Google finds a way to correct more errors in quantum computing

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Google Quantum AI
Google Quantum AI

Just like our computers at home, quantum computers are prone to errors that need to be corrected to allow them to fulfil their tasks properly. A team of researchers at Google Quantum AI say they've developed a system of error correction that could be scaled up without triggering a bunch of new errors; a problem many error correction systems have struggled with. They tested this system at different sizes on a quantum processor, and found the larger version of the system performed better than its smaller counterpart. The researchers say more work needs to be done to reduce the number of errors that slip through their net, but this is a step in the right direction.

Media release

From: Springer Nature

Quantum computing: Improving error correction in quantum computers 

A demonstration of quantum computing where error rates decrease as the size of error correction increases is reported in Nature this week. The work represents a step towards the development of scalable quantum error correction to enable quantum computers to reach sufficiently low error rates and run useful quantum algorithms.

Quantum computers, like their classical counterparts, are prone to errors caused by ‘noise’ (or disruption) from the underlying physical system; realizing their potential requires the reduction of error rates. One method of quantum error correction uses error-correcting codes, in which an ensemble of physical qubits (units of quantum information, equivalent to classical computer bits) form a logical qubit. This system, called a surface code logical qubit, can detect and correct errors without affecting information, but scaling up such systems means manipulating more qubits, which may introduce more logical errors. For logical performance to improve with increasing code size, the overall error correction needs to outweigh the additional logical errors.

Hartmut Neven and colleagues at Google Quantum AI demonstrate that a surface code logical qubit can lower error rates as the system size increases. They created a superconducting quantum processor with 72 qubits and tested it with two different surface codes: one called a distance-5 logical qubit (on 49 physical qubits), and smaller ones called distance-3 logical qubits (on 17 physical qubits). The larger surface code was shown to enable better logical qubit performance (2.914% logical error per cycle) than the smaller surface code (3.028% logical error per cycle). The authors note that more work is needed to reach logical error rates required for effective computation, but this work demonstrates a fundamental requirement for future developments.

**Please note that an online press briefing for the paper below will take place UNDER STRICT EMBARGO on Tuesday 21st February at 4 pm London time (GMT) / 11 am US Eastern Time**

Hartmut Neven, Julian Kelly and Charina Chou will discuss the research. This will be followed by a Q&A session.

To attend this briefing you will need to pre-register by following the link here. Once you are registered, you will receive an email containing the details for the briefing. You will also be provided with the option to save the details of the briefing to your calendar.

Multimedia

Photo of a fully assembled quantum system at Google Quantum AI.
Photo of a fully assembled quantum system at Google Quantum AI.
Photo of two generations of Sycamore processors fabricated by Google Quantum AI.
Photo of two generations of Sycamore processors fabricated by Google Quantum AI.

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Research Springer Nature, Web page The URL will go live after the embargo ends
Journal/
conference:
Nature
Research:Paper
Organisation/s: University of Technology Sydney (UTS)
Funder: We are grateful to S. Brin, S. Pichai, R. Porat, J. Dean, E. Collins and J. Yagnik for their executive sponsorship of the Google Quantum AI team, and for their continued engagement and support. A portion of this work was performed in the University of California, Santa Barbara Nanofabrication Facility, an open access laboratory. J.M. acknowledges support from the National Aeronautics and Space Administration (NASA) Ames Research Center (NASA-Google SAA 403512), NASA Advanced Supercomputing Division for access to NASA high-performance computing systems, and NASA Academic Mission Services (NNA16BD14C). D.B. is a CIFAR Associate Fellow in the Quantum Information Science Program.
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