RosneT: A Block Tensor Algebra Library for Out-of-Core Quantum Computing Simulation

With the advent of more powerful Quantum Computers, the need for larger Quantum Simulations has boosted. As the amount of resources grows exponentially with size of the target system Tensor Networks emerge as an optimal framework with which we represent Quantum States in tensor factorizations. As th...

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Bibliographic Details
Published in2021 IEEE/ACM Second International Workshop on Quantum Computing Software (QCS) pp. 1 - 8
Main Authors Sanchez-Ramirez, Sergio, Conejero, Javier, Lordan, Francesc, Queralt, Anna, Cortes, Toni, Badia, Rosa M, Garcia-Saez, Artur
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2021
Subjects
Algebra
COMPSs
Computational modeling
distributed computing
HPC
Libraries
out-of-core
Programming
quantum computing
simulation
Supercomputers
task-based programming
tensor network
Tensors
Transforms
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Summary:With the advent of more powerful Quantum Computers, the need for larger Quantum Simulations has boosted. As the amount of resources grows exponentially with size of the target system Tensor Networks emerge as an optimal framework with which we represent Quantum States in tensor factorizations. As the extent of a tensor network increases, so does the size of intermediate tensors requiring HPC tools for their manipulation. Simulations of medium-sized circuits cannot fit on local memory, and solutions for distributed contraction of tensors are scarce. In this work we present RosneT, a library for distributed, out-of-core block tensor algebra. We use the PyCOMPSs programming model to transform tensor operations into a collection of tasks handled by the COMPSs runtime, targeting executions in existing and upcoming Exascale supercomputers. We report results validating our approach showing good scalability in simulations of Quantum circuits of up to 53 qubits.
DOI:10.1109/QCS54837.2021.00004