2,682 research outputs found
Optimization problems in contracted tensor networks
Abstract We discuss the calculus of variations in tensor representations with a special focus on tensor networks and apply it to functionals of practical interest. The survey provides all necessary ingredients for applying minimization methods in a general setting. The important cases of target functionals which are linear and quadratic with respect to the tensor product are discussed, and combinations of these functionals are presented in detail. As an example, we consider the representation rank compression in tensor networks. For the numerical treatment, we use the nonlinear block Gauss-Seidel method. We demonstrate the rate of convergence in numerical tests
qTorch: The Quantum Tensor Contraction Handler
Classical simulation of quantum computation is necessary for studying the
numerical behavior of quantum algorithms, as there does not yet exist a large
viable quantum computer on which to perform numerical tests. Tensor network
(TN) contraction is an algorithmic method that can efficiently simulate some
quantum circuits, often greatly reducing the computational cost over methods
that simulate the full Hilbert space. In this study we implement a tensor
network contraction program for simulating quantum circuits using multi-core
compute nodes. We show simulation results for the Max-Cut problem on 3- through
7-regular graphs using the quantum approximate optimization algorithm (QAOA),
successfully simulating up to 100 qubits. We test two different methods for
generating the ordering of tensor index contractions: one is based on the tree
decomposition of the line graph, while the other generates ordering using a
straight-forward stochastic scheme. Through studying instances of QAOA
circuits, we show the expected result that as the treewidth of the quantum
circuit's line graph decreases, TN contraction becomes significantly more
efficient than simulating the whole Hilbert space. The results in this work
suggest that tensor contraction methods are superior only when simulating
Max-Cut/QAOA with graphs of regularities approximately five and below. Insight
into this point of equal computational cost helps one determine which
simulation method will be more efficient for a given quantum circuit. The
stochastic contraction method outperforms the line graph based method only when
the time to calculate a reasonable tree decomposition is prohibitively
expensive. Finally, we release our software package, qTorch (Quantum TensOR
Contraction Handler), intended for general quantum circuit simulation.Comment: 21 pages, 8 figure
Improving the efficiency of variational tensor network algorithms
We present several results relating to the contraction of generic tensor
networks and discuss their application to the simulation of quantum many-body
systems using variational approaches based upon tensor network states. Given a
closed tensor network , we prove that if the environment of a
single tensor from the network can be evaluated with computational cost
, then the environment of any other tensor from can be
evaluated with identical cost . Moreover, we describe how the set of
all single tensor environments from can be simultaneously
evaluated with fixed cost . The usefulness of these results, which are
applicable to a variety of tensor network methods, is demonstrated for the
optimization of a Multi-scale Entanglement Renormalization Ansatz (MERA) for
the ground state of a 1D quantum system, where they are shown to substantially
reduce the computation time.Comment: 12 pages, 8 figures, RevTex 4.1, includes reference implementation.
Software updated to v1.02: Resolved two scenarios in which multienv would
generate errors for valid input
Lecture Notes of Tensor Network Contractions
Tensor network (TN), a young mathematical tool of high vitality and great
potential, has been undergoing extremely rapid developments in the last two
decades, gaining tremendous success in condensed matter physics, atomic
physics, quantum information science, statistical physics, and so on. In this
lecture notes, we focus on the contraction algorithms of TN as well as some of
the applications to the simulations of quantum many-body systems. Starting from
basic concepts and definitions, we first explain the relations between TN and
physical problems, including the TN representations of classical partition
functions, quantum many-body states (by matrix product state, tree TN, and
projected entangled pair state), time evolution simulations, etc. These
problems, which are challenging to solve, can be transformed to TN contraction
problems. We present then several paradigm algorithms based on the ideas of the
numerical renormalization group and/or boundary states, including density
matrix renormalization group, time-evolving block decimation,
coarse-graining/corner tensor renormalization group, and several distinguished
variational algorithms. Finally, we revisit the TN approaches from the
perspective of multi-linear algebra (also known as tensor algebra or tensor
decompositions) and quantum simulation. Despite the apparent differences in the
ideas and strategies of different TN algorithms, we aim at revealing the
underlying relations and resemblances in order to present a systematic picture
to understand the TN contraction approaches.Comment: 134 pages, 68 figures. In this version, the manuscript has been
changed into the format of book; new sections about tensor network and
quantum circuits have been adde
The Tensor Networks Anthology: Simulation techniques for many-body quantum lattice systems
We present a compendium of numerical simulation techniques, based on tensor
network methods, aiming to address problems of many-body quantum mechanics on a
classical computer. The core setting of this anthology are lattice problems in
low spatial dimension at finite size, a physical scenario where tensor network
methods, both Density Matrix Renormalization Group and beyond, have long proven
to be winning strategies. Here we explore in detail the numerical frameworks
and methods employed to deal with low-dimension physical setups, from a
computational physics perspective. We focus on symmetries and closed-system
simulations in arbitrary boundary conditions, while discussing the numerical
data structures and linear algebra manipulation routines involved, which form
the core libraries of any tensor network code. At a higher level, we put the
spotlight on loop-free network geometries, discussing their advantages, and
presenting in detail algorithms to simulate low-energy equilibrium states.
Accompanied by discussions of data structures, numerical techniques and
performance, this anthology serves as a programmer's companion, as well as a
self-contained introduction and review of the basic and selected advanced
concepts in tensor networks, including examples of their applications.Comment: 115 pages, 56 figure
Simulating Strongly Correlated Quantum Systems with Tree Tensor Networks
We present a tree-tensor-network-based method to study strongly correlated
systems with nonlocal interactions in higher dimensions. Although the
momentum-space and quantum-chemistry versions of the density matrix
renormalization group (DMRG) method have long been applied to such systems, the
spatial topology of DMRG-based methods allows efficient optimizations to be
carried out with respect to one spatial dimension only. Extending the
matrix-product-state picture, we formulate a more general approach by allowing
the local sites to be coupled to more than two neighboring auxiliary subspaces.
Following Shi. et. al. [Phys. Rev. A, 74, 022320 (2006)], we treat a tree-like
network ansatz with arbitrary coordination number z, where the z=2 case
corresponds to the one-dimensional scheme. For this ansatz, the long-range
correlation deviates from the mean-field value polynomially with distance, in
contrast to the matrix-product ansatz, which deviates exponentially. The
computational cost of the tree-tensor-network method is significantly smaller
than that of previous DMRG-based attempts, which renormalize several blocks
into a single block. In addition, we investigate the effect of unitary
transformations on the local basis states and present a method for optimizing
such transformations. For the 1-d interacting spinless fermion model, the
optimized transformation interpolates smoothly between real space and momentum
space. Calculations carried out on small quantum chemical systems support our
approach
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