Scalable quantum simulation by reductions and decompositions through the Id-operator
AB de Avila, RHS Reiser, AC Yamin… - Proceedings of the 31st …, 2016 - dl.acm.org
Proceedings of the 31st Annual ACM Symposium on Applied Computing, 2016•dl.acm.org
One of the main obstacles for the adoption of quantum algorithm simulation is the
exponential increase in temporal and spatial complexities, due to the expansion of
transformations and read/write memory states by using tensor product in multi-dimension
applications. Reduction and decomposition optimizations via the Id-operator provide a smart
and appropriate storage and distribution of quantum information. Reductions are achieved
by avoiding replication and sparsity inherited from such operators. By using decompositions …
exponential increase in temporal and spatial complexities, due to the expansion of
transformations and read/write memory states by using tensor product in multi-dimension
applications. Reduction and decomposition optimizations via the Id-operator provide a smart
and appropriate storage and distribution of quantum information. Reductions are achieved
by avoiding replication and sparsity inherited from such operators. By using decompositions …
One of the main obstacles for the adoption of quantum algorithm simulation is the exponential increase in temporal and spatial complexities, due to the expansion of transformations and read/write memory states by using tensor product in multi-dimension applications. Reduction and decomposition optimizations via the Id-operator provide a smart and appropriate storage and distribution of quantum information. Reductions are achieved by avoiding replication and sparsity inherited from such operators. By using decompositions, applications may be divided into sub-steps to store only distinct values from Id-operators, instead of executing quantum transformations in a single step. Additional optimizations based on mixed partial processes provide control over increase in read/write memory states in quantum transformations, also contributing to increase the scalability regarding hardware-GPUs memory limit. Hadamard and Discret Quantum Fourier Transforms were simulated up to 28 qubits applications over a single GPU with drastic temporal complexity reduction and simulation time.
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