Student: Milan Shah (North Carolina State University, Argonne National Laboratory (ANL))
Supervisor: Michela Becchi (North Carolina State University)
Abstract: Quantum circuit simulation can be carried out as a contraction over many quantum tensors. QTensor, a library built for quantum circuit simulation using a bucket elimination algorithm, contracts tensors to return a final energy value. As bucket elimination advances, tensors can grow large, and memory becomes a bottleneck. To address memory limitations of circuit simulation while enabling more complex circuits to be simulated, we focus on implementing a lossy compressor that can compress the floating-point data stored in quantum circuit tensors while simultaneously preserving a final energy value within an error bound after decompression. We study the effects of various lossy compression/decompression strategies on data compressibility, throughput, and result error to ensure compression/decompression can be effective, fast, and does not heavily distort data. The work for this project is in progress and preliminary results for proposed preprocessing/postprocessing strategies and compressor optimizations that have been developed will be showcased.
ACM-SRC Semi-Finalist: yes
Poster Summary: PDF
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