BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD DTSTART:19451014T230000 TZOFFSETFROM:+0630 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20261002T133304Z UID:2D9AE072-6B3F-4D12-A687-006F04B54021 DTSTART;TZID=Asia/Kolkata:20261002T173000 DTEND;TZID=Asia/Kolkata:20261002T183000 DESCRIPTION:The rapid growth of data-intensive applications demands advance d computational tools capable of processing complex information\nefficient ly. Quantum computing and machine learning (ML) are emerging paradigms wit h significant potential for next-generation information\nprocessing. Quant um computing exploits quantum principles such as superposition\, entanglem ent\, and interference to perform computations\nbeyond the capabilities of conventional approaches for selected problems. Machine learning enables a utomated extraction of patterns\,\nrepresentations\, and predictions from large and complex datasets. Their integration has given rise to quantum ma chine learning\, which explores\nquantum-enhanced methods for learning and intelligent information processing. This talk will provide an overview of important quantum\ncomputing and ML tools with emphasis on hybrid quantum -classical architectures that combine quantum processors with classical ML \nalgorithms for practical applications. These approaches are explored for data representation\, feature extraction\, classification\, optimization\ , and\ndecision-making in domains such as healthcare\, signal processing\, communications\, and intelligent sensing. The talk also discusses key cha llenges\nand emerging opportunities for future information processing appl ications.\n\nSpeaker(s): Priya Ranjan\n\nVirtual: https://events.vtools.ie ee.org/m/580107 LOCATION:Virtual: https://events.vtools.ieee.org/m/580107 ORGANIZER:manirban@ieee.org SEQUENCE:13 SUMMARY:Quantum Computing and Machine Learning Tools for Information Proces sing URL;VALUE=URI:https://events.vtools.ieee.org/m/580107 X-ALT-DESC:Description: <br /><p style="text-align: justify\;">The rapid gr owth of data-intensive applications demands advanced computational tools c apable of processing complex information<br>efficiently. Quantum computing and machine learning (ML) are emerging paradigms with significant potenti al for next-generation information<br>processing. Quantum computing exploi ts quantum principles such as superposition\, entanglement\, and interfere nce to perform computations<br>beyond the capabilities of conventional app roaches for selected problems. Machine learning enables automated extracti on of patterns\,<br>representations\, and predictions from large and compl ex datasets. Their integration has given rise to quantum machine learning\ , which explores<br>quantum-enhanced methods for learning and intelligent information processing. This talk will provide an overview of important qu antum<br>computing and ML tools with emphasis on hybrid quantum-classical architectures that combine quantum processors with classical ML<br>algorit hms for practical applications. These approaches are explored for data rep resentation\, feature extraction\, classification\, optimization\, and<br> decision-making in domains such as healthcare\, signal processing\, commun ications\, and intelligent sensing. The talk also discusses key challenges <br>and emerging opportunities for future information processing applicati ons.</p> END:VEVENT END:VCALENDAR