Matrix multiplication ≈ Matrix multiplication
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Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 Open
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time. At training-time the binary weights and activations are used for computing the parameters gradients. During …
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Binarized Neural Networks Open
We introduce a method to train Binarized Neural Networks (BNNs) - neural networks with binary weights and activations at run-time and when computing the parameters' gradient at train-time. We conduct two sets of experiments, each based on …
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The ITensor Software Library for Tensor Network Calculations Open
ITensor is a system for programming tensor network calculations with an interface modeled on tensor diagrams, allowing users to focus on the connectivity of a tensor network without manually bookkeeping tensor indices. The ITensor interfac…
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Speeding Up Distributed Machine Learning Using Codes Open
Codes are widely used in many engineering applications to offer robustness against noise. In large-scale systems there are several types of noise that can affect the performance of distributed machine learning algorithms -- straggler nodes…
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Unifying time evolution and optimization with matrix product states Open
We show that the time-dependent variational principle provides a unifying\nframework for time-evolution methods and optimisation methods in the context of\nmatrix product states. In particular, we introduce a new integration scheme for\nst…
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DScribe: Library of descriptors for machine learning in materials science Open
DScribe is a software package for machine learning that provides popular\nfeature transformations ("descriptors") for atomistic materials simulations.\nDScribe accelerates the application of machine learning for atomistic property\npredict…
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Matrix product states and projected entangled pair states: Concepts, symmetries, theorems Open
The theory of entanglement provides a fundamentally new language for describing interactions and correlations in many-body systems. Its vocabulary consists of qubits and entangled pairs, and the syntax is provided by tensor networks. How m…
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GPyTorch: Blackbox Matrix-Matrix Gaussian Process Inference with GPU Acceleration Open
Despite advances in scalable models, the inference tools used for Gaussian processes (GPs) have yet to fully capitalize on developments in computing hardware. We present an efficient and general approach to GP inference based on Blackbox M…
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Photonic matrix multiplication lights up photonic accelerator and beyond Open
Matrix computation, as a fundamental building block of information processing in science and technology, contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms. Photonic accelerat…
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Discovering faster matrix multiplication algorithms with reinforcement learning Open
Improving the efficiency of algorithms for fundamental computations can have a widespread impact, as it can affect the overall speed of a large amount of computations. Matrix multiplication is one such primitive task, occurring in many sys…
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Photonic Perceptron Based on a Kerr Microcomb for High‐Speed, Scalable, Optical Neural Networks Open
Optical artificial neural networks (ONNs)—analog computing hardware tailored for machine learning—have significant potential for achieving ultra‐high computing speed and energy efficiency. A new approach to architectures for ONNs based on …
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Chameleon Open
We present Chameleon, a novel hybrid (mixed-protocol) framework for secure function evaluation (SFE) which enables two parties to jointly compute a function without disclosing their private inputs. Chameleon combines the best aspects of ge…
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Polynomial codes: an optimal design for high-dimensional coded matrix multiplication Open
We consider a large-scale matrix multiplication problem where the computation is carried out using a distributed system with a master node and multiple worker nodes, where each worker can store parts of the input matrices. We propose a com…
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Hand-waving and interpretive dance: an introductory course on tensor networks Open
The curse of dimensionality associated with the Hilbert space of spin systems\nprovides a significant obstruction to the study of condensed matter systems.\nTensor networks have proven an important tool in attempting to overcome this\ndiff…
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A Refined Laser Method and Faster Matrix Multiplication Open
The complexity of matrix multiplication is measured in terms of ω, the smallest real number such that two n × n matrices can be multiplied using O(nω+∊) field operations for all ∊ > 0; the best bound until now is ω < 2.37287 [Le Gall'14]. …
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Variational optimization algorithms for uniform matrix product states Open
We combine the Density Matrix Renormalization Group (DMRG) with Matrix\nProduct State tangent space concepts to construct a variational algorithm for\nfinding ground states of one dimensional quantum lattices in the thermodynamic\nlimit. A…
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Variational optimization with infinite projected entangled-pair states Open
We present a scheme to perform an iterative variational optimization with\ninfinite projected entangled-pair states (iPEPS), a tensor network ansatz for a\ntwo-dimensional wave function in the thermodynamic limit, to compute the ground\nst…
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In‐Memory Vector‐Matrix Multiplication in Monolithic Complementary Metal–Oxide–Semiconductor‐Memristor Integrated Circuits: Design Choices, Challenges, and Perspectives Open
The low communication bandwidth between memory and processing units in conventional von Neumann machines does not support the requirements of emerging applications that rely extensively on large sets of data. More recent computing paradigm…
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Codebase release 0.3 for ITensor Open
ITensor is a system for programming tensor network calculations with an interface modeled on tensor diagrams, allowing users to focus on the connectivity of a tensor network without manually bookkeeping tensor indices. The ITensor interfac…
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Dynamical phase diagram of quantum spin chains with long-range interactions Open
Using an infinite Matrix Product State (iMPS) technique based on the\ntime-dependent variational principle (TDVP), we study two major types of\ndynamical phase transitions (DPT) in the one-dimensional transverse-field Ising\nmodel (TFIM) w…
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Matrix product operators, matrix product states, and <i>ab initio</i> density matrix renormalization group algorithms Open
Current descriptions of the ab initio density matrix renormalization group (DMRG) algorithm use two superficially different languages: an older language of the renormalization group and renormalized operators, and a more recent language of…
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Modern Approaches to Exact Diagonalization and Selected Configuration Interaction with the Adaptive Sampling CI Method Open
Recent advances in selected configuration interaction methods have made them competitive with the most accurate techniques available and, hence, creating an increasingly powerful tool for solving quantum Hamiltonians. In this work, we buil…
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Variational algorithms for linear algebra Open
Quantum algorithms have been developed for efficiently solving linear algebra tasks. However, they generally require deep circuits and hence universal fault-tolerant quantum computers. In this work, we propose variational algorithms for li…
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Adaptive sparse tiling for sparse matrix multiplication Open
Tiling is a key technique for data locality optimization and is widely used in high-performance implementations of dense matrix-matrix multiplication for multicore/manycore CPUs and GPUs. However, the irregular and matrix-dependent data ac…
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Dynamics of the Kitaev-Heisenberg Model Open
We introduce a matrix-product state based method to efficiently obtain dynamical response functions for two-dimensional microscopic Hamiltonians. We apply this method to different phases of the Kitaev-Heisenberg model and identify characte…
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Integer multiplication in time $O(n\mathrm{log}\, n)$ Open
International audience
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The ITensor Software Library for Tensor Network Calculations Open
ITensor is a system for programming tensor network calculations with an interface modeled on tensor diagram notation, which allows users to focus on the connectivity of a tensor network without manually bookkeeping tensor indices. The ITen…
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Isometric Tensor Network States in Two Dimensions Open
Tensor-network states (TNS) are a promising but numerically challenging tool for simulating two-dimensional (2D) quantum many-body problems. We introduce an isometric restriction of the TNS ansatz that allows for highly efficient contracti…
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Generic construction of efficient matrix product operators Open
Matrix Product Operators (MPOs) are at the heart of the second-generation\nDensity Matrix Renormalisation Group (DMRG) algorithm formulated in Matrix\nProduct State language. We first summarise the widely known facts on MPO\narithmetic and…
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Gamma: leveraging Gustavson’s algorithm to accelerate sparse matrix multiplication Open
Sparse matrix-sparse matrix multiplication (spMspM) is at the heart of a wide range of scientific and machine learning applications. spMspM is inefficient on general-purpose architectures, making accelerators attractive. However, prior spM…