Asset Details
MbrlCatalogueTitleDetail
Do you wish to reserve the book?
qTorch: The quantum tensor contraction handler
by
Cao, Yudong
, Kivlichan, Ian D.
, Aspuru-Guzik, Alán
, Sawaya, Nicolas P. D.
, Romero, Jhonathan
, Fried, E. Schuyler
in
Algorithms
/ Analysis
/ Biology
/ Chemistry
/ Circuits
/ Computational efficiency
/ Computer and Information Sciences
/ Computer applications
/ Computer simulation
/ Computer Simulation - standards
/ Computer Simulation - statistics & numerical data
/ Computers
/ Contraction
/ Data collection
/ Decomposition
/ Equipment Design
/ Graphs
/ Hilbert space
/ Integrated circuits
/ Linux
/ Mathematical analysis
/ MATHEMATICS AND COMPUTING
/ Methods
/ Microprocessors
/ Neural Networks, Computer
/ Optimization
/ Optimization algorithms
/ Optimization theory
/ Other Topics
/ Physical Sciences
/ Quantum computers
/ Quantum Theory
/ Qubits (quantum computing)
/ Research and Analysis Methods
/ Science & Technology
/ Simulation
/ Software
/ Stochastic Processes
/ Stochasticity
/ Tensors
/ Test procedures
2018
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
You are currently in the queue to collect this book. You will be notified once it is your turn to collect the book.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
qTorch: The quantum tensor contraction handler
by
Cao, Yudong
, Kivlichan, Ian D.
, Aspuru-Guzik, Alán
, Sawaya, Nicolas P. D.
, Romero, Jhonathan
, Fried, E. Schuyler
in
Algorithms
/ Analysis
/ Biology
/ Chemistry
/ Circuits
/ Computational efficiency
/ Computer and Information Sciences
/ Computer applications
/ Computer simulation
/ Computer Simulation - standards
/ Computer Simulation - statistics & numerical data
/ Computers
/ Contraction
/ Data collection
/ Decomposition
/ Equipment Design
/ Graphs
/ Hilbert space
/ Integrated circuits
/ Linux
/ Mathematical analysis
/ MATHEMATICS AND COMPUTING
/ Methods
/ Microprocessors
/ Neural Networks, Computer
/ Optimization
/ Optimization algorithms
/ Optimization theory
/ Other Topics
/ Physical Sciences
/ Quantum computers
/ Quantum Theory
/ Qubits (quantum computing)
/ Research and Analysis Methods
/ Science & Technology
/ Simulation
/ Software
/ Stochastic Processes
/ Stochasticity
/ Tensors
/ Test procedures
2018
Oops! Something went wrong.
While trying to remove the title from your shelf something went wrong :( Kindly try again later!
Do you wish to request the book?
qTorch: The quantum tensor contraction handler
by
Cao, Yudong
, Kivlichan, Ian D.
, Aspuru-Guzik, Alán
, Sawaya, Nicolas P. D.
, Romero, Jhonathan
, Fried, E. Schuyler
in
Algorithms
/ Analysis
/ Biology
/ Chemistry
/ Circuits
/ Computational efficiency
/ Computer and Information Sciences
/ Computer applications
/ Computer simulation
/ Computer Simulation - standards
/ Computer Simulation - statistics & numerical data
/ Computers
/ Contraction
/ Data collection
/ Decomposition
/ Equipment Design
/ Graphs
/ Hilbert space
/ Integrated circuits
/ Linux
/ Mathematical analysis
/ MATHEMATICS AND COMPUTING
/ Methods
/ Microprocessors
/ Neural Networks, Computer
/ Optimization
/ Optimization algorithms
/ Optimization theory
/ Other Topics
/ Physical Sciences
/ Quantum computers
/ Quantum Theory
/ Qubits (quantum computing)
/ Research and Analysis Methods
/ Science & Technology
/ Simulation
/ Software
/ Stochastic Processes
/ Stochasticity
/ Tensors
/ Test procedures
2018
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
qTorch: The quantum tensor contraction handler
2018
Request Book From Autostore
and Choose the Collection Method
Overview
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. For a nontrivial subset of these quantum circuits, 50 to 100 qubits can easily be simulated on a single compute node.
Publisher
Public Library of Science,Public Library of Science (PLoS)
This website uses cookies to ensure you get the best experience on our website.