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

IEEE International Conference on Quantum Computing and Engineering (QCE)

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, D. M. T. van Zanten, Silvia Zorzetti, Jafar Saniie

Published Sep 15, 2024• 1 citation
Publisher

Abstract

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.

DOI
10.1109/qce60285.2024.00156

Related papers

Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware

Hans Johnson, Silvia Zorzetti, Jafar Saniie

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the noise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.

IEEE International Conference on Quantum Computing and Engineering (QCE)•Sep 17, 2023•1 citation

Improving Signal-to-Noise Ratio (SNR) for Readout Signals Using Adaptive Filters on Reconfigurable Controls Hardware

Hans Johnson, Silvia Zorzetti, Jafar Saniie

This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the n oise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.

•Nov 6, 2023•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

Current Methodology• Readout pulses are very noisy!• The current method to average readout pulses in post processing for qubit readout works for smaller systems.However, this has limitations as qubit systems scale up.100,000 averages Phase Sweep @ ~6.3 GHz for single-shot time trace readout Ground State Excited State

arXiv:2408.00904•Sep 26, 2024•1 citation

Real-Time Adaptive Filtering and the Boxcar Limit in Superconducting Qubit Readout

Hans Johnson, Tanay Roy, Leonardo Bove +3 more

Control and readout systems for superconducting quantum computing support real-time control and high-fidelity qubit-state discrimination. We examine when additional digital processing improves readout fidelity. A common baseline for dispersive readout uses a boxcar averager to compute a uniformly weighted average over a fixed window followed by threshold-based state assignment. We evaluated an adaptive finite-impulse-response (FIR) filter trained in real time using the least-mean-squares (LMS) algorithm on a field-programmable gate array (FPGA) quantum controller. We also analyzed measured readout shots offline to compare the frozen filter with boxcar averaging, and evaluate per-sample weighting plus sequential detection. For the fixed FIR filters tested, summing the filtered samples gives the boxcar sum multiplied by one complex number apart from a small edge correction. We call this the kernel collapse. The multiplication scales the state separation and noise equally without improving state discrimination, and we call this the boxcar limit. The frozen LMS filter reduces trace noise, but shows no resolved improvement in readout fidelity. On a 2D transmon, optimizing the readout window accounted for much of the improvement over full-window integration. We then examined how changing the decision method could reduce calibration effort and decision time. On a higher-fidelity 3D ancilla readout, our modeled template estimation reached the same fidelity target as direct averaging with 89.8% fewer labeled shots per state. Offline sequential replay assigned most shots before the acquisition window ended. These results distinguish waveform denoising from state-discrimination improvement, and motivate direct control of decision weights and assignment time in dispersive readout.

quant-pharXiv:2610.00783•Oct 2, 2026•0 citations

Superconducting qubit readout and control system based on FPGA and development of a pulse sequencer

BOVE, LEONARDO

This thesis presents the development of a superconducting qubit control and readout system based on FPGA technology, with a focus on the design of a pulse sequencer and the integration of frequency-multiplexed control. The system leverages the Quantum Instrumentation Control Kit (QICK), an open-source Xilinx RFSoC-based platform capable of direct digital synthesis and acquisition of microwave signals up to 10 GHz. The QICK firmware was extended to support the simultaneous generation and readout of multiple frequency-multiplexed tones, enabling the control and measurement of several qubits coupled to a shared feedline. This approach optimizes the number of controllable qubits relative to the available ADC and DAC resources on the RFSoC 4x2 board. Furthermore, this work includes the development of Qubase, a Python-based pulse sequencer that allows users to easily define and execute complex sequences of control and measurement pulses through the QICK Python interface. The resulting architecture is both flexible and scalable, providing a foundation for efficient multi-qubit experiments and enabling advanced techniques such as quantum error correction and real-time feedback.

•Sep 27, 2025•0 citations

Improving Signal-to-Noise Ratio (SNR) for Quantum Readout Signals Using Adaptive Hardware Filters on FPGA

Hans Johnson, Silvia Zorzetti, Jafar Saniie

This research presents an innovative approach to improving the Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals, utilizing FPGA-based adaptive filtering on the control board. Our solution aims to bolster quantum computations' reliability by providing a more accurate and precise way to obtain our readout traces and achieve a higher discernability between excitation states in real-time.

•Sep 8, 2023•0 citations
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.
This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the noise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.
This study investigates the optimization of Signal-to-Noise Ratio (SNR) in superconducting quantum computing readout signals through adaptive filtering. Quantum computing technology has the potential to revolutionize various fields by delivering exponential speedup in solving certain computational problems. However, the technology's practical implementation is hindered by the difficulty of extracting clean, reliable signals during the readout phase, with various sources of noise presenting a significant barrier to clean signals. This noise, often present in readout profiles due to imperfect isolation, degrades the system's overall SNR, thus impeding the ability to extract the quantum state accurately. The research leverages the power of adaptive filtering to improve the SNR of quantum computing readout signals. Specifically, an adaptive filter is implemented in a PYNQ overlay on an FPGA, and eventually will be connected to a quantum computing system. The system models the n oise with a Least Mean Squares (LMS) adaptive filter, and then subtracts the estimated noise from the received signal to improve the SNR. A Direct Memory Access (DMA) channel is used to handle the signal processing, delivering efficient, high-speed data transfer between the PYNQ system and the hardware. The study explores the benefits of this adaptive filtering technique, potentially providing a significant contribution to practical and fast quantum computing.
Control and readout systems for superconducting quantum computing support real-time control and high-fidelity qubit-state discrimination. We examine when additional digital processing improves readout fidelity. A common baseline for dispersive readout uses a boxcar averager to compute a uniformly weighted average over a fixed window followed by threshold-based state assignment. We evaluated an adaptive finite-impulse-response (FIR) filter trained in real time using the least-mean-squares (LMS) algorithm on a field-programmable gate array (FPGA) quantum controller. We also analyzed measured readout shots offline to compare the frozen filter with boxcar averaging, and evaluate per-sample weighting plus sequential detection. For the fixed FIR filters tested, summing the filtered samples gives the boxcar sum multiplied by one complex number apart from a small edge correction. We call this the kernel collapse. The multiplication scales the state separation and noise equally without improving state discrimination, and we call this the boxcar limit. The frozen LMS filter reduces trace noise, but shows no resolved improvement in readout fidelity. On a 2D transmon, optimizing the readout window accounted for much of the improvement over full-window integration. We then examined how changing the decision method could reduce calibration effort and decision time. On a higher-fidelity 3D ancilla readout, our modeled template estimation reached the same fidelity target as direct averaging with 89.8% fewer labeled shots per state. Offline sequential replay assigned most shots before the acquisition window ended. These results distinguish waveform denoising from state-discrimination improvement, and motivate direct control of decision weights and assignment time in dispersive readout.
This thesis presents the development of a superconducting qubit control and readout system based on FPGA technology, with a focus on the design of a pulse sequencer and the integration of frequency-multiplexed control. The system leverages the Quantum Instrumentation Control Kit (QICK), an open-source Xilinx RFSoC-based platform capable of direct digital synthesis and acquisition of microwave signals up to 10 GHz. The QICK firmware was extended to support the simultaneous generation and readout of multiple frequency-multiplexed tones, enabling the control and measurement of several qubits coupled to a shared feedline. This approach optimizes the number of controllable qubits relative to the available ADC and DAC resources on the RFSoC 4x2 board. Furthermore, this work includes the development of Qubase, a Python-based pulse sequencer that allows users to easily define and execute complex sequences of control and measurement pulses through the QICK Python interface. The resulting architecture is both flexible and scalable, providing a foundation for efficient multi-qubit experiments and enabling advanced techniques such as quantum error correction and real-time feedback.