Google's quantum computer reduces error rates, hits new 'milestone'

The next step now requires a leap in the hardware of the processor.
Ameya Paleja
Google Quantum AI
Google Quantum AI

Quantum AI 

Search engine giant Google has reached a major "milestone" in its ambitious plan to build a commercially viable quantum computer. It has succeeded in reducing the error rate of calculation error rate calculation in its quantum computers, which paves the way to building quantum computers that can solve real-world problems.

Google's announcement comes at a time when the company is struggling to demonstrate its capabilities in the artificial Intelligence (AI) domain. At the same time, OpenAI hogs the limelight with its conversational chatbot, ChatGPT. Amidst whispers of whether the company has lost its edge, Google has shown little but significant improvements in error correction in its quantum computer.

How error correction can help build better quantum computers

At the core of the processor in a quantum computer are quantum bits or qubits that can perform calculations much faster than any known computer. However, like conventional computers, qubits can also make errors, but correcting them is difficult.

A regular computer can store copies of information in special error correction bits. However, the qubits are in a superposition, meaning that qubits can be a mixture of two states, unlike regular bits that occupy either a 0 or a 1 state. Observing or trying to measure the state also collapses the state's wave function, which means that the qubit is no longer in that state.

To solve this problem, physicists are using a logical qubit, a collection of many physical qubits that can be used by the machine to correct errors. More physical qubits are added to the processor to increase the accuracy of the logical qubit.

Google used this approach on its third-generation Sycamore quantum processor, which has 53 qubits. The team has demonstrated that using 17 physical qubits could help recover one error at a time. By increasing this number to 49, two errors could be dealt with simultaneously. Doing so brought down the error rate from 3.028 percent to 2.914 percent, New Scientist said in its report.

While this might not sound like a lot, it paves the way for scaling up quantum computers since they will become fault-tolerant. The logical next step would be to increase the number of qubits. However, that is currently impossible due to the limitations of the current hardware design.

A leap in the hardware design is needed to carry out the next step, which will eventually lead to a quantum computer with 1 million physical qubits and 1,000 logical ones. Google hopes this can be achieved by the end of the decade.

Google's findings were published in the journal Nature.

Abstract

Practical quantum computing will require error rates well below those achievable with physical qubits. Quantum error correction1,2 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. Here we report the measurement of logical qubit performance scaling across several code sizes, and demonstrate that our system of superconducting qubits has sufficient performance to overcome the additional errors from increasing qubit number. We find that our distance-5 surface code logical qubit modestly outperforms an ensemble of distance-3 logical qubits on average, in terms of both logical error probability over 25 cycles and logical error per cycle ((2.914 ± 0.016)% compared to (3.028 ± 0.023)%). To investigate damaging, low-probability error sources, we run a distance-25 repetition code and observe a 1.7 × 10−6 logical error per cycle floor set by a single high-energy event (1.6 × 10−7 excluding this event). We accurately model our experiment, extracting error budgets that highlight the biggest challenges for future systems. These results mark an experimental demonstration in which quantum error correction begins to improve performance with increasing qubit number, illuminating the path to reaching the logical error rates required for computation.

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