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

Hans Johnson, Silvia Zorzetti, Jafar Saniie

Published Sep 8, 2023• 0 citations
Publisher

Abstract

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.

DOI
10.2172/1998908

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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.
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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.
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.
In the context of rapid technological advancements, progress in chip manufacturing technology is propelling electronic devices toward miniaturization and high efficiency at an unprecedented pace. The fundamental principles of quantum mechanics are beginning to play a crucial role in modern electronic devices, providing the foundation for new technological methods and research directions. Optical quantum signal processing, as an important branch of quantum technology, utilizes the quantum properties of photons for information processing and transmission, offering advantages such as high bandwidth, low noise, and strong resistance to interference. However, current optical quantum measurement and control instruments face significant challenges in terms of real-time performance and programming complexity, resulting in reduced data processing efficiency and significantly increased development costs. This study implements a real-time processing algorithm for optical quantum signals on a Field-Programmable Gate Array (FPGA). The algorithm possesses advantages of real-time performance, low power consumption, and high integration. It allows for rapid functionality switching and efficient optical quantum signal processing. The design specifically aims to enhance the signal-to-noise ratio, significantly improving measurement accuracy and reliability. Experimental results indicate that this method substantially enhances the measurement precision and signal-to-noise ratio of optical quantum signals. This research provides an efficient and reliable new method for optical quantum signal processing, laying a solid foundation for further studies in quantum communication, quantum computing, and quantum precision measurement.