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DTSTART:19700308T020000
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DTSTAMP:20230124T171522Z
LOCATION:C140-142
DTSTART;TZID=America/Chicago:20221113T113000
DTEND;TZID=America/Chicago:20221113T115000
UID:submissions.supercomputing.org_SC22_sess423_ws_qcs118@linklings.com
SUMMARY:Predicting the Optimizability for Workflow Decisions
DESCRIPTION:Workshop\n\nPredicting the Optimizability for Workflow Decisio
 ns\n\nMete, Ruefenacht, Schulz\n\nWith the rapid advancement of quantum te
 chnologies, the integration between classical and quantum computing system
 s is an active area of research critical to future development. The coupli
 ng between these systems requires both to be as efficient as possible. One
  of the key elements to increase efficiency on the quantum side is circuit
  optimization. The goal is to execute the circuit on the desired hardware 
 in less time and with less complexity, thereby reducing the impact of nois
 e on the quantum system. However, the optimization process does not guaran
 tee to generate improved results, yet it is always a computationally highl
 y complex task that can create significant load for the classical computin
 g side. To mitigate this problem, we propose a novel approach to predict t
 he optimizability of any circuit using a Machine Learning-based algorithm 
 within the decision workflow. This optimizes the most suitable circuits th
 ereby increasing efficiency of the optimization process itself.\n\nSession
  Format: Recorded\n\nTag: Quantum Computing\n\nRegistration Category: Work
 shop Reg Pass
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