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arXiv:2110.09894 · IEEE/ACM International Workshop on Quantum Computing Software (QCS)

Tensor Network Circuit Simulation at Exascale

John Brennan, Momme Allalen, D. Brayford, Kenneth Hanley, Luigi Iapichino, L. ORiordan, Myles Doyle, Niall Moran

Published Nov 1, 2021• 9 citations
arXivHTMLPDF

Abstract

Tensor network methods are incredibly effective for simulating quantum circuits. This is due to their ability to efficiently represent and manipulate the wave-functions of large interacting quantum systems. We describe the challenges faced when scaling tensor network simulation approaches to Exascale compute platforms and introduce QuantEx, a framework for tensor network circuit simulation at Exascale.

DOI
10.1109/qcs54837.2021.00006

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The accurate simulation of quantum circuits remains a significant challenge due to the exponential scaling of computational resources with circuit size. This work explores the application of tensor network methods, specifically Matrix Product States (MPS), to provide a scalable approach for simulating quantum circuits. We demonstrate how tensor networks can efficiently represent and manipulate the states of quantum circuits, reducing the computational burden associated with traditional methods. The core claim of this research is that tensor networks offer a viable path to accurately simulating larger and more complex quantum systems. This research introduces a novel framework for symbolic simulation, leveraging the power of tensor network representations to overcome the limitations of conventional simulation techniques. The effectiveness of this approach is illustrated through theoretical analysis and conceptual examples.
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This paper explores the potential of tensor networks for enabling distributed quantum simulation, a crucial step towards scaling up quantum computations. Traditional quantum simulation faces limitations due to the exponential growth of state vector sizes required for complex systems. Distributed quantum simulation aims to overcome this by partitioning the simulation across multiple quantum processors. However, efficient representation and manipulation of the resulting distributed quantum state remain a significant challenge. This work proposes utilizing tensor networks as a framework for representing and processing these distributed quantum states. Tensor networks offer an inherent structure that facilitates efficient parallel computation and minimizes communication overhead between quantum processors. We outline a theoretical framework, detailing the application of tensor network contractions and disentanglement techniques specifically tailored for distributed quantum simulation scenarios. The core claim of this work is that scaling up quantum simulations requires efficient methods for representing and manipulating quantum states, and tensor networks provide a promising avenue for achieving this goal. We present a method to reduce the computational complexity associated with simulating large quantum systems by leveraging the properties of tensor networks. The approach allows for a more scalable simulation strategy, moving beyond the limitations of traditional state vector representations. Further research will explore specific tensor network architectures and optimization strategies for different simulation problems.
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