A new work from Google Quantum AI represents a step towards the development of scalable quantum error correction to enable quantum computers to reach sufficiently low error rates and run useful algorithms.
Left: a fully assembled quantum system at Google Quantum AI; prominently displayed is the dilution refrigerator in which the computations occur, the quantum processor and quantum-limited amplifiers installed at the bottom stage of the refrigerator, various cables connecting bottom to top, and control electronics of quantum computer in the back. Right: two generations of Sycamore processors fabricated by Google Quantum AI; each processor features two chips that are bonded together: a chip containing the quantum bits (qubits), and a chip containing the wiring between the qubits and the outside world; the latest generation of the Sycamore processor on the right is notably larger than previous generations, but also has several modifications to the qubits resulting in improved performance. Image credit: Google Quantum AI.
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) 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.
“Practical quantum computing will require error rates well below those achievable with physical qubits,” said Dr. Hartmut Neven and colleagues from Google Quantum AI.
“Quantum error correction offers a path to algorithmically relevant error rates by encoding logical qubits within many physical qubits, for which increasing the number of physical qubits enhances protection against physical errors.”
“However, introducing more qubits also increases the number of error sources, so the density of errors must be sufficiently low for logical performance to improve with increasing code size.”
In their new work, the authors demonstrated 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).
“More work is needed to reach logical error rates required for effective computation, but this work demonstrates a fundamental requirement for future developments,” the researchers said.
Their paper was published in the journal Nature.
_____
Google Quantum AI. 2023. Suppressing quantum errors by scaling a surface code logical qubit. Nature 614, 676-681; doi: 10.1038/s41586-022-05434-1
