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DTSTAMP:20231019T120108Z
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DTSTART;TZID=America/New_York:20231012T110000
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DESCRIPTION:Remarkable progress has been achieved in deep learning-related 
 systems\nand applications. In the post-Moore’s Law era\, however\, the l
 imit of\nsemiconductor fabrication technology and the increasing data size
  have\nslowed down the development of learning algorithms. In parallel\, t
 he\nrapid development of quantum computing has pushed classical system\nde
 signs into a new era. We propose novel architectures with\nquantum-state-f
 idelity-based evaluation functions to train deep\nlearning models. The new
  designs encode the data with a reduced number\nof qubits and generate the
  quantum circuit\, pushing it to the quantum\nmachines for the best states
  iteratively. We conduct intensive\nexperiments on both quantum simulators
  and the IBM-Q quantum platform.\nAdditionally\, we compare IBM-Q results 
 with IonQ as they are the two\nleading qubit implementations for quantum i
 nformation processing at\nscale and have complementary characteristics. Fi
 nally\, we will discuss\nthe challenges and opportunities to develop quant
 um-based systems and\napplications further.\n\nCo-sponsored by: Electrical
  and Computer Engineering Department\, New Jersey Institute of Technology\
 n\nSpeaker(s): Dr. Ying Mao\, \n\nRoom: 202\, Bldg: ECEC Building\, 323 Dr
  Martin Luther King Jr Blvd\, New Jersey Institute of Technology\, Newark\
 , New Jersey\, United States\, 07102\, Virtual: https://events.vtools.ieee
 .org/m/376228
LOCATION:Room: 202\, Bldg: ECEC Building\, 323 Dr Martin Luther King Jr Blv
 d\, New Jersey Institute of Technology\, Newark\, New Jersey\, United Stat
 es\, 07102\, Virtual: https://events.vtools.ieee.org/m/376228
ORGANIZER:tao.han@njit.edu
SEQUENCE:16
SUMMARY: Deep Learning with Quantum State Fidelity and Beyond
URL;VALUE=URI:https://events.vtools.ieee.org/m/376228
X-ALT-DESC:Description: <br /><p>Remarkable progress has been achieved in d
 eep learning-related systems<br />and applications. In the post-Moore&rsqu
 o\;s Law era\, however\, the limit of<br />semiconductor fabrication techn
 ology and the increasing data size have<br />slowed down the development o
 f learning algorithms. In parallel\, the<br />rapid development of quantum
  computing has pushed classical system<br />designs into a new era. We pro
 pose novel architectures with<br />quantum-state-fidelity-based evaluation
  functions to train deep<br />learning models. The new designs encode the 
 data with a reduced number<br />of qubits and generate the quantum circuit
 \, pushing it to the quantum<br />machines for the best states iteratively
 . We conduct intensive<br />experiments on both quantum simulators and the
  IBM-Q quantum platform.<br />Additionally\, we compare IBM-Q results with
  IonQ as they are the two<br />leading qubit implementations for quantum i
 nformation processing at<br />scale and have complementary characteristics
 . Finally\, we will discuss<br />the challenges and opportunities to devel
 op quantum-based systems and<br />applications further.</p>
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