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DTSTART:20240718T080000Z
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TRANSP:OPAQUE
LOCATION:ICFO Auditorium
SUMMARY:ICFO | AIKATERINI GRATSEA
CLASS:PUBLIC
DESCRIPTION:In this thesis\, I focused on introducing tools to quantify the
  performance of quantum computing algorithms and their applications. The m
 ain focus is on two of the most popular application areas of quantum compu
 ting\, quantum machine learning and quantum chemistry. To this end\, I ana
 lyze the properties of quantum machine learning models by following statis
 tical method techniques\, which can help us build our understanding of the
  capabilities of such quantum models. Moreover\, I introduce the teacher-s
 tudent scheme as a computational tool to benchmark the performance of diff
 erent quantum models and their training capabilities. Until large-scale be
 nchmarking is available\, these tools can help us understand the potential
  of quantum machine learning and guide the research in the right direction
 . Next\, in recent years substantial effort have been devoted to the devel
 opment of quantum algorithms for quantum chemistry applications. I introdu
 ce tools to assess the utility of various combinations of quantum chemistr
 y algorithms. I perform extensive numerical simulations on computationally
  affordable systems of intermediate size to explore how quantum methods ca
 n accelerate tasks of quantum chemistry. These works set a foundation from
  which to further explore the requirements to achieve quantum advantage in
  quantum chemistry. Finally\, I discuss how research in quantum computing 
 has tended to fall into one of two camps: near-term intermediate scale qua
 ntum (NISQ) and fault-tolerant quantum computing (FTQC). Through a quantum
  chemistry application\, I explore how to use quantum computers in transit
 ion between these two eras\, namely the early fault-tolerant quantum compu
 ting (EFTQC) regime.\n&nbsp\;\nThursday July 18\, 10:00 h. ICFO Auditorium
  and Online (Teams)\nThesis Director: Prof. Dr. Maciej Lewenstein and Dr. 
 Patrick H&uuml\;mbeli
DTSTAMP:20260407T072317Z
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