[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117744-en":3,"doc-seo-117744-105":29,"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":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},117744,1099513958607,"Jiven","https://ap-avatar.wpscdn.com/avatar/100002390cf8733938c?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778829742770036399",8,"Research & Report","Pulse-efficient quantum machine learning - Research report on PET for quantum machine learning near-term advantage","Pulse-efficient quantum machine learning investigates how hardware-native pulse-efficient transpilation (PET) affects near-term quantum machine learning algorithms built from parameterized quantum circuits. The study targets performance degradation caused by device noise, including exponential loss landscape flattening and noise-induced barren plateaus. Results across quantum neural network classification and quantum kernel estimation show PET greatly reduces average circuit durations and improves classification accuracy, then extends these benefits to Hamiltonian Variational Ansatz.","arXiv :2211 .01383v1 [ quant-ph] 2 Nov 2022  \nPulse-e􀀎cient quantum machine learning  \nAndr􀀓e Melo  \nKavli Institute of Nanoscience, Delft University of Technology, P. O. Box 4056, 2600 GA Delft, The Netherlands and  \nIBM Quantum, IBM Netherlands, Amsterdam, NH 1066 VH, The Netherlands 􀀃  \nNathan Earnest-Noble   \nIBM Quantum, IBM T. J. Watson Research Center, Yorktown Heights, New York 10598, USA  \nFrancesco Tacchino   \nIBM Quantum, IBM Research Europe { Zurich, 8803 R􀁿uschlikon, Switzerland  \nQuantum machine learning algorithms based on parameterized quantum circuits are promising candidates for near-term quantum advantage. Although these algorithms are compatible with the current generation of quantum processors, device noise limits their performance, for example by inducing an exponential 􀀍attening of loss landscapes. Error suppression schemes such as dynamical decoupling and Pauli twirling alleviate this issue by reducing noise at the hardware level. A recent addition to this toolbox of techniques is pulse-e􀀎cient transpilation, which reduces circuit schedule duration by exploiting hardware-native cross-resonance interaction. In this work, we investigate the impact of pulse-e􀀎cient circuits on near-term algorithms for quantum machine learning. We report results for two standard experiments: binary classi􀀌cation on a synthetic dataset with quantum neural networks and handwritten digit recognition with quantum kernel estimation. In both cases, we 􀀌nd that pulse-e􀀎cient transpilation vastly reduces average circuit durations and, as a result, signi􀀌cantly improves classi􀀌cation accuracy. We conclude by applying pulse-e􀀎cient transpilationto the Hamiltonian Variational Ansatz and show that it delays the onset of noise-induced barren plateaus.  \nI. INTRODUCTION  \nQuantum machine learning (QML) is a nascent area of research that has seen rapid developments over the last decade [1{4] . Initial works in the 􀀌eld focused on developing quantum versions of existing classical algorithms, thereby achieving asymptotically faster runtimes [5{7] . However, these algorithms are beyond the capabilities of current quantum hardware, as they require the execution of rather deep quantum circuits and often rely on speci􀀌c assumptions like quantum memories [8] to overcome bottlenecks in data loading and readout. More recently, the fast technological progress and wide availability of noisy quantum processors [9, 10] motivated the emergence of a second generation of QML algorithms based on parameterized quantum circuits (PQCs) [11{18] . In this alternative QML paradigm, quantum computers may function as a co-processor working in tandem with classical computers. Because the circuit ans􀁿atze can be shallow in depth, PQC-based algorithms are in principle compatible with the existing generation of quantum devices.  \nAn important obstacle to the viability of QML in the near term is the presence of hardware noise [19] . Coherent errors often simply shift the position of minimain the loss landscape, in which case they can be trained away [20] . In contrast, errors arising from incoherent noise have more adverse e􀀋ects that hinder trainability and performance. As an example, incoherent errors  \n􀀃 [am@andremelo.org](am@andremelo.org)  \ncause the loss function of a large family of ans􀁿atze to vanish exponentially with increasing number of layers, a phenomenon known as noise-induced barren plateaus (NIBP) [21] . A similar e􀀋ect occurs in kernel-based methods where noise can lead to an exponential concentration of the kernel values [22] .  \nA common approach to mitigate the e􀀋ects of device noise is to use protocols that estimate improved expectation values through classical post-processing, a procedure known as error mitigation [23{28] . While schemes such as zero-noise extrapolation [23, 24] and virtual distillation [25] can signi􀀌cantly enhance the performances of noisy processors [29], they also introduce additional experimental or computational overhead an","cbCaikIqhBoxN66t","https://ap.wps.com/l/cbCaikIqhBoxN66t","pdf",846640,1,"English","en",105,"# Introduction\n## Near-term QML and parameterized quantum circuits\n## Noise, trainability, and error mitigation\n## Pulse-efficient transpilation (PET) and its motivation\n# Methods and experimental tasks\n## Quantum neural networks on synthetic data\n## Quantum kernel estimation on MNIST\n## Impact on noise-induced barren plateaus\n# Conclusions","[{\"question\":\"What problem does pulse-efficient transpilation address in near-term quantum machine learning?\",\"answer\":\"Device noise limits the performance of parameterized quantum circuit algorithms by flattening loss landscapes and degrading trainability. PET reduces circuit schedule duration by exploiting hardware-native cross-resonance interactions.\"},{\"question\":\"How was PET evaluated in the reported study?\",\"answer\":\"The authors test PET on two standard QML experiments: binary classification using quantum neural networks on a synthetic dataset and handwritten digit recognition using quantum kernel estimation for MNIST.\"},{\"question\":\"What overall effects does PET have on algorithm performance and noise-induced barren plateaus?\",\"answer\":\"PET substantially reduces average circuit durations and improves classification accuracy in both experiments. Applied to the Hamiltonian Variational Ansatz, it delays the onset of noise-induced barren plateaus.\"}]","Pulse-efficient quantum machine learning - Research report on PET for quantum machine learning near-term advantage | PDF",1785679313,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":27},"pulse-efficient-quantum-machine-learning-research-report-on-pet-for-quantum-machine-learning-near-term-advantage","",{"@graph":35,"@context":85},[36,53,68],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/pulse-efficient-quantum-machine-learning-research-report-on-pet-for-quantum-machine-learning-near-term-advantage/117744/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-05","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does pulse-efficient transpilation address in near-term quantum machine learning?","Question",{"text":75,"@type":76},"Device noise limits the performance of parameterized quantum circuit algorithms by flattening loss landscapes and degrading trainability. PET reduces circuit schedule duration by exploiting hardware-native cross-resonance interactions.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was PET evaluated in the reported study?",{"text":80,"@type":76},"The authors test PET on two standard QML experiments: binary classification using quantum neural networks on a synthetic dataset and handwritten digit recognition using quantum kernel estimation for MNIST.",{"name":82,"@type":73,"acceptedAnswer":83},"What overall effects does PET have on algorithm performance and noise-induced barren plateaus?",{"text":84,"@type":76},"PET substantially reduces average circuit durations and improves classification accuracy in both experiments. Applied to the Hamiltonian Variational Ansatz, it delays the onset of noise-induced barren plateaus.","https://schema.org",{"og:url":51,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":45,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":45,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":45,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":125,"show_sort_weight":28,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":28,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]