Compressing Quantum Circuit Simulation Tensor Data
DescriptionQuantum 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.
Event Type
ACM Student Research Competition: Graduate Poster
ACM Student Research Competition: Undergraduate Poster
TimeWednesday, 16 November 20228:30am - 5pm CST
Registration Categories
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