{"id":37101,"date":"2023-09-07T13:57:05","date_gmt":"2023-09-07T12:57:05","guid":{"rendered":"https:\/\/www.innovationnewsnetwork.com\/?p=37101"},"modified":"2024-09-04T20:17:05","modified_gmt":"2024-09-04T19:17:05","slug":"quantum-error-correction-improved-machine-learning","status":"publish","type":"post","link":"https:\/\/www.innovationnewsnetwork.com\/quantum-error-correction-improved-machine-learning\/37101\/","title":{"rendered":"Quantum error correction improved with Machine Learning"},"content":{"rendered":"

Researchers from the RIKEN Center for Quantum Computing have used Machine Learning to better quantum error correction.<\/h2>\n

Performing error correction for quantum computers is a crucial step for making these devices practical.<\/p>\n

The team used an autonomous correction system that can efficiently determine how best to make the necessary corrections.<\/p>\n

The paper, \u2018Approximate Autonomous Quantum Error Correction with Reinforcement Learning<\/a>,\u2019 is published in the journal Physical Review Letters. <\/em><\/p>\n

Characteristics of quantum computers<\/h3>\n

Classic computers operate on bits that can only take the basic values of 0 and 1. In contrast to this, quantum computers<\/a> operate on qubits, which can assume any superposition of the computational basis states.<\/p>\n

Another 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":"\nQuantum error correction improved with Machine Learning<\/title>\n<meta name=\"description\" content=\"Researchers from the RIKEN Center for Quantum Computing have used Machine Learning to better quantum error correction.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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