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CATEGORIES:Utilities
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20250902T132652Z
DESCRIPTION:The Duke Quantum Center\, the IBM Quantum Innovation Center at
  NC State\, and the UNC Kenan-Flagler's Rethinc. Labs are pleased to pres
 ent the first Fall Semester Triangle Quantum Computing Seminar on Septemb
 er 5\, 2025 at 2 p.m.\n\nThe UNC Kenan-Flagler Rethinc. Labs welcomes Jac
 k Morgan\, to give a talk on "Improving Quantum Recurrent Neural Networks
  with Amplitude Encoding."\n---\n\nAbstract: Quantum machine learning hol
 ds promise for advancing time series forecasting. The Quantum Recurrent N
 eural Network (QRNN)\, inspired by classical RNNs\, encodes temporal data
  into quantum states that are periodically input into a quantum circuit. 
 While prior QRNN work has predominantly used angle encoding\, alternative
  encoding strategies like amplitude encoding remain underexplored due to 
 their high computational complexity. In this paper\, we evaluate and impr
 ove amplitude-based QRNNs using EnQode\, a recently introduced method for
  approximate amplitude encoding. We propose a simple pre-processing techn
 ique that enhances amplitude encoded inputs with their pre-normalized mag
 nitudes\, leading to improved generalization on two real world data sets.
  Additionally\, we introduce a novel circuit architecture for the QRNN th
 at is mathematically equivalent to the original model but achieves a subs
 tantial reduction in circuit depth. Together\, these contributions demons
 trate practical improvements to QRNN design in both model performance and
  quantum resource efficiency.\n---\nJack Morgan is a master's student in 
 Applied Data Science at the University of Chicago. He previously earned h
 is B.S. in Physics from Haverford College. For three years he was a Quant
 um Research Fellow at the Kenan Institute of Private Enterprise\, where h
 e developed hybrid quantum-classical algorithms for finance applications.
  He has authored publications on quantum linear system solvers\, asset pr
 icing\, and credit risk modeling on quantum processors. His research inte
 rests include quantum machine learning\, hybrid quantum algorithms\, and 
 statistical methods for finance. \n\n---\nThese weekly seminars are virtu
 al. Contact quantumcomputing@ncsu.edu with any questions.
DURATION:PT1H
DTSTAMP:20250902T193937Z
DTSTART;TZID=America/New_York:20250905T140000
LAST-MODIFIED:20250902T193937Z
LOCATION;X-BEDEWORK-UID=2c918085-71ed832f-0172-098647fa-0000767f:Virtual
STATUS:CONFIRMED
SUMMARY:Triangle Quantum Computing Seminar Series: Improving Quantum Recur
 rent Neural Networks with Amplitude Encoding
UID:CAL-8a00ec8b-979413b9-0199-0a9be435-0000059ademobedework@mysite.edu
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 u:NC State University Quantum Computing
X-BEDEWORK-SPEAKER:Jack Morgan\, Applied Data Science Master's student\, U
 niversity of Chicago
X-BEDEWORK-DUKE-SERIES:Triangle Quantum Computing Seminar Series
X-BEDEWORK-DUKE-WEBCAST:https://ncsu.zoom.us/j/99055056450?pwd=aNYY7wbrBY0
 aibayn5WJXdPAwYa6go.1#success
X-BEDEWORK-SUBMITTEDBY:mfg20 for Duke Quantum Center (agrp_DQC)
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