Breakthrough in Quantum Computing: Logical Qubit Retention Reaches 96% on IBM Heron Processor
A significant shift is emerging in quantum computing. A team of researchers from the University of Sydney, in collaboration with IBM engineers, has achieved a substantial improvement in the stability of logical qubits — a key element for building fault-tolerant quantum machines. The new error correction architecture has increased the qubit survival rate to 96% per cycle, a major leap from previous results that did not exceed 90%.
The Problem of "Idle Noise"
The main obstacle to stable fault-tolerant quantum computing (FTQC) is the so-called "idle noise." In modern systems, correcting errors requires regular intermediate measurements of qubits. During these pauses, the remaining components of the processor lose coherence, generating new failures. This effect has long undermined attempts to scale quantum systems.
A New Approach to Architecture
Physicists have completely redesigned error correction schemes, radically reducing the time of forced computation halts. Testing was conducted on the advanced 156-qubit superconducting processor IBM Quantum Heron r2. Optimizing the algorithms not only increased accuracy to 96% but also reduced data degradation during operation. As project leader Stephen Bartlett noted, each stage of computation requires multiple checks, and "idle time" has long remained a "serious obstacle" to reliable operation.
Prospects for the Industry
Although the result was obtained in a laboratory setting on a single processor, its significance for the industry is hard to overstate. Scalability and fault tolerance remain the main barriers to the commercialization of quantum computing. In June, IBM already demonstrated progress in error correction, and now the company aims to achieve the first confirmed cases of quantum advantage by the end of 2026.
Analyst's comment: Achieving 96% preservation is not just a number. It is a signal that we are approaching the threshold where quantum computing will become practically useful for solving real-world problems, including cryptography and modeling complex molecules. However, full dominance of quantum machines over classical ones is still far off — challenges of scaling and reducing the cost of such systems remain to be addressed.