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DTSTART;TZID=America/Toronto:20220907T120000
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URL:https://uwaterloo.ca/institute-for-quantum-computing/events/iqc-student
 -seminar-featuring-joan-arrow
LOCATION:QNC - Quantum Nano Centre 200 University Avenue West QNC 1201 Wate
 rloo ON N2L 3G1 Canada
SUMMARY:IQC Student Seminar featuring Joan Arrow
CLASS:PUBLIC
DESCRIPTION:ASSESSING THE TRAINABILITY OF THE VARIATIONAL QUANTUM STATE\nDI
 AGONALIZATION ALGORITHM AT SCALE\n\nDeveloping new quantum algorithms is a
  famously hard problem. The lack\nof intuition concerning the quantum real
 m makes constructing quantum\nalgorithms that solve particular problems of
  interest difficult. In\naddition\, modern hardware limitations place stro
 ng restrictions on the\ntypes of algorithms which can be implemented in no
 isy circuits. These\nchallenges have produced several solutions to the pro
 blem of quantum\nalgorithm development in the modern Near-term Intermediat
 e Scale\nQuantum (NISQ) Era. One of the most prominent of these is the use
  of\nclassical machine learning to discover novel quantum algorithms by\nm
 inimizing a cost function associated with the particular application\nof i
 nterest. This quantum-classical hybrid approach\, also called\nVariational
  Quantum Algorithms (VQAs)\, has attracted major interest\nfrom both acade
 mic and industrial researchers due to its flexible\nframework and expandin
 g list of applications - most notably\noptimization (QAOA) and chemistry (
 VQE). What is still unclear is\nwhether these algorithms will deliver on t
 heir promise when\nimplemented at a useful scale\, in fact there is strong
  reason to worry\nwhether the classical machine learning model will be abl
 e to train in\nthe larger parameter space. This phenomenon is commonly ref
 erred to as\nthe Barren Plateaus problem\, which occurs when the training 
 gradient\nvanishes exponentially quickly as the system size increases. Rec
 ent\nresults have shown that some cost functions used in training can be\n
 proven to result in a barren plateau\, while other cost functions can\nbe 
 proven to avoid them. In this presentation\, I apply these results\nto my 
 2018 paper where my group developed a new Variational Quantum\nState Diago
 nalization (VQSD) algorithm and so demonstrate that this\nalgorithm's cur
 rent cost function will encounter a Barren Plateau at\nscale. I then intro
 duce a simple modification to this cost function\nwhich preserves its func
 tion while ensuring trainability at scale. I\nalso discuss the next steps 
 for this project where I am teaching a\nteam of 6 quantum novices across 4
  continents the core calculation I\nuse in this work to expand my analysis
  to the entire literature of\nVQAs.\n\nReference: https://uwspace.uwaterlo
 o.ca/handle/10012/18187
DTSTAMP:20260408T080358Z
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