The instability of qubits stems from the way they rely on quantum properties such as superposition and entanglement to solve complex problems in fewer steps. Qubits can sift through vast numbers of possibilities using quantum inference to find solutions, but their natural state is extremely fragile. They can be disrupted all too easily, by microscopic defects in hardware, or even the slightest variation in heat, minuscule vibrations, electromagnetic interference or even cosmic rays.
That explains why existing quantum computers either run at temperatures close to absolute zero, or use novel technologies like lasers to manipulate ion-based qubits held in a vacuum, but while these techniques do help, they don’t completely solve the problem of instability.
By using quantum error correction systems, it’s possible to get around the instability problem by grouping multiple qubits into a single, logical qubit and performing regular consistency checks on them. It then becomes possible to identify logical qubits that deviate from the norm, and correct them when it happens.
The difficulty lies in spotting these errors, and that’s where AlphaQubit can help – Google DeepMind explains that it’s a neural network-based decoder that leverages the same transformer architecture that underpins many of today’s large language models. The team trained AlphaQubit on hundreds of consistency checks, so that it can correctly predict when a logical qubit starts behaving incorrectly.
As the researchers explained:
“We began by training our model to decode the data from a set of 49 qubits inside a Sycamore quantum processor, the central computational unit of the quantum computer. To teach AlphaQubit the general decoding problem, we used a quantum simulator to generate hundreds of millions of examples across a variety of settings and error levels. Then we tuned AlphaQubit for a specific decoding task by giving it thousands of experimental samples from a particular Sycamore processor.”
In the researchers’ tests, AlphaQubit displayed incredible accuracy compared to existing quantum decoders, making 6% fewer errors than tensor network methods in the largest Sycamore experiments. While tensor networks are quite accurate themselves, the problem is that they’re extremely slow. AlphaQubit, on the other hand, can identify errors with more accuracy and at much greater speed, more than fast enough to scale up to handle real-world quantum computing operations.
