[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118143-en":3,"doc-seo-118143-105":29,"detail-sidebar-cat-0-en-105":90},{"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":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},118143,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",8,"Research & Report","Photonic probabilistic machine learning using quantum vacuum noise","Probabilistic machine learning encodes uncertainty using controllable randomness, and this work harnesses quantum vacuum noise from fluctuating electromagnetic fields to power stochastic photonic elements. It implements a photonic probabilistic computer via a programmable photonic probabilistic neuron using a bistable optical parametric oscillator with vacuum-level injected bias fields. A measurement-and-feedback loop with time-multiplexed PPNs and electronic processors programs probabilistic inference and generative models, demonstrating MNIST digit inference and pixelCNN generation.","arXiv :2403 .04731v1 [physics .optics] 7 Mar 2024  \nPhotonic probabilistic machine learning using quantum vacuum noise  \nSeou Choi 1 ,∗ Yannick Salamin 1 ,2 , Charles Roques-Carmes 1 ,3 ,† Rumen Dangovski 1 ,4 , Di Luo4 ,5 ,6 , Zhuo Chen2 ,4 , Michael Horodynski2 , Jamison Sloan 1 , Shiekh Zia Uddin 1 ,2 , and Marin Soljai1 ,2  \n1 Research Laboratory of Electronics, MIT, Cambridge MA USA  \n2 Department of Physics, MIT, Cambridge MA USA  \n3 E. L. Ginzton Laboratories, Stanford University, 348 Via Pueblo, Stanford, CA USA  \n4 The NSF AI Institute for Artificial Intelligence and Fundamental Interactions, Cambridge, MA 02139, USA  \n5 Center for Theoretical Physics, Massachusetts Institute of Technology,  \nCambridge, MA 02139, USA and  \n6 Department of Physics, Harvard University, Cambridge, MA 02138, USA  \nProbabilistic machine learning utilizes controllable sources of randomness to encode uncertainty and enable statistical modeling. Harnessing the pure randomness of quantum vacuum noise, which stems from fluctuating electromagnetic fields, has shown promise for high speed and energy-efficient stochastic photonic elements. Nevertheless, photonic computing hardware which can control these stochastic elements to program probabilistic machine learning algorithms has been limited. Here, we implement a photonic probabilistic computer consisting of a controllable stochastic photonic element – a photonic probabilistic neuron (PPN) . Our PPN is implemented in a bistable optical parametric oscillator (OPO) with vacuum-level injected bias fields. We then program a measurement-and-feedback loop for time-multiplexed PPNs with electronic processors (FPGA or GPU) to solve certain probabilistic machine learning tasks. We showcase probabilistic inference and image generation of MNIST-handwritten digits, which are representative examples of discriminative and generative models. In both implementations, quantum vacuum noise is used as a random seed to encode classification uncertainty or probabilistic generation of samples. In addition, we propose a path towards an all-optical probabilistic computing platform, with an estimated sampling rate of ∼ 1 Gbps and energy consumption of ∼ 5 fJ/MAC. Our work paves the way for scalable, ultrafast, and energy-efficient probabilistic machine learning hardware.  \nProbabilistic machine learning can accelerate image generation [1, 2], heuristic optimization [3, 4], and probabilistic inference [5, 6] by leveraging stochasticity to encode uncertainty and enable statistical modeling [7, 8] . These approaches are well suited for real-life applications which must account for uncertainty and variability, including autonomous driving [9], medical diagnosis [10], and drug discovery [11] . However, digital complementary metal-oxide-semiconductor (CMOS) technology requires extensive resource overhead to simulate randomness and control probabilities, which leads to significantly increased power consumption and decreased operational speed [12] . These challenges have sparked recent proposals for beyond-CMOS hardware such as low-barrier magnetic tunnel junctions [13] and diffusive memristors [14]  \n—both of which leverage intrinsic noise as a source of randomness.  \nConcurrently, optical neural networks (ONNs) [15, 16] have shown remarkable progress in energy efficiency [17, 18], speed [19] and bandwidth [20] for solving deterministic tasks such as image classification [21] and speech recognition [22] . Another important feature of ONNs is the inherent presence of noise in their operation. Therefore, photonic computing hardware typically implements computational tasks that are robust to optical noise [16] . ONNs have also been explored in regimes where deterministic tasks are performed with high accuracy, despite the presence of high levels of inherent noise [18] . Conversely, ONNs in which optoelectronic noise is intentionally added have also been proposed for optimization [23] and generative networks [24] . Meanwhile,  \nopt","cbCaimvO6KUsJbQR","https://ap.wps.com/l/cbCaimvO6KUsJbQR","pdf",3480070,1,"English","en",105,"# Introduction\n## Motivation: probabilistic ML and quantum randomness\n## Limits of beyond-CMOS implementations\n# Experimental platform\n## Photonic probabilistic neuron and OPO implementation\n## Measurement-and-feedback programming loop\n# Demonstrations and outlook\n## MNIST probabilistic inference\n## MNIST generation with pixelCNN\n## Toward all-optical probabilistic computing","[{\"question\":\"How does quantum vacuum noise support probabilistic machine learning in this work?\",\"answer\":\"Quantum vacuum noise provides a physical random seed derived from fluctuating electromagnetic fields. In the system, it encodes classification uncertainty or statistical sampling for generated outputs.\"},{\"question\":\"What hardware element implements the photonic probabilistic neuron (PPN)?\",\"answer\":\"The PPN is realized using a bistable optical parametric oscillator (OPO) with vacuum-level injected bias fields, forming a controllable stochastic photonic element.\"},{\"question\":\"What tasks are demonstrated to validate the approach?\",\"answer\":\"The paper demonstrates probabilistic inference of MNIST handwritten digits using a stochastic binary neural network and showcases MNIST digit generation using a pixelCNN-style generative model.\"}]","Photonic probabilistic machine learning using quantum vacuum noise | PDF",1785681863,20,{"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":85,"head_meta":87,"extra_data":89,"updated_unix":27},"photonic-probabilistic-machine-learning-using-quantum-vacuum-noise","",{"@graph":35,"@context":84},[36,53,67],{"@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/photonic-probabilistic-machine-learning-using-quantum-vacuum-noise/118143/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"How does quantum vacuum noise support probabilistic machine learning in this work?","Question",{"text":74,"@type":75},"Quantum vacuum noise provides a physical random seed derived from fluctuating electromagnetic fields. In the system, it encodes classification uncertainty or statistical sampling for generated outputs.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"What hardware element implements the photonic probabilistic neuron (PPN)?",{"text":79,"@type":75},"The PPN is realized using a bistable optical parametric oscillator (OPO) with vacuum-level injected bias fields, forming a controllable stochastic photonic element.",{"name":81,"@type":72,"acceptedAnswer":82},"What tasks are demonstrated to validate the approach?",{"text":83,"@type":75},"The paper demonstrates probabilistic inference of MNIST handwritten digits using a stochastic binary neural network and showcases MNIST digit generation using a pixelCNN-style generative model.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]