Researchers from the RIKEN Center for Quantum Computing have utilized machine learning to execute error correction for quantum computers. This is a crucial step in making these devices practical. The researchers have developed an autonomous correction system that can efficiently determine the best way to make the necessary corrections, even though it is an approximate system.
The research has been published in the journal Physical Review Letters.
Unlike classical computers that operate on bits with values of 0 and 1, quantum computers operate on “qubits” that can assume any superposition of computational basis states. When combined with quantum entanglement, which connects different qubits beyond classical means, quantum computers can perform new operations and potentially offer advantages in computational tasks such as large-scale searches, optimization problems, and cryptography.
The main challenge in implementing quantum computers lies in the fragile nature of quantum superpositions. Even small perturbations caused by the environment can lead to errors that quickly destroy quantum superpositions, causing quantum computers to lose their advantage.
To overcome this obstacle, sophisticated methods for quantum error correction have been developed. While these methods can theoretically neutralize errors, they often come with a significant increase in device complexity, which itself is prone to errors and may even increase the exposure to errors. As a result, achieving full-fledged error correction has been challenging.
2023-09-07 21:24:03
Original from phys.org
