The journal Nature published a study by Microsoft Quantum and ion quantum computer developer Quantinuum on reducing logical errors in quantum processors. The teams reported improvements ranging from 11 to 800 times compared to comparable physical schemes.
Separately, IBM Research described its approach to discovering new quantum error correction codes using large language models (LLMs). The OpenEvolve-based system identified 465 candidates, but their practical applicability still needs to be verified.
Microsoft Quantum and Quantinuum's Work
Error correction remains one of the main barriers to scaling quantum computers. Current qubits are sensitive to noise and quickly accumulate errors, necessitating logical qubits, decoders, and circuits that detect and correct failures during operation.
A physical qubit is the hardware unit of a quantum processor, while a logical qubit is a more reliable unit that combines several physical qubits through error correction coding. This setup is essential for quantum computers to perform long computations without losing results due to noise.
The article Improved quantum processor logical error rates via correction and detection details the results of the collaboration between Microsoft Quantum and Quantinuum. The experiment utilized two designs optimized for Quantinuum's ion processor: a 12-qubit code inspired by the Knill scheme and a 16-qubit tesseract color code. The first encodes two logical qubits, while the second encodes four.
According to Microsoft, the schemes covered computations involving up to 12 logical qubits. When preparing a Bell state, the logical error rate dropped from about 0.8% for the physical scheme to 0.001%, resulting in an 800-fold improvement.
Repeated error correction achieved a result 51 times lower than the physical baseline for one round. Preparing a 12-qubit cat state, a multi-qubit superposition state, yielded a 22-fold improvement.
“Our results show that modern quantum devices are already capable of leveraging fault tolerance and error correction to significantly suppress errors in non-trivial quantum circuits,” the article's abstract states.
Microsoft also highlighted previous joint results with Quantinuum: over 14,000 individual experiments without recorded errors, demonstrations of 12 reliable logical qubits, and a hybrid chemical simulation using logical qubits, artificial intelligence, and high-performance computing.
IBM Research Utilizes AI for Code Discovery
IBM Research announced the use of OpenEvolve to find quantum error correction codes. OpenEvolve is an open-source library that employs large language models for the evolutionary enhancement of software code.
The team focused on bivariate bicycle codes, a type of quantum code with low parity-check density, which IBM considers in its roadmap for fault-tolerant quantum computing.
The parameters of such codes are recorded in the format [[n,k,d]], where n is the number of physical qubits, k is the number of logical qubits, and d is the code distance. The higher the d, the more errors the code can withstand before losing utility.
From initial runs, the system proposed 465 candidates. Among them, IBM highlighted the code [[288,50,8]] with 50 logical qubits, surpassing the previous record of 16 for this family. The company also noted the compact code [[72,4,8]] with 72 physical qubits and options [[288,16,12]] and [[360,12,≤24]].
IBM estimates that some candidates may be comparable to the [[144,12,12]] gross code under certain types of noise, which the company plans to use in fault-tolerant quantum computers. However, IBM emphasizes that the practical applicability of the discovered codes requires further validation.
The source code for the project qcode-discovery is available on GitHub. The OpenEvolve library is also accessible in an open repository.
In June 2025, IBM announced plans to build IBM Quantum Starling—a large-scale fault-tolerant quantum computer with 200 logical qubits and 100 million quantum gates—by 2029. The system architecture will also rely on bivariate bicycle codes.
Additionally, in June, Quantum X Labs and the Quantum Machines IQCC research platform reported plans to test an AI decoder for quantum error correction.