The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
S Machnes - One of the best experts on this subject based on the ideXlab platform.
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comparing optimizing and benchmarking quantum control algorithms in a unifying Programming Framework
Physical Review A, 2011Co-Authors: S Machnes, U Sander, Steffen J Glaser, P De Fouquieres, Audrunas Gruslys, S G Schirmer, Thomas SchulteherbruggenAbstract:For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e. piecewise constant control amplitudes, iteratively into an optimised shape. Here, we present the first comparative study of optimal control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: grape methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. — Moreover we introduce a novel unifying algorithmic Framework, dynamo (dynamic optimisation platform) designed to provide the quantum-technology community with a convenient matlab-based toolset for optimal control. In addition, it gives researchers in optimal-control techniques a Framework for benchmarking and comparing new proposed algorithms to the state-of-the-art. It allows for a mix-and-match approach with various types of gradients, update and step-size methods, and subspace choices. Open-source code including examples is made available at http://qlib.info.
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comparing optimizing and benchmarking quantum control algorithms in a unifying Programming Framework
Physical Review A, 2011Co-Authors: S Machnes, U Sander, Steffen J Glaser, P De Fouquieres, Audrunas Gruslys, S G Schirmer, Thomas SchulteherbruggenAbstract:For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. Moreover, we introduce a unifying algorithmic Framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a Framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.
Thomas Schulteherbruggen - One of the best experts on this subject based on the ideXlab platform.
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comparing optimizing and benchmarking quantum control algorithms in a unifying Programming Framework
Physical Review A, 2011Co-Authors: S Machnes, U Sander, Steffen J Glaser, P De Fouquieres, Audrunas Gruslys, S G Schirmer, Thomas SchulteherbruggenAbstract:For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e. piecewise constant control amplitudes, iteratively into an optimised shape. Here, we present the first comparative study of optimal control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: grape methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. — Moreover we introduce a novel unifying algorithmic Framework, dynamo (dynamic optimisation platform) designed to provide the quantum-technology community with a convenient matlab-based toolset for optimal control. In addition, it gives researchers in optimal-control techniques a Framework for benchmarking and comparing new proposed algorithms to the state-of-the-art. It allows for a mix-and-match approach with various types of gradients, update and step-size methods, and subspace choices. Open-source code including examples is made available at http://qlib.info.
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comparing optimizing and benchmarking quantum control algorithms in a unifying Programming Framework
Physical Review A, 2011Co-Authors: S Machnes, U Sander, Steffen J Glaser, P De Fouquieres, Audrunas Gruslys, S G Schirmer, Thomas SchulteherbruggenAbstract:For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. Moreover, we introduce a unifying algorithmic Framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a Framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.
U Sander - One of the best experts on this subject based on the ideXlab platform.
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comparing optimizing and benchmarking quantum control algorithms in a unifying Programming Framework
Physical Review A, 2011Co-Authors: S Machnes, U Sander, Steffen J Glaser, P De Fouquieres, Audrunas Gruslys, S G Schirmer, Thomas SchulteherbruggenAbstract:For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e. piecewise constant control amplitudes, iteratively into an optimised shape. Here, we present the first comparative study of optimal control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: grape methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. — Moreover we introduce a novel unifying algorithmic Framework, dynamo (dynamic optimisation platform) designed to provide the quantum-technology community with a convenient matlab-based toolset for optimal control. In addition, it gives researchers in optimal-control techniques a Framework for benchmarking and comparing new proposed algorithms to the state-of-the-art. It allows for a mix-and-match approach with various types of gradients, update and step-size methods, and subspace choices. Open-source code including examples is made available at http://qlib.info.
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comparing optimizing and benchmarking quantum control algorithms in a unifying Programming Framework
Physical Review A, 2011Co-Authors: S Machnes, U Sander, Steffen J Glaser, P De Fouquieres, Audrunas Gruslys, S G Schirmer, Thomas SchulteherbruggenAbstract:For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. Moreover, we introduce a unifying algorithmic Framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a Framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.
Steffen J Glaser - One of the best experts on this subject based on the ideXlab platform.
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comparing optimizing and benchmarking quantum control algorithms in a unifying Programming Framework
Physical Review A, 2011Co-Authors: S Machnes, U Sander, Steffen J Glaser, P De Fouquieres, Audrunas Gruslys, S G Schirmer, Thomas SchulteherbruggenAbstract:For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e. piecewise constant control amplitudes, iteratively into an optimised shape. Here, we present the first comparative study of optimal control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: grape methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. — Moreover we introduce a novel unifying algorithmic Framework, dynamo (dynamic optimisation platform) designed to provide the quantum-technology community with a convenient matlab-based toolset for optimal control. In addition, it gives researchers in optimal-control techniques a Framework for benchmarking and comparing new proposed algorithms to the state-of-the-art. It allows for a mix-and-match approach with various types of gradients, update and step-size methods, and subspace choices. Open-source code including examples is made available at http://qlib.info.
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comparing optimizing and benchmarking quantum control algorithms in a unifying Programming Framework
Physical Review A, 2011Co-Authors: S Machnes, U Sander, Steffen J Glaser, P De Fouquieres, Audrunas Gruslys, S G Schirmer, Thomas SchulteherbruggenAbstract:For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. Moreover, we introduce a unifying algorithmic Framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a Framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.
P De Fouquieres - One of the best experts on this subject based on the ideXlab platform.
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comparing optimizing and benchmarking quantum control algorithms in a unifying Programming Framework
Physical Review A, 2011Co-Authors: S Machnes, U Sander, Steffen J Glaser, P De Fouquieres, Audrunas Gruslys, S G Schirmer, Thomas SchulteherbruggenAbstract:For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e. piecewise constant control amplitudes, iteratively into an optimised shape. Here, we present the first comparative study of optimal control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: grape methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. — Moreover we introduce a novel unifying algorithmic Framework, dynamo (dynamic optimisation platform) designed to provide the quantum-technology community with a convenient matlab-based toolset for optimal control. In addition, it gives researchers in optimal-control techniques a Framework for benchmarking and comparing new proposed algorithms to the state-of-the-art. It allows for a mix-and-match approach with various types of gradients, update and step-size methods, and subspace choices. Open-source code including examples is made available at http://qlib.info.
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comparing optimizing and benchmarking quantum control algorithms in a unifying Programming Framework
Physical Review A, 2011Co-Authors: S Machnes, U Sander, Steffen J Glaser, P De Fouquieres, Audrunas Gruslys, S G Schirmer, Thomas SchulteherbruggenAbstract:For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions are pointed out. Moreover, we introduce a unifying algorithmic Framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a Framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.
