{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/continuous-variable-quantum-neural-networks","title":"Continuous-variable quantum neural networks","arxiv_id":"1806.06871","date":"2018-06-18","proceeding":null,"authors":["Nathan Killoran","Thomas R. Bromley","Juan Miguel Arrazola","Maria Schuld","Nicolás Quesada","Seth Lloyd"],"abstract":"We introduce a general method for building neural networks on quantum\ncomputers. The quantum neural network is a variational quantum circuit built in\nthe continuous-variable (CV) architecture, which encodes quantum information in\ncontinuous degrees of freedom such as the amplitudes of the electromagnetic\nfield. This circuit contains a layered structure of continuously parameterized\ngates which is universal for CV quantum computation. Affine transformations and\nnonlinear activation functions, two key elements in neural networks, are\nenacted in the quantum network using Gaussian and non-Gaussian gates,\nrespectively. The non-Gaussian gates provide both the nonlinearity and the\nuniversality of the model. Due to the structure of the CV model, the CV quantum\nneural network can encode highly nonlinear transformations while remaining\ncompletely unitary. We show how a classical network can be embedded into the\nquantum formalism and propose quantum versions of various specialized model\nsuch as convolutional, recurrent, and residual networks. Finally, we present\nnumerous modeling experiments built with the Strawberry Fields software\nlibrary. These experiments, including a classifier for fraud detection, a\nnetwork which generates Tetris images, and a hybrid classical-quantum\nautoencoder, demonstrate the capability and adaptability of CV quantum neural\nnetworks.","url_abs":"http://arxiv.org/abs/1806.06871v1","url_pdf":"http://arxiv.org/pdf/1806.06871v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"continuous-variable-quantum-neural-networks","repo_url":"https://github.com/XanaduAI/quantum-learning","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"continuous-variable-quantum-neural-networks","repo_url":"https://github.com/XanaduAI/quantum-neural-networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"continuous-variable-quantum-neural-networks","repo_url":"https://github.com/lewis-od/QNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"continuous-variable-quantum-neural-networks","repo_url":"https://github.com/sophchoe/Binary_Classification_Pennylane_Keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"continuous-variable-quantum-neural-networks","repo_url":"https://github.com/sophchoe/Hybrid-Quantum-Classical-MNIST-Classfication-Model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"continuous-variable-quantum-neural-networks","repo_url":"https://github.com/sophchoe/QML","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"continuous-variable-quantum-neural-networks","repo_url":"https://github.com/sophchoe/continous-variable-quantum-mnist-classifiers","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"continuous-variable-quantum-neural-networks","repo_url":"https://github.com/zaheerkhancs/QuantumNeuralNetwork","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"fraud-detection","task_name":"Fraud Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1806.06871","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.06871"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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