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Quantum computing: Google researchers think they can now stop qubits going wrong

ZDNet

Google's researchers have successfully tested error correction methods with the company's Sycamore processor. Google's researchers have demonstrated that, subject to certain conditions, error correction works on the company's Sycamore quantum processor and can even scale exponentially, in what is yet another step towards building a fault-tolerant quantum computer. The breakthrough is likely to catch the attention of scientists working on quantum error correction, a field that is concerned not with qubit counts but rather with qubit quality. While increasing the number of qubits supported by quantum computers is often presented as the key factor in unlocking the unprecedented compute power of quantum technologies, equally as important is ensuring that those qubits behave in a way that allows for reliable, error-free results. This is the idea that underpins the concept of a fault-tolerant quantum computer, but quantum error correction is still in very early stages.


Fault-tolerant detection of a quantum error

Science

A critical component of any quantum error–correcting scheme is detection of errors by using an ancilla system. We demonstrate a fault-tolerant error-detection scheme that suppresses spreading of ancilla errors by a factor of 5, while maintaining the assignment fidelity. The same method is used to prevent propagation of ancilla excitations, increasing the logical qubit dephasing time by an order of magnitude. Our approach is hardware-efficient, as it uses a single multilevel transmon ancilla and a cavity-encoded logical qubit, whose interaction is engineered in situ by using an off-resonant sideband drive. The results demonstrate that hardware-efficient approaches that exploit system-specific error models can yield advances toward fault-tolerant quantum computation.


Google: We'll build this 'useful' quantum computer by the end of the decade

ZDNet

Google has unveiled its new Quantum AI campus in Santa Barbara, California, where engineers and scientists will be working on its first commercial quantum computer – but that will probably be a decade way. The new campus has a focus on both software and hardware. On the latter front, these include its first quantum data center, quantum hardware research labs, and Google's own quantum processor chip fabrication facilities, says Erik Lucero, lead engineer for Google Quantum AI in a blogpost. Quantum computers offer great promise for cryptography and optimization problems. ZDNet explores what quantum computers will and won't be able to do, and the challenges we still face.


Google demonstrates vital step towards large-scale quantum computers

New Scientist

Google has shown that its Sycamore quantum computer can detect and fix computational errors, an essential step for large-scale quantum computing, but its current system generates more errors than it solves. Error-correction is a standard feature for ordinary, or classical, computers, which store data using bits with two possible states: 0 and 1. Transmitting data with extra "parity bits" that warn if a 0 has flipped to 1, or vice versa, means such errors can be found and fixed. In quantum computing the problem is far more complex as each quantum bit, or qubit, exists in a mixed state of 0 and 1, and any attempt to measure them directly destroys the data. One longstanding theoretical solution to this has been to cluster many physical qubits into a single "logical qubit". Although such logical qubits have been created previously, they hadn't been used for error correction until now.


Closing In on Quantum Error Correction

Communications of the ACM

After decades of research, quantum computers are approaching the scale at which they could outperform their "classical" counterparts on some problems. They will be truly practical, however, only when they implement quantum error correction, which combines many physical quantum bits, or qubits, into a logical qubit that preserves its quantum information even when its constituents are disrupted. Although this task once seemed impossible, theorists have developed multiple techniques for doing so, including "surface codes" that could be implemented in an integrated-circuit-like planar geometry. For ordinary binary data, errors can be corrected, for example, using the majority rule: A desired bit, whether 1 or 0, is first triplicated as 111 or 000. Later, even if one of the three bits has been corrupted, the other two "outvote" it and allow recovery of the original data.