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DTSTART;TZID=America/Chicago:20221116T154500
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UID:submissions.supercomputing.org_SC22_sess227_spostg112@linklings.com
SUMMARY:Compressing Quantum Circuit Simulation Tensor Data
DESCRIPTION:ACM Student Research Competition: Graduate Poster, ACM Student
  Research Competition: Undergraduate Poster, Posters\n\nCompressing Quantu
 m Circuit Simulation Tensor Data\n\nShah\n\nQuantum circuit simulation can
  be carried out as a contraction over many quantum tensors. QTensor, a lib
 rary built for quantum circuit simulation using a bucket elimination algor
 ithm, contracts tensors to return a final energy value. As bucket eliminat
 ion advances, tensors can grow large, and memory becomes a bottleneck. To 
 address memory limitations of circuit simulation while enabling more compl
 ex circuits to be simulated, we focus on implementing a lossy compressor t
 hat can compress the floating-point data stored in quantum circuit tensors
  while simultaneously preserving a final energy value within an error boun
 d after decompression. We study the effects of various lossy compression/d
 ecompression strategies on data compressibility, throughput, and result er
 ror to ensure compression/decompression can be effective, fast, and does n
 ot heavily distort data. The work for this project is in progress and prel
 iminary results for proposed preprocessing/postprocessing strategies and c
 ompressor optimizations that have been developed will be showcased.\n\nSes
 sion Format: Recorded\n\nRegistration Category: Tech Program Reg Pass
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