{"response":{"award":[{"abstractText":"Quantum information science (QIS), at the intersection of physics, mathematics, and computer science, is pioneering new frontiers in computing and communication, promising unprecedented capabilities. However, the multidisciplinary complexity of QIS demands expert knowledge in diverse areas, from cutting-edge experimental physics to theoretical computer science. This project develops an open-source ecosystem of modeling tools for quantum hardware that bridges the gap between these extremes. The project novelties are founded upon creating user-friendly tools that allow researchers to model quantum systems without mastering a vast array of underlying techniques. Among the project impacts are enabling a network scientist to test the performance of a networking protocol on realistic hardware models without having intricate knowledge of quantum simulation algorithms or, conversely, a hardware engineer to optimize their hardware sitting at the bottom of a full-stack network application modeled for them. As a whole, these tools will democratize access to quantum technology, foster innovation, and accelerate the growth of QIS.\r\n\r\n\r\nThe project revolves around the development of a symbolic algebra system for backend-agnostic modeling of quantum systems, capable of marshaling multiple backend simulators for different formalisms, seamlessly translating symbolic representations into numerics. The most appropriate simulation methods is automatically selected for each scenario. The investigators employ recent advances in scientific machine learning and auto-differentiation (even over discrete random functions) to provide the necessary foundation for constructing simulator systems of unprecedented sophistication. Emphasizing the importance of co-design, the project incorporates cutting-edge optimization and digital twin tooling, allowing for holistic optimization of quantum hardware and network dynamics.\r\n\r\nThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.","activeAwd":"true","agency":"NSF","awardAgencyCode":"4900","awardee":"UNIVERSITY OF MASSACHUSETTS","awardeeAddress":"101 COMMONWEALTH AVE","awardeeCity":"AMHERST","awardeeCountryCode":"US","awardeeDistrict":"02","awardeeDistrictCode":"MA02","awardeeName":"University of Massachusetts Amherst","awardeePhone":"4135450698","awardeeStateCode":"MA","awardeeZipCode":"01003","cfdaNumber":"47.070","coPDPI":["Donald F Towsley towsley@cs.umass.edu"],"date":"07/02/2024","dirAbbr":"CSE","divAbbr":"CNS","estimatedTotalAmt":"1149999","expDate":"06/30/2027","fundAgencyCode":"4900","fundProgramName":"CCRI-CISE Cmnty Rsrch Infrstrc","fundsObligated":["FY 2024 = $1,149,999.00"],"fundsObligatedAmt":"1149999","histAwd":"false","id":"2346089","initAmendmentDate":"07/02/2024","jrnl":[{"artTitl":"Optimization of quantum-repeater networks using stochastic automatic differentiation","auth":"Avis, Guus and Krastanov, Stefan","authIndCode":"N","dgtlObjId":"https://doi.org/10.1103/2g9m-h3jm","jrnlTitl":"Physical Review Research","jrnlYr":"2025","parPblcId":"10599728"}],"latestAmendmentDate":"07/02/2024","managingPec":"735900","orgCodeDir":"05000000","orgCodeDiv":"05050000","orgLongName":"Directorate for Computer and Information Science and Engineering","orgLongName2":"Division Of Computer and Network Systems","orgUrl":"http://www.nsf.gov/div/index.jsp?div=CNS","parentUeiNumber":"VGJHK59NMPK9","pdPIName":"Stefan Krastanov","perfAddress":"COMMONWEALTH AVE","perfCity":"AMHERST","perfCountryCode":"US","perfDistrict":"02","perfDistrictCode":"MA02","perfLocation":"University of Massachusetts Amherst","perfStateCode":"MA","perfZipCode":"010039243","pi":["Stefan Krastanov skrastanov@umass.edu"],"piEmail":"skrastanov@umass.edu","piFirstName":"Stefan","piLastName":"Krastanov","poEmail":"achtchel@nsf.gov","poName":"Almadena Chtchelkanova","poPhone":"7032927498","primaryProgram":["01002425DB NSF RESEARCH & RELATED ACTIVIT"],"progEleCode":"735900","progRefCode":"","program":"","publicAccessMandate":"1","publicationResearch":["Physical Review Research~2025~Avis, Guus and Krastanov, Stefan~https://doi.org/10.1103/2g9m-h3jm~Optimization of quantum-repeater networks using stochastic automatic differentiation~N~10599728~10599728~OSTI~2025-08-25 23:42:02.793"],"startDate":"07/01/2024","title":"Research Infrastructure: CIRC: New: Full-stack Codesign Tools for Quantum Hardware","transType":"Standard Grant","ueiNumber":"VGJHK59NMPK9"}],"metadata":{"offset":0,"rpp":25,"totalCount":1}}}