Machine learning contributes to better quantum error correction

Researchers from the RIKEN Center for Quantum Computing used machine learning to perform error correction for quantum computers — a critical step to making such devices practical — using an independent correction system that, despite being approximate, can determine how best to efficiently make the necessary corrections.

The research is published in the journal Physical review letters.

In contrast to classical computers, which operate on bits that can only take the base values ​​0 and 1, quantum computers operate on “quantum bits”, which can assume any superposition of arithmetic base states. In combination with quantum entanglement, another quantum property that connects different qubits beyond classical means, this enables quantum computers to perform entirely new operations, leading to potential advantages in some computational tasks, such as large-scale searches, optimization problems, and cryptography. .

The main challenge to putting quantum computers into practice stems from the very fragile nature of quantum superpositions. Indeed, small perturbations caused, for example, by the ubiquitousness of the environment lead to errors that quickly destroy quantum superpositions, and as a result, quantum computers lose their edge.

To overcome this hurdle, sophisticated quantum error-correction methods have been developed. Although they could, in theory, successfully neutralize the impact of errors, they often come with a significant overhead in device complexity, which is itself error-prone, and thus potentially increases vulnerability to errors. As a result, full correction of errors remained elusive.

In this work, the researchers took advantage of machine learning in search of error correction systems that reduce device overhead while maintaining good error correction performance. To this end, they focused on an independent approach to quantum error correction, in which an intelligently designed synthetic environment replaces the necessity of repeated measurements to detect errors.

They also looked at “bossonic qubit ciphers,” which, for example, are available and used in some of today’s most promising and widespread quantum computing machines based on superconducting circuits.

Finding high-performance candidates in the vast search space of bosonic qubits is a complex optimization task, which researchers tackle through reinforcement learning, an advanced machine learning method in which an agent explores a potential abstract environment to learn and improve its action policy.

With this, the group found that a simple, approximate qubit cipher could not only significantly reduce device complexity compared to other proposed ciphers, but also outperform its competitors in terms of its ability to correct errors.

“Our work not only demonstrates the possibility of deploying machine learning towards quantum error correction, but it may also bring us a step closer to the successful implementation of quantum error correction in experiments,” says Yixiong Zheng, first author of the study.

According to Franco Nuri, “Machine learning can play a pivotal role in addressing quantum computing challenges and large-scale optimizations. Currently, we are actively involved in a number of projects integrating machine learning, artificial neural networks, quantum error correction, and quantum analysis. Fault tolerance.”