DeepMind’s research division is renowned for leveraging machine learning models in various research fields. An exemplary case is AlphaFold, which assists in protein research and has garnered the prestigious Nobel Prize in Chemistry.
Recently, DeepMind unveiled their research on AlphaQubit in the journal Nature, merging DeepMind’s machine learning techniques with Google Quantum AI team’s quantum computing research. The challenge in quantum computing technology lies in the instability that arises with an increasing number of qubits due to factors like heat, vibrations, electromagnetic waves, or even cosmic rays, leading to computational errors when qubit states change.
The quantum industry’s solution to this issue involves combining multiple qubits into logical qubits, allowing for error correction by constantly checking the qubit states to maintain accuracy. A visual example is a set of logical qubits comprising 9 qubits, where gray signifies correct states while blue or pink indicates deviations.
Previously, the quantum industry had algorithms for predicting logical qubits, such as the fast but less accurate correlated matching method and the precise yet slow tensor network method. AlphaQubit adapts the Transformer model architecture to predict future changes in logical qubits accurately, thereby prolonging quantum processing duration by ensuring data integrity.
DeepMind tested AlphaQubit on their own Sycamore quantum processor with 49 qubits, demonstrating superior accuracy compared to correlated matching and tensor network methods, while operating at exceptional speed. The successful testing prompted DeepMind to upscale the trial to a 241-qubit Sycamore system, yielding more accurate results than correlated matching, hinting at promising potential for medium-sized quantum computers in the near future.
Despite these advancements, DeepMind notes that AlphaQuantum still operates too slowly for superconductor-based quantum processors capable of measuring qubits a million times per second. Therefore, accelerating prediction methods is crucial for the eventual scalability to quantum computers with millions of qubits.
TLDR: DeepMind’s innovative AlphaQubit research integrates machine learning techniques into quantum computing, enhancing accuracy and speed for predicting logical qubit states, with promising prospects for future quantum computing advancements.
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