Spiking neural networks improve streaming qubit state readout accuracy
Spiking neural networks for streaming qubit readout
Neural and Evolutionary Computing
Summary
Reading the state of quantum bits quickly and accurately is very important for making quantum computers work properly. The authors show that spiking neural networks, which process data as it comes in, can better identify these quantum states compared to traditional methods. These networks update their guesses in real time during the measurement, not only at the end, making the process faster and more precise. They also demonstrate that this approach can be efficiently implemented on hardware to keep up with fast data streams. This work could help build better real-time controls for quantum computers and other science tasks that require quick decisions from streaming data.
What this means in practice
- •For quantum hardware engineers: Implement real-time qubit state detection on FPGA to improve feedback and error correction latency in superconducting quantum processors.
- •For signal processing teams: Use spiking neural networks to classify complex time-dependent signals progressively as data arrives, allowing faster and more accurate streaming signal analysis.
Authors
Barry M. Dillon, Aqib Javed, Jim Harkin, Patryk Dabkowski, Benjamin Lienhard
Abstract
Fast and accurate qubit-state assignment is essential for feedback, calibration, and error correction in quantum processors. In superconducting platforms, frequency-multiplexed readout makes this task intrinsically multivariate as measured traces can encode crosstalk, qubit-state relaxation events, and other transient nonidealities that are not fully captured by conventional matched filtering. Here, we introduce spiking neural network (SNN) discriminators for superconducting qubit readout. By processing the measurement window in successive time chunks, the networks exploit temporal structure and update classification scores as data arrive, rather than waiting until the end of the readout window. The spiking networks outperform matched-filter discrimination and approach the accuracy of a full-trace artificial neural network. Beyond reaching the performance of artificial neural networks, the key advantage of SNNs is that they provide a streaming, time-resolved estimate of the qubit state that evolves as the readout signal is acquired. Using quantisation-aware training and hls4ml synthesis, we further demonstrate that each FPGA inference update can be completed before the next readout chunk arrives. These results establish spiking neural networks as a promising route to low-latency, real-time qubit readout on FPGA hardware, with broader implications for time-critical quantum-control and scientific-inference applications.