Quantum computation and quantum information

By Jim Law. SOEN, 2001

913 citations recorded by the source corpus.

An easy-to-comprehend text for required undergraduate courses in computer theory, this work thoroughly covers the three fundamental areas of computer theory--formal languages, automata theory, and Turing machines. It is an imaginative and pedagogically strong attempt to remove the unnecessary mathematical complications associated with the study of these subjects. The author substitutes graphic representation for symbolic proofs, allowing students with poor mathematical background to easily follow each step. Includes a large selection of well thought out problems at the end of each chapter.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/134770862b5ccedbb5376cb1a9de61d3d4973c17

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In Artificial Intelligence

In Atomic and Molecular Physics, and Optics

Citations in the corpus

Trailhead for quantum simulation of SU(3) Yang-Mills lattice gauge theory in the local multiplet basis (2021, methodology): “Ignoring simplifications for the implementation of terms sharing a basis, each of these exponentially numerous terms can be implemented with Oð2nÞ or OðlogðΛpΛqÞÞ CNOT gates [110] resulting in the total number of gates to implement the plaquette operator time evolution scaling exponentially with n o”
Qudits and High-Dimensional Quantum Computing (2020, methodology): “5, requires at least 5 two-qubit gates [64].”
Quantum technology for military applications (2021, background): “Quantum information science describes the quantum information flow in quantum computing and quantum communications, although in a broader sense it can be applied in quantum sensing and metrology, see [19, 20].”
Quantum error correction: an introductory guide (2019, background): “ction, we do assume an understanding of elementary quantum mechanics and the circuit model of quantum computing. The reader should be comfortable with quantum circuit notation, as seen for example in [35], and be familiar with standard gates such as the Hadamard gate (H), the controlled-NOT gate (CN”
qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor (2019, background): “[48] M.”
Machine learning and the physical sciences (2019, methodology): “In the most popular framework of gate-based quantum computing (Nielsen and Chuang, 2002), a quantum algorithm describes the evolution of an initial state jψ0i of a quantum system of n two-level systems called qubits to a final state jψfi through discrete transformations or quantum gates. The gates u”
Wigner Function Negativity and Contextuality in Quantum Computation on Rebits (2014, methodology): “1 Stabilizer formalism The stabilizer formalism [13] is an indispensable tool in the field of quantum error correction [20], and we give a brief introduction to the formalism herein.”
Machine learning \& artificial intelligence in the quantum domain (2017, methodology): “The quantum circuit model (Nielsen and Chuang, 2011) is standard for algebraic problems.”
Lattice gauge theory simulations in the quantum information era (2016, background): “Many-body problems, and in particular quantum ones, lie at the border of our current knowledge inmany different fields of science, from very applied ones, such as the electronic structure problem with impact on chemistry and drugs research [1], the development of quantum technologies [2,3], or the q”
Data re-uploading for a universal quantum classifier (2019, methodology): “This whole algorithm has been developed by us following the steps from [17].”
Operator complexity: a journey to the edge of Krylov space (2020, background): “For circuit complexity, to cite but one example, such a tolerance parameter must be introduced, and its presence is crucial in establishing its boundedness [13–15].”
Quantum computing with neutral atoms (2020, methodology): “amounts to characterizing the low lying eigenstates of a very large electronic Hamiltonian. Quantum approaches to nd eigenvalues have previously relied on the quantum phase estimation (QPE) algorithm [14], which oers an exponential speedup over classical methods,but remains unpractical for NISQ quan”