Researchers Build Fast Qubit Reader Using Spiking Nets

Quantum processors now access qubit states quickly enough for real-time error correction. Spiking neural networks classify qubit states as measurement data arrives, circumventing the need to wait until an entire trace is acquired. This delivers a continuously updated estimate of the qubit’s condition; surpassing matched filtering techniques and equalling the accuracy of conventional artificial neural networks. A new system utilising ‘spiking’ neural networks rapidly determines the condition of qubits, the fundamental building blocks of quantum computers.

Unlike existing methods which require complete data collection before analysis, this technique processes incoming signals in stages allowing for continuous updates regarding the qubit’s status. Researchers at Ulster University and collaborating institutions have pioneered a new approach to reading information from qubits using artificial intelligence inspired by the human brain. The system employs ‘spiking’ neural networks that process incoming signals in stages offering continuous updates regarding the qubit’s status, unlike traditional methods requiring complete data collection before analysis.

Frequency-multiplexed readout is like listening to multiple radio stations simultaneously; separating each signal demands careful processing. The innovation surpasses matched filtering techniques, akin to comparing an unknown signal against pre-defined templates, while achieving comparable accuracy to conventional artificial neural networks. This allows for real-time assessment of a qubit’s condition and opens up possibilities for faster error correction but can these spiking networks be efficiently implemented on existing quantum hardware.