Google's dedicated fabrication facility in Santa Barbara, California, achieved a historically significant milestone in early 2026: for the first time, a quantum processor demonstrated that increasing the code distance which is the number of physical qubits used to encode a logical qubit actually decreased the logical error rate. In all previous demonstrations, adding more physical qubits introduced more noise than the error correction could handle, making larger logical qubits less reliable.

Willow doubled its logical qubit lifetime from 100 microseconds to 240 microseconds by increasing the surface-code distance from 3 to 5, crossing the threshold where the benefit of error correction outweighed the additional noise.

Google has also demonstrated that Willow can complete a random circuit sampling benchmark in under five minutes that would take the Frontier classical supercomputer approximately 10 septillion years, extending the quantum advantage claim first made with Sycamore in 2019. Google Quantum AI lead Hartmut Neven said the company is now targeting a 1,000-logical-qubit system capable of executing commercially relevant quantum chemistry simulations within three years.

📋 IonQ and Trapped-Ion Approaches

IonQ's Forte Enterprise system, based on trapped ytterbium ions, achieved 256 algorithmic qubits with 99.96% single-qubit gate fidelity and 99.6% two-qubit gate fidelity in May 2026, setting the record for gate fidelity among commercial quantum computers. Trapped-ion qubits benefit from inherently long coherence times and all-to-all connectivity, meaning any qubit can interact with any other without the nearest-neighbor constraints of superconducting architectures, reducing the number of SWAP operations needed to execute quantum circuits.

IonQ has deployed Forte Enterprise systems at the US Air Force Research Lab and with several pharmaceutical partners for molecular simulation workloads.

Quantinuum, formed from the merger of Honeywell Quantum Solutions and Cambridge Quantum, demonstrated 99.91% two-qubit gate fidelity with its H3 system using a unique shuttling-based architecture where ions are physically moved between interaction zones. D-Wave, the pioneer of quantum annealing, reported the first peer-reviewed demonstration of a quantum speedup on a practically relevant problem using its Advantage2 system with 7,000 qubits to solve magnetic materials simulation problems that scale exponentially on classical computers.