[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124619-en":3,"doc-seo-124619-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124619,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",8,"Research & Report","Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures - readout","Reading a qubit converts quantum information into classical bits ('0'/'1'), yet superconducting-qubit readout remains among the most error-prone and slowest steps. On leading processors, readout errors often reach roughly 1–10% due to crosstalk, spontaneous transitions, and readout-pulse–induced excitations, motivating more accurate discriminators for both near-term high-fidelity operation and future error-corrected systems. The work introduces herqles, a scalable hierarchy-of-matched-filters scheme with a smaller neural network that runs efficiently on off-the-shelf FPGAs, improving readout accuracy by 16.4% relative to the baseline.","Scaling Qubit Readout with Hardware Efficient Machine  \nLearning Architectures  \nSatvik Maurya  \n[smaurya@wisc.edu](smaurya@wisc.edu)[ ](smaurya@wisc.edu)University of Wisconsin-Madison Madison, WI, USA  \nChaithanya Naik Mude  \n[cmude@wisc.edu](cmude@wisc.edu)[ ](cmude@wisc.edu)University of Wisconsin-Madison Madison, WI, USA  \nWilliam D. Oliver  \n[william.oliver@mit.edu](william.oliver@mit.edu)[ ](william.oliver@mit.edu)Massachusetts Institute of Technology Cambridge, MA, USA  \narXiv :2212 .03895v2 [ quant-ph] 17 Jun 2023  \nBenjamin Lienhard  \n[blienhard@princeton.edu](blienhard@princeton.edu)[ ](blienhard@princeton.edu)Princeton University Princeton, NJ, USA  \nSwamit Tannu  \n[swamit@cs.wisc.edu](swamit@cs.wisc.edu)[ ](swamit@cs.wisc.edu)University of Wisconsin-Madison Madison, WI, USA  \nABSTRACT  \nReading a qubit is a fundamental operation in quantum computing. It translates quantum information into classical information enabling subsequent classification to assign the qubit states ‘0’ or‘1’. Unfortunately, qubit readout is one of the most error-prone and slowest operations on a superconducting quantum processor. On state-of-the-art superconducting quantum processors, readout errors can range from 1-10% . These errors occur for various reasons – crosstalk, spontaneous state transitions, and excitation caused by the readout pulse. The error-prone nature of readout has resulted in significant research to design better discriminators to achieve higher qubit-readout accuracies. High readout accuracy is essential for enabling high fidelity for near-term noisy quantum computers and error-corrected quantum computers of the future.  \nPrior works have used machine-learning-assisted single-shot qubit-state classification, where a deep neural network was used for more robust discrimination by compensating for crosstalk errors. However, the neural network size can limit the scalability of systems, especially if fast hardware discrimination is required. This state-of-the-art baseline design cannot be implemented on off-theshelf FPGAs used for the control and readout of superconducting qubits in most systems, which increases the overall readout latency as discrimination has to be performed in software.  \nIn this work, we propose herqles, a scalable approach to improve qubit-state discrimination by using a hierarchy of matched filters in conjunction with a significantly smaller and scalable neural network for qubit-state discrimination. We achieve substantially higher readout accuracies (16.4% relative improvement) than the baseline with a scalable design that can be readily implemented on off-the-shelf FPGAs. We also show that herqles is more versatile and can support shorter readout durations than the baseline design without additional training overheads.  \n1 INTRODUCTION  \nQuantum computers leverage fundamental quantum-mechanical properties of their constituent quantum bits (qubits) to gain a computational advantage for a specific class of complex problems. Today, scientists and engineers are racing to build larger, more-reliable quantum computers and demonstrate their effectiveness at evaluating increasingly complex quantum algorithms. Quantum hardware has two primary components: Qubits, which hold the quantum  \ninformation, and a control computer that manipulates this information to orchestrate the execution of quantum programs. The control computer is further divided into a qubit readout and control pipeline. The control pipeline sends precise gate pulses to qubits, whereas the readout pipeline measures the qubits. Control and readout are performed on existing quantum computers with hundreds of qubits using FPGAs and signal generators.  \nReadout is a fundamental operation in quantum computing. It converts quantum information into classical information represented by the computational space (‘0’ and ‘1’) . Readout of superconducting qubits involves three stages –(1) query the qubit state using a readout pulse,(2) acquire the response ","cbCairdWyu1siOnN","https://ap.wps.com/l/cbCairdWyu1siOnN","pdf",8512125,1,13,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Why is qubit readout considered error-prone and slow in superconducting processors?\",\"answer\":\"Qubit readout translates quantum information into classical states and is vulnerable to crosstalk, spontaneous state transitions, and excitations caused by the readout pulse. It is also one of the slowest operations, with typical durations over hundreds of nanoseconds.\"},{\"question\":\"What limitation affects neural-network-based single-shot discriminators in practice?\",\"answer\":\"Neural network size can hinder scalability, especially when fast hardware discrimination is needed. The baseline design cannot run directly on off-the-shelf FPGAs, forcing software discrimination and increasing readout latency.\"},{\"question\":\"What approach does herqles use to improve scalable qubit-state discrimination?\",\"answer\":\"herqles combines a hierarchy of matched filters with a significantly smaller, scalable neural network for qubit-state discrimination. It targets FPGA implementation and achieves a 16.4% relative improvement in readout accuracy over the baseline.\"}]","Scaling Qubit Readout with Hardware Efficient Machine Learning Architectures - readout | PDF",1785893352,33,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"scaling-qubit-readout-with-hardware-efficient-machine-learning-architectures-readout","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/scaling-qubit-readout-with-hardware-efficient-machine-learning-architectures-readout/124619/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is qubit readout considered error-prone and slow in superconducting processors?","Question",{"text":75,"@type":76},"Qubit readout translates quantum information into classical states and is vulnerable to crosstalk, spontaneous state transitions, and excitations caused by the readout pulse. It is also one of the slowest operations, with typical durations over hundreds of nanoseconds.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What limitation affects neural-network-based single-shot discriminators in practice?",{"text":80,"@type":76},"Neural network size can hinder scalability, especially when fast hardware discrimination is needed. The baseline design cannot run directly on off-the-shelf FPGAs, forcing software discrimination and increasing readout latency.",{"name":82,"@type":73,"acceptedAnswer":83},"What approach does herqles use to improve scalable qubit-state discrimination?",{"text":84,"@type":76},"herqles combines a hierarchy of matched filters with a significantly smaller, scalable neural network for qubit-state discrimination. It targets FPGA implementation and achieves a 16.4% relative improvement in readout accuracy over the baseline.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]