Hyper-optimized tensor network contraction

Johnnie Gray1,2 and Stefanos Kourtis1,3,4

1Blackett Laboratory, Imperial College London, London SW7 2AZ, United Kingdom
2Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA
3Department of Physics, Boston University, Boston, MA, 02215, USA
4Institut quantique & Département de physique, Université de Sherbrooke, Québec J1K 2R1, Canada

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Abstract

Tensor networks represent the state-of-the-art in computational methods across many disciplines, including the classical simulation of quantum many-body systems and quantum circuits. Several applications of current interest give rise to tensor networks with irregular geometries. Finding the best possible contraction path for such networks is a central problem, with an exponential effect on computation time and memory footprint. In this work, we implement new randomized protocols that find very high quality contraction paths for arbitrary and large tensor networks. We test our methods on a variety of benchmarks, including the random quantum circuit instances recently implemented on Google quantum chips. We find that the paths obtained can be very close to optimal, and often many orders or magnitude better than the most established approaches. As different underlying geometries suit different methods, we also introduce a hyper-optimization approach, where both the method applied and its algorithmic parameters are tuned during the path finding. The increase in quality of contraction schemes found has significant practical implications for the simulation of quantum many-body systems and particularly for the benchmarking of new quantum chips. Concretely, we estimate a speed-up of over 10,000$\times$ compared to the original expectation for the classical simulation of the Sycamore `supremacy' circuits.

Tensor networks offer a universal language that can express a large variety of scientific concepts, from partition functions to quantum circuits. Tensor network contraction can be a practical method for addressing computational challenges, such as the solution of hard constraint satisfaction problems or the simulation and benchmarking of quantum computation on classical computers. The core task of contracting a tensor network is exponentially sensitive to the quality of a so-called contraction tree, which describes a series of pairwise merges that turn the network into a single tensor. We tackle this problem with three key techniques. Firstly, we re-conceive the tensor network as a hyper-graph and use hyper-graph partitioning to build the tree. Secondly, we introduce various simplification schemes to prepare the tensor network for contraction. Finally, we use a Bayesian optimizer that intelligently learns to build ever better trees for a particular contraction. We show vastly improved performance for a variety of problems, including the simulation of Google's Sycamore circuits, the simulation of the quantum approximate optimization algorithm, as well as the solution of weighted model counting problems. For accessible sizes, the paths we find are very close to optimal.

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Cited by

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