quantum computers<\/a> operate on qubits, which can assume any superposition of the computational basis states.<\/p>\nAnother quantum characteristic is quantum entanglement, which connects different qubits beyond classical means.<\/p>\n
In combination with the qubits, quantum entanglement allows quantum computers to perform entirely new operations. This gives rise to potential advantages in some computational tasks, such as large-scale searches, optimisation problems, and cryptography.<\/p>\n
Challenges of putting quantum computers into practice<\/h3>\n
The main challenge towards putting quantum computers into practice is derived from the fragile nature of quantum superpositions.<\/p>\n
For example, tiny perturbations can be induced by the presence of an environment that gives rise to errors that destroy quantum superpositions. This causes quantum computers to lose their edge.<\/p>\n
Overcoming quantum challenges with error correction<\/h3>\n
To overcome this challenge, sophisticated methods for quantum error correction have been developed.<\/p>\n
Although in theory, they can neutralise the effect of errors, they often come with a complex device. This in itself is error-prone and therefore has the potential to increase the exposure to errors.<\/p>\n
Because of this, error correction has remained elusive.<\/p>\n
Leveraging Machine Learning for quantum error correction<\/h3>\n
The team used Machine Learning in their search for quantum error correction schemes that minimise device overhead whilst maintaining good error-correcting performance.<\/p>\n
They focused on an autonomous approach to quantum error correction. In this approach, a cleverly designed, artificial environment replaces the necessity to perform frequent error-detecting measurements.<\/p>\n
The researchers also looked at bosonic qubit encodings. These are available in some of the most promising and widespread quantum computing machines based on superconducting circuits.<\/p>\n
Finding potential options in the vast space of bosonic qubit encoding represents a complex optimisation task. This was addressed with reinforcement learning \u2013 an advanced Machine Learning method \u2013 where an agent explores a possibly abstract environment to learn and optimise its action policy.<\/p>\n
With this, the team discovered that a simple qubit encoding could not only greatly reduce the device complexity compared to other encodings, but also outperformed its competitors in terms of its capability to correct errors.<\/p>\n
Yexiong Zeng, the first author of the paper, published in Physical Review Letters, stated: \u201cOur work not only demonstrates the potential for 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.\u201d<\/p>\n
Franco Nori concluded: \u201cMachine Learning can play a pivotal role in addressing large-scale quantum computation and optimisation challenges. Currently, we are actively involved in a number of projects that integrate Machine Learning, artificial neural networks, quantum error correction, and quantum fault tolerance.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"
Researchers from the RIKEN Center for Quantum Computing have used Machine Learning to better quantum error correction.<\/p>\n","protected":false},"author":18,"featured_media":37110,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[24615],"tags":[570,793],"acf":[],"yoast_head":"\n
Quantum error correction improved with Machine Learning<\/title>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\t\n\t\n\t\n\n\n\n\n\n\t\n\t\n\t\n