Real-Time Detection of Charge Jumps in Superconducting Qubits with a Convolutional Neural Network
quant-ph, cs.LG
Submitted: 2026-07-15
Updated: 2026-09-18
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- Quantum sensing with tunable superconducting qubits: optimization and speed-up
- Listening For New Physics With Quantum Acoustics
- Measuring quasiparticle dynamics for particle impact reconstruction in a superconducting qubit chip
- Disentangling the sources of ionizing radiation in superconducting qubits
- Correlated Charge Noise and Relaxation Errors in Superconducting Qubits
- First Measurement of Correlated Charge Noise in Superconducting Qubits at an Underground Facility
- Experimental advances with the QICK (Quantum Instrumentation Control Kit) for superconducting quantum hardware
- End-to-end workflow for machine learning-based qubit readout with QICK and hls4ml
- Deep Neural Network Discrimination of Multiplexed Superconducting Qubit States
- Real-time Inference with 2D Convolutional Neural Networks on Field Programmable Gate Arrays for High-rate Particle Imaging Detectors
- Automatic Charge State Tuning of 300 mm FDSOI Quantum Dots Using Neural Network Segmentation of Charge Stability Diagram
- An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling
- Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
- The QICK (Quantum Instrumentation Control Kit): Readout and control for qubits and detectors
- Fast inference of deep neural networks in FPGAs for particle physics
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