Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Bulletin of the American Physical Society

A scalable FPGA platform for qubit readout and control

Mats O. Tholén, Riccardo Borgani, Shan W. Jolin, David B. Haviland

Published Mar 2, 2020• 0 citations
OpenAlex

Related papers

Scalable FPGA-based qubit control hardware

Gang Huang, Yilun Xu, Lawrence Doolittle +2 more

Bulletin of the American Physical Society•Mar 7, 2019•0 citations

End-to-End Workflow for Machine-Learning-Based Qubit Readout With QICK and hls4ml

Giuseppe Di Guglielmo, Botao Du, Javier Campos +10 more

We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx RFSoC FPGAs, we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes thehls4mlpackage and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python APIs. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32.25 ns and less than 16% FPGA look-up table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.

arXiv:2501.14663•IEEE Transactions on Quantum Engineering•Jan 1, 2025•11 citations

An FPGA-based quantum feedback system for real-time qubit control

Unpil Baek, Yilun Xu, Gang Huang +2 more

Bulletin of the American Physical Society•Mar 7, 2019•0 citations

Demonstrating the Potential of Adaptive LMS Filtering on FPGA-Based Qubit Control Platforms for Improved Qubit Readout in 2D and 3D Quantum Processing Units

Hans Johnson, Nicholas Bornman, Tae-Yoon Kim +3 more

Advancements in quantum computing underscore the critical need for sophisticated qubit readout techniques to accurately discern quantum states. This abstract presents our research intended for optimizing readout pulse fidelity for 2D and 3D Quantum Processing Units (QPUs), the latter coupled with Superconducting Radio Frequency (SRF) cavities. Focusing specifically on the application of the Least Mean Squares (LMS) adaptive filtering algorithm, we explore its integration into the FPGA-based control systems to enhance the accuracy and efficiency of qubit state detection by improving Signal-to-Noise Ratio (SNR). Implementing the LMS algorithm on the Zynq UltraScale+ RFSoC Gen 3 devices (RFSoC 4x2 FPGA and ZCU216 FPGA) using the Quantum Instrumentation Control Kit (QICK) open-source platform, we aim to dynamically test and adjust the filtering parameters in real-time to characterize and adapt to the noise profile presented in quantum computing readout signals. Our preliminary results demonstrate the LMS filter's capability to maintain high readout accuracy while efficiently managing FPGA resources. These findings are expected to contribute to developing more reliable and scalable quantum computing architectures, highlighting the pivotal role of adaptive signal processing in quantum technology advancements.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 15, 2024•1 citation

Quantized NN Workflow for Low-Latency Multi-Qubit State Discrimination on FPGA

Pradeep Kumar Gautam, Shantharam Kalipatnapu, H Shankaranarayanan +4 more

Measuring a qubit state is a fundamental but error-prone task in quantum computing. As the number of qubits increases, scalable readout demands multiplexed qubit measurement, which often suffers from crosstalk and other nonidealities. The conventional approach of qubit state detection often struggles with scalability and noise resilience. Neural network (NN)-based discriminators have emerged as a promising alternative, offering superior performance for inferring qubit states from multiplexed signals. However, their deployment on hardware platforms like Field programmable gate arrays (FPGA) is hindered by computation complexity, resource constraints, and latency. In this work, we present an end-to-end methodology for designing and deploying quantized NN-based multi-qubit state discriminators on FPGAs. We demonstrate that implementing a fully connected neural network accelerator for multi-qubit readout is advantageous, balancing computational complexity with low latency requirements without significant loss in accuracy. We significantly reduce the FPGA footprint and latency of the neural network by employing quantization-aware training (QAT) to quantize weights, activations, and inputs, and leveraging automated mapping flows for efficient, hardware-aware design-space exploration and optimization. The hardware accelerator performs frequency-multiplexed readout of five superconducting qubits in 26.7 ns, on a radio frequency system on chip (RFSoC) ZCU111 FPGA. These modules can be implemented and integrated into existing FPGA based quantum control and readout platforms, making them ready for experimental deployment.

International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies (HEART)•Jun 17, 2026•0 citations

An FPGA-based hardware abstraction of quantum computing systems

Madiha Khalid, Umar Mujahid, Atif Raza Jafri +2 more

Journal of Computational Electronics•Sep 23, 2021•12 citations
We present an end-to-end workflow for superconducting qubit readout that embeds co-designed Neural Networks (NNs) into the Quantum Instrumentation Control Kit (QICK). Capitalizing on the custom firmware and software of the QICK platform, which is built on Xilinx RFSoC FPGAs, we aim to leverage machine learning (ML) to address critical challenges in qubit readout accuracy and scalability. The workflow utilizes thehls4mlpackage and employs quantization-aware training to translate ML models into hardware-efficient FPGA implementations via user-friendly Python APIs. We experimentally demonstrate the design, optimization, and integration of an ML algorithm for single transmon qubit readout, achieving 96% single-shot fidelity with a latency of 32.25 ns and less than 16% FPGA look-up table resource utilization. Our results offer the community an accessible workflow to advance ML-driven readout and adaptive control in quantum information processing applications.
Advancements in quantum computing underscore the critical need for sophisticated qubit readout techniques to accurately discern quantum states. This abstract presents our research intended for optimizing readout pulse fidelity for 2D and 3D Quantum Processing Units (QPUs), the latter coupled with Superconducting Radio Frequency (SRF) cavities. Focusing specifically on the application of the Least Mean Squares (LMS) adaptive filtering algorithm, we explore its integration into the FPGA-based control systems to enhance the accuracy and efficiency of qubit state detection by improving Signal-to-Noise Ratio (SNR). Implementing the LMS algorithm on the Zynq UltraScale+ RFSoC Gen 3 devices (RFSoC 4x2 FPGA and ZCU216 FPGA) using the Quantum Instrumentation Control Kit (QICK) open-source platform, we aim to dynamically test and adjust the filtering parameters in real-time to characterize and adapt to the noise profile presented in quantum computing readout signals. Our preliminary results demonstrate the LMS filter's capability to maintain high readout accuracy while efficiently managing FPGA resources. These findings are expected to contribute to developing more reliable and scalable quantum computing architectures, highlighting the pivotal role of adaptive signal processing in quantum technology advancements.
Measuring a qubit state is a fundamental but error-prone task in quantum computing. As the number of qubits increases, scalable readout demands multiplexed qubit measurement, which often suffers from crosstalk and other nonidealities. The conventional approach of qubit state detection often struggles with scalability and noise resilience. Neural network (NN)-based discriminators have emerged as a promising alternative, offering superior performance for inferring qubit states from multiplexed signals. However, their deployment on hardware platforms like Field programmable gate arrays (FPGA) is hindered by computation complexity, resource constraints, and latency. In this work, we present an end-to-end methodology for designing and deploying quantized NN-based multi-qubit state discriminators on FPGAs. We demonstrate that implementing a fully connected neural network accelerator for multi-qubit readout is advantageous, balancing computational complexity with low latency requirements without significant loss in accuracy. We significantly reduce the FPGA footprint and latency of the neural network by employing quantization-aware training (QAT) to quantize weights, activations, and inputs, and leveraging automated mapping flows for efficient, hardware-aware design-space exploration and optimization. The hardware accelerator performs frequency-multiplexed readout of five superconducting qubits in 26.7 ns, on a radio frequency system on chip (RFSoC) ZCU111 FPGA. These modules can be implemented and integrated into existing FPGA based quantum control and readout platforms, making them ready for experimental deployment.