BEGIN:VCALENDAR VERSION:2.0 PRODID:researchseminars.org CALSCALE:GREGORIAN X-WR-CALNAME:researchseminars.org BEGIN:VEVENT SUMMARY:Kunal Sharma (University of Maryland) DTSTART:20211014T000000Z DTEND:20211014T010000Z DTSTAMP:20260423T024831Z UID:UTSQSI/35 DESCRIPTION:Title: Trainability of Parameterized Quantum Circuits\nby Kunal Sharma (Unive rsity of Maryland) as part of Centre for Quantum Software and Information Seminar Series\n\n\nAbstract\nVariational quantum algorithms (VQAs) and Qu antum Neural Networks (QNNs) have emerged as promising strategies to achie ve quantum advantage on near-term quantum devices. The success of VQAs/QNN s depends on several factors\, including the trainability and expressibili ty of parameterized quantum circuits (PQCs). Along with numerical experime nts\, rigorous analytical results are necessary to guarantee the scalabili ty of these algorithms.\nIn this seminar\, I will first summarize well-kno wn results on barren plateaus\, where certain circumstances lead to expone ntially vanishing gradients. Then I will present our recent results establ ishing a fundamental relationship between expressibility and trainability of PQCs. Next\, I will outline our analysis on the trainability of percept ron-based QNNs. One common assumption to avoid barren plateaus is to empl oy problem-inspired PQCs. I will present that for problem-inspired PQCs\, such as Quantum Alternating Operator Ansatz (QAOA) and Hamiltonian Variati onal Ansatz (HVA)\, trainability depends on the controllability of the sys tem and is not always guaranteed. Finally\, I will describe the effect of hardware noise on the training landscape for a generic PQC and summarize p otential strategies to avoid trainability issues.\n\nTo request the zoom l ink\, please send a message cqsiadmin@uts.edu.au using your business/organ isation/institution email address.\n LOCATION:https://researchseminars.org/talk/UTSQSI/35/ END:VEVENT END:VCALENDAR