Learning interpretable representations of entanglement in quantum optics experiments using deep generative models

[1]  Alán Aspuru-Guzik,et al.  MPGVAE: improved generation of small organic molecules using message passing neural nets , 2021, Mach. Learn. Sci. Technol..

[2]  Sukin Sim,et al.  Noisy intermediate-scale quantum (NISQ) algorithms , 2021, Reviews of Modern Physics.

[3]  M. Cerezo,et al.  Variational quantum algorithms , 2020, Nature Reviews Physics.

[4]  Jian-Wei Pan,et al.  Quantum computational advantage using photons , 2020, Science.

[5]  Matthias Degroote,et al.  Natural Evolutionary Strategies for Variational Quantum Computation , 2020, Mach. Learn. Sci. Technol..

[6]  Alán Aspuru-Guzik,et al.  Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments , 2020, Physical Review X.

[7]  Benjamin J. Bucior,et al.  Inverse design of nanoporous crystalline reticular materials with deep generative models , 2020, Nature Machine Intelligence.

[8]  Mario Krenn,et al.  Computer-inspired quantum experiments , 2020, Nature Reviews Physics.

[9]  T. Jaakkola,et al.  Hierarchical Generation of Molecular Graphs using Structural Motifs , 2020, ICML.

[10]  Mario Krenn,et al.  Advances in high-dimensional quantum entanglement , 2019, 1911.10006.

[11]  Mario Krenn,et al.  Quantum Optical Experiments Modeled by Long Short-Term Memory , 2019, Photonics.

[12]  H. Wiseman,et al.  A strong no-go theorem on the Wigner’s friend paradox , 2019, Nature Physics.

[13]  Jian-Wei Pan,et al.  Quantum Teleportation in High Dimensions. , 2019, Physical review letters.

[14]  Ali Razavi,et al.  Generating Diverse High-Fidelity Images with VQ-VAE-2 , 2019, NeurIPS.

[15]  Alba Cervera-Lierta,et al.  Quantum circuits for maximally entangled states , 2019, Physical Review A.

[16]  Anthony Laing,et al.  Generation and sampling of quantum states of light in a silicon chip , 2018, Nature Physics.

[17]  R. Nichols,et al.  A hybrid machine learning algorithm for designing quantum experiments , 2018, Quantum Machine Intelligence.

[18]  J. Matthews,et al.  Designing quantum experiments with a genetic algorithm , 2018, Quantum Science and Technology.

[19]  Cynthia Rudin,et al.  Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead , 2018, Nature Machine Intelligence.

[20]  Mario Krenn,et al.  Experimental Greenberger–Horne–Zeilinger entanglement beyond qubits , 2018, Nature Photonics.

[21]  A. Zeilinger,et al.  Experimental Greenberger–Horne–Zeilinger entanglement beyond qubits , 2018, Nature Photonics.

[22]  Renato Renner,et al.  Discovering physical concepts with neural networks , 2018, Physical review letters.

[23]  Juan Miguel Arrazola,et al.  Machine learning method for state preparation and gate synthesis on photonic quantum computers , 2018, Quantum Science and Technology.

[24]  Alán Aspuru-Guzik,et al.  Inverse molecular design using machine learning: Generative models for matter engineering , 2018, Science.

[25]  Lalana Kagal,et al.  Explaining Explanations: An Overview of Interpretability of Machine Learning , 2018, 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA).

[26]  Qi Liu,et al.  Constrained Graph Variational Autoencoders for Molecule Design , 2018, NeurIPS.

[27]  Mario Krenn,et al.  Quantum experiments and graphs II: Quantum interference, computation, and state generation , 2018, Proceedings of the National Academy of Sciences.

[28]  Colin Raffel,et al.  A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music , 2018, ICML.

[29]  Razvan Pascanu,et al.  Learning Deep Generative Models of Graphs , 2018, ICLR 2018.

[30]  Niloy Ganguly,et al.  NeVAE: A Deep Generative Model for Molecular Graphs , 2018, AAAI.

[31]  Regina Barzilay,et al.  Junction Tree Variational Autoencoder for Molecular Graph Generation , 2018, ICML.

[32]  John Preskill,et al.  Quantum Computing in the NISQ era and beyond , 2018, Quantum.

[33]  Jian-Wei Pan,et al.  Satellite-to-Ground Entanglement-Based Quantum Key Distribution. , 2017, Physical review letters.

[34]  Mario Krenn,et al.  Active learning machine learns to create new quantum experiments , 2017, Proceedings of the National Academy of Sciences.

[35]  Mario Krenn,et al.  Quantum Experiments and Graphs: Multiparty States as Coherent Superpositions of Perfect Matchings. , 2017, Physical review letters.

[36]  Matt J. Kusner,et al.  Grammar Variational Autoencoder , 2017, ICML.

[37]  Stefano Ermon,et al.  Learning Hierarchical Features from Generative Models , 2017, ArXiv.

[38]  Erhardt Barth,et al.  A Hybrid Convolutional Variational Autoencoder for Text Generation , 2017, EMNLP.

[39]  Andy R. Terrel,et al.  SymPy: Symbolic computing in Python , 2017, PeerJ Prepr..

[40]  Christopher Burgess,et al.  beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework , 2016, ICLR 2016.

[41]  Alán Aspuru-Guzik,et al.  Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules , 2016, ACS central science.

[42]  Mario Krenn,et al.  Entanglement by Path Identity. , 2016, Physical review letters.

[43]  B. J. Metcalf,et al.  Distinguishability and Many-Particle Interference. , 2016, Physical review letters.

[44]  Ole Winther,et al.  Ladder Variational Autoencoders , 2016, NIPS.

[45]  Samy Bengio,et al.  Generating Sentences from a Continuous Space , 2015, CoNLL.

[46]  Jing Liu,et al.  A search algorithm for quantum state engineering and metrology , 2015, 1511.05327.

[47]  E. Knill,et al.  A strong loophole-free test of local realism , 2015, 2016 Conference on Lasers and Electro-Optics (CLEO).

[48]  A. Zeilinger,et al.  Significant-Loophole-Free Test of Bell's Theorem with Entangled Photons. , 2015, Physical review letters.

[49]  A. Zeilinger,et al.  Automated Search for new Quantum Experiments. , 2015, Physical review letters.

[50]  A. Zeilinger,et al.  Multi-photon entanglement in high dimensions , 2015, Nature Photonics.

[51]  Max Welling,et al.  Semi-supervised Learning with Deep Generative Models , 2014, NIPS.

[52]  D. Gauthier,et al.  High-dimensional quantum cryptography with twisted light , 2014, 1402.7113.

[53]  Max Welling,et al.  Auto-Encoding Variational Bayes , 2013, ICLR.

[54]  W. Cui,et al.  Absolutely Maximally Entangled States: Existence and Applications , 2013, 1306.2536.

[55]  A. Zeilinger,et al.  Generation and confirmation of a (100 × 100)-dimensional entangled quantum system , 2013, Proceedings of the National Academy of Sciences.

[56]  Alán Aspuru-Guzik,et al.  A variational eigenvalue solver on a photonic quantum processor , 2013, Nature Communications.

[57]  Marcus Huber,et al.  Structure of multidimensional entanglement in multipartite systems. , 2012, Physical review letters.

[58]  Chong Wang,et al.  Stochastic variational inference , 2012, J. Mach. Learn. Res..

[59]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[60]  M. J. Padgett,et al.  Increasing the dimension in high-dimensional two-photon orbital angular momentum entanglement , 2012, 1205.1968.

[61]  Siddhartha Santra,et al.  Quantum entanglement in random physical states. , 2011, Physical review letters.

[62]  Cheng-Zhi Peng,et al.  Observation of eight-photon entanglement , 2011, Nature Photonics.

[63]  Adetunmise C. Dada,et al.  Experimental high-dimensional two-photon entanglement and violations of generalized Bell inequalities , 2011, 1104.5087.

[64]  Gilson A. Giraldi,et al.  Genetic Algorithms and Quantum Computation , 2004, ArXiv.

[65]  H. Weinfurter,et al.  Multiphoton entanglement and interferometry , 2003, 0805.2853.

[66]  S. Barnett,et al.  Measuring the orbital angular momentum of a single photon. , 2002, Physical review letters.

[67]  J. Cirac,et al.  Three qubits can be entangled in two inequivalent ways , 2000, quant-ph/0005115.

[68]  C. Hong,et al.  Generation of correlated photons via four-wave mixing in optical fibres , 2000, QELS 2000.

[69]  H. Weinfurter,et al.  Observation of three-photon Greenberger-Horne-Zeilinger entanglement , 1998, quant-ph/9810035.

[70]  Herzog,et al.  Frustrated two-photon creation via interference. , 1994, Physical review letters.

[71]  D. W. Scott,et al.  Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .

[72]  J. P. Woerdman,et al.  Orbital angular momentum of light and the transformation of Laguerre-Gaussian laser modes. , 1992, Physical review. A, Atomic, molecular, and optical physics.

[73]  L. Mandel,et al.  Induced coherence without induced emission. , 1991, Physical review. A, Atomic, molecular, and optical physics.

[74]  David Weininger,et al.  SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules , 1988, J. Chem. Inf. Comput. Sci..

[75]  Ken Shoemake,et al.  Animating rotation with quaternion curves , 1985, SIGGRAPH.

[76]  R. C. Miller,et al.  Tunable Coherent Parametric Oscillation in LiNb O 3 at Optical Frequencies , 1965 .

[77]  E. Schrödinger.  Discussion of Probability Relations between Separated Systems , 1935, Mathematical Proceedings of the Cambridge Philosophical Society.

[78]  Albert Einstein,et al.  Can Quantum-Mechanical Description of Physical Reality Be Considered Complete? , 1935 .

[79]  Mario Krenn,et al.  Observation of nonlocal quantum interference between the origins of a four-photon state in a silicon chip , 2021 .

[80]  Jakob S. Kottmann,et al.  Conceptual Understanding through Efficient Automated Design of Quantum Optical Experiments , 2021 .

[81]  K. V. Rashmi,et al.  USENIX Symposium on Operating Systems Design and Implementation , 2020 .

[82]  J. S. BELLt.  Einstein-Podolsky-Rosen Paradox , 2018 .

[83]  A. EINsTEIN,et al.  Can Quantum-Mechanical Description of Physical Reality Be Considered Complete ' ? , 2011 .

[84]  Ruslan Salakhutdinov,et al.  Learning Deep Generative Models , 2009 .

[85]  Hitoshi Iba,et al.  Genetic Algorithms for Quantum Circuit Design –Evolving a Simpler Teleportation Circuit– , 2000 .

[86]  M. Hasselmo,et al.  Gaussian Processes for Regression , 1995, NIPS.

[87]  J. Simonoff.  Multivariate Density Estimation , 1996 .

[88]  Andy Davis,et al.  TensorFlow: A system for large-scale machine learning , 2016, OSDI.