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DTSTART:19700308T020000
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DTSTAMP:20230124T171524Z
LOCATION:C140-142
DTSTART;TZID=America/Chicago:20221113T153000
DTEND;TZID=America/Chicago:20221113T155000
UID:submissions.supercomputing.org_SC22_sess423_ws_qcs125@linklings.com
SUMMARY:Exploiting In-Constraint Energy in Constrained Variational Quantum
  Optimization
DESCRIPTION:Workshop\n\nExploiting In-Constraint Energy in Constrained Var
 iational Quantum Optimization\n\nHao, Shaydulin, Pistoia, Larson\n\nA cent
 ral challenge of applying near-term quantum optimization algorithms to ind
 ustrially relevant problems is the need to incorporate complex constraints
 . In general, such constraints cannot be easily encoded in the circuit, an
 d the quantum circuit measurement outcomes are not guaranteed to respect t
 he constraints. Therefore, the optimization must trade off the in-constrai
 nt probability and the quality of the in-constraint solution by adding a p
 enalty for constraint violation into the objective. We propose a new appro
 ach for solving constrained optimization problems with unconstrained, easy
 -to-implement quantum ansätze. Our method leverages the in-constraint ener
 gy as the objective and adds a lower-bound constraint on the in-constraint
  probability to the optimizer. We demonstrate significant gains in solutio
 n quality over directly optimizing the penalized energy. We implement our 
 method in QVoice, a Python package that interfaces with Qiskit for quick p
 rototyping in simulators and on quantum hardware.\n\nSession Format: Recor
 ded\n\nTag: Quantum Computing\n\nRegistration Category: Workshop Reg Pass
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