var bibbase_data = {"data":"\"Loading..\"\n\n
\n\n \n\n \n\n \n \n\n \n\n \n \n\n \n\n \n
\n generated by\n \n \"bibbase.org\"\n\n \n
\n \n\n
\n\n \n\n\n
\n\n Excellent! Next you can\n create a new website with this list, or\n embed it in an existing web page by copying & pasting\n any of the following snippets.\n\n
\n JavaScript\n (easiest)\n
\n \n <script src=\"https://bibbase.org/show?bib=https://bibbase.org/network/files/Q5cL7GfTpa3AeKfyk&jsonp=1&jsonp=1\"></script>\n \n
\n\n PHP\n
\n \n <?php\n $contents = file_get_contents(\"https://bibbase.org/show?bib=https://bibbase.org/network/files/Q5cL7GfTpa3AeKfyk&jsonp=1\");\n print_r($contents);\n ?>\n \n
\n\n iFrame\n (not recommended)\n
\n \n <iframe src=\"https://bibbase.org/show?bib=https://bibbase.org/network/files/Q5cL7GfTpa3AeKfyk&jsonp=1\"></iframe>\n \n
\n\n

\n For more details see the documention.\n

\n
\n
\n\n
\n\n This is a preview! To use this list on your own web site\n or create a new web site from it,\n create a free account. The file will be added\n and you will be able to edit it in the File Manager.\n We will show you instructions once you've created your account.\n
\n\n
\n\n

To the site owner:

\n\n

Action required! Mendeley is changing its\n API. In order to keep using Mendeley with BibBase past April\n 14th, you need to:\n

    \n
  1. renew the authorization for BibBase on Mendeley, and
    2. \n
  2. update the BibBase URL\n in your page the same way you did when you initially set up\n this page.\n
    3. \n
\n

\n\n

\n \n \n Fix it now\n

\n
\n\n
\n\n\n
\n \n \n
\n
\n  \n 2020\n \n \n (2)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n CNOT circuit extraction for topologically-constrained quantum memories.\n \n \n \n \n\n\n \n Kissinger, A.; and Meijer-van de Griend, A.\n\n\n \n\n\n\n Quantum Information and Computation, 20(7&8): 581–596. 2020.\n \n\n\n\n
\n\n\n\n \n \n \"CNOT paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{1904.00633,\r\nAuthor = {Kissinger, Aleks and Meijer-van de Griend, Arianne},\r\nTitle = {CNOT circuit extraction for topologically-constrained quantum memories},\r\njournal={Quantum Information and Computation},\r\nvolume={20},\r\nnumber={7\\&8},\r\npages={581--596},\r\nyear={2020},\r\n%note = {Also presented at QPL 2019, Chapman University (Orange, USA)},\r\nurl_Paper = {http://www.rintonpress.com/xxqic20/qic-20-78/0581-0596.pdf},\r\nabstract = {Many physical implementations of quantum computers impose stringent\r\nmemory constraints in which 2-qubit operations can only be performed between\r\nqubits which are nearest neighbours in a lattice or graph structure. Hence, before\r\na computation can be run on such a device, it must be mapped onto the physical\r\narchitecture. That is, logical qubits must be assigned physical locations in the\r\nquantum memory, and the circuit must be replaced by an equivalent one containing\r\nonly operations between nearest neighbours. In this paper, we give a new technique\r\nfor quantum circuit mapping (a.k.a. routing), based on Gaussian elimination\r\nconstrained to certain optimal spanning trees called Steiner trees. We give a reference\r\nimplementation of the technique for CNOT circuits and show that it significantly outperforms general-purpose routines on CNOT circuits. We then comment on how the\r\ntechnique can be extended straightforwardly to the synthesis of CNOT+Rz circuits and\r\nas a modification to a recently-proposed circuit simplification/extraction procedure for\r\ngeneric circuits based on the ZX-calculus.\r\n}\r\n}\r\n\r\n
\n
\n\n\n
\n Many physical implementations of quantum computers impose stringent memory constraints in which 2-qubit operations can only be performed between qubits which are nearest neighbours in a lattice or graph structure. Hence, before a computation can be run on such a device, it must be mapped onto the physical architecture. That is, logical qubits must be assigned physical locations in the quantum memory, and the circuit must be replaced by an equivalent one containing only operations between nearest neighbours. In this paper, we give a new technique for quantum circuit mapping (a.k.a. routing), based on Gaussian elimination constrained to certain optimal spanning trees called Steiner trees. We give a reference implementation of the technique for CNOT circuits and show that it significantly outperforms general-purpose routines on CNOT circuits. We then comment on how the technique can be extended straightforwardly to the synthesis of CNOT+Rz circuits and as a modification to a recently-proposed circuit simplification/extraction procedure for generic circuits based on the ZX-calculus. \n
\n\n\n
\n\n\n
\n \n\n \n \n \n \n \n \n Architecture-aware synthesis of phase polynomials for NISQ devices.\n \n \n \n \n\n\n \n Meijer-van de Griend, A.; and Duncan, R.\n\n\n \n\n\n\n arXiv preprint arXiv:2004.06052. 2020.\n To appear in proceedings of QPL 2020 conference\n\n\n\n
\n\n\n\n \n \n \"Architecture-aware paper\n  \n \n \n \"Architecture-aware link\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@article{2004.06052,\r\n  title={Architecture-aware synthesis of phase polynomials for NISQ devices},\r\n  author={Meijer-van de Griend, Arianne and Duncan, Ross},\r\n  journal={arXiv preprint arXiv:2004.06052},\r\n  year={2020},\r\n  note={To appear in proceedings of QPL 2020 conference},\r\n  url_Paper = {https://arxiv.org/pdf/2004.06052.pdf},\r\n  url_Link = {https://www.youtube.com/watch?v=uOAA0nbh9MI},\r\n  abstract = {We propose a new algorithm to synthesise quantum circuits for phase polynomials, which takes\r\n  into account the qubit connectivity of the quantum computer. We focus on the architectures\r\n  of currently available NISQ devices. Our algorithm generates circuits with a smaller CNOT\r\n  depth than the algorithms currently used in Staq and t|ket>, while improving the runtime\r\n  with respect the former.}\r\n}
\n
\n\n\n
\n We propose a new algorithm to synthesise quantum circuits for phase polynomials, which takes into account the qubit connectivity of the quantum computer. We focus on the architectures of currently available NISQ devices. Our algorithm generates circuits with a smaller CNOT depth than the algorithms currently used in Staq and t|ket>, while improving the runtime with respect the former.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2019\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Constrained quantum CNOT circuit re-synthesis using deep reinforcement learning.\n \n \n \n \n\n\n \n Meijer-van de Griend, A.\n\n\n \n\n\n\n 2019.\n UNPUBLISHED, Master thesis Artificial Intelligence\n\n\n\n
\n\n\n\n \n \n \"Constrained paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@online{AIThesis,\r\nAuthor = {Meijer-van de Griend, Arianne},\r\nTitle = {Constrained quantum CNOT circuit re-synthesis using deep reinforcement learning},\r\nYear = {2019},\r\nEprint = {RG.2.2.11886.77125},\r\nEprinttype = {ResearchGate},\r\nnote = {UNPUBLISHED, Master thesis Artificial Intelligence},\r\nurl_Paper = {https://www.researchgate.net/publication/335977643_Constrained_quantum_CNOT_circuit_re-synthesis_using_deep_reinforcement_learning},\r\nabstract = {In this master thesis, we describe a novel approach to constrained CNOT circuit resynthesis as a first step towards neural constrained quantum circuit re-synthesis. We train a neural network to do constrained Gaussian elimination from a parity matrix using deep reinforcement learning. The CNOT circuit is transformed into a parity matrix from which an equivalent CNOT circuit is synthesized such that all CNOT gates adhere to the connectivity constraints provided by the quantum computer architecture. For our n-step deep Q learning approach, we have used an asynchronous dueling neural network with three different action selection policies: ϵ-greedy, softmax and a novel oracle selection policy. To train this neural network, we have proposed a novel phased training procedure that guides the training process from trivial problems to arbitrary ones while simulating. Although we were only able to successfully train an agent for trivial quantum computer connectivity constraints, the 2 and 3 qubit coupling graphs. We did show that those agents were able to perform similar to the genetic Steiner baseline and could even improve on them. We also investigated the effect of coupling graph sizes and connectivity on network performance and training time. Lastly, we show that transfer learning can result in an improved network, but it takes longer to train. This is a very promising start of a new research field that could result in a universal quantum circuit optimization and mapping algorithm that is robust to both expected and unexpected future changes in quantum computer architectures.}\r\n}\r\n\r\n
\n
\n\n\n
\n In this master thesis, we describe a novel approach to constrained CNOT circuit resynthesis as a first step towards neural constrained quantum circuit re-synthesis. We train a neural network to do constrained Gaussian elimination from a parity matrix using deep reinforcement learning. The CNOT circuit is transformed into a parity matrix from which an equivalent CNOT circuit is synthesized such that all CNOT gates adhere to the connectivity constraints provided by the quantum computer architecture. For our n-step deep Q learning approach, we have used an asynchronous dueling neural network with three different action selection policies: ϵ-greedy, softmax and a novel oracle selection policy. To train this neural network, we have proposed a novel phased training procedure that guides the training process from trivial problems to arbitrary ones while simulating. Although we were only able to successfully train an agent for trivial quantum computer connectivity constraints, the 2 and 3 qubit coupling graphs. We did show that those agents were able to perform similar to the genetic Steiner baseline and could even improve on them. We also investigated the effect of coupling graph sizes and connectivity on network performance and training time. Lastly, we show that transfer learning can result in an improved network, but it takes longer to train. This is a very promising start of a new research field that could result in a universal quantum circuit optimization and mapping algorithm that is robust to both expected and unexpected future changes in quantum computer architectures.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n
\n
\n  \n 2018\n \n \n (1)\n \n \n
\n
\n \n \n
\n \n\n \n \n \n \n \n \n Natural language generation for commercial applications.\n \n \n \n \n\n\n \n Meijer-van de Griend, A.\n\n\n \n\n\n\n 2018.\n UNPUBLISHED, Master thesis Computing Science\n\n\n\n
\n\n\n\n \n \n \"Natural paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
\n 
@online{CSThesis,\r\nAuthor = {Meijer-van de Griend, Arianne},\r\nTitle = {Natural language generation for commercial applications},\r\nYear = {2018},\r\nEprint = {RG.2.2.21953.10087},\r\nEprinttype = {ResearchGate},\r\nnote = {UNPUBLISHED, Master thesis Computing Science},\r\nurl_Paper = {https://www.researchgate.net/publication/335977746_Natural_language_generation_for_commercial_applications},\r\nabstract = {This master thesis gives an overview on natural language generation with the focus of dialogue systems for commercial use. \r\nWe give a description of the general approach to natural language generation and their neural architectures first.\r\nThen three application domains are discussed in more detail: language style transfer, dialogue response generation and controlling dialogue response generation. \r\nFor each domain, a use case was implemented and the results are discussed. We investigated automatic customer support, an empathetic automatic customer support and sentiment adjustment of reviews.\r\nWe show promising results for the first two use cases, but the last use case was inconclusive due to difficulties with implementation. \r\nWe finish with a short discussion of the use of natural language generation in commercial applications and what can be improved in our current model architectures.}\r\n}\r\n\r\n
\n
\n\n\n
\n This master thesis gives an overview on natural language generation with the focus of dialogue systems for commercial use. We give a description of the general approach to natural language generation and their neural architectures first. Then three application domains are discussed in more detail: language style transfer, dialogue response generation and controlling dialogue response generation. For each domain, a use case was implemented and the results are discussed. We investigated automatic customer support, an empathetic automatic customer support and sentiment adjustment of reviews. We show promising results for the first two use cases, but the last use case was inconclusive due to difficulties with implementation. We finish with a short discussion of the use of natural language generation in commercial applications and what can be improved in our current model architectures.\n
\n\n\n
\n\n\n\n\n\n
\n
\n\n\n\n\n
\n\n\n \n\n \n \n \n \n\n
\n"}; document.write(bibbase_data.data);