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Nature Communications 13(1):7760. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-022-35364-5. Number: 1 Publisher: Nature Publishing Group. Accessed 2022-12-15\nArrasmith, A., Cerezo, M., Czarnik, P., Cincio, L., Coles, P.J.: Effect of barren plateaus on gradient-free optimization. Quantum 5, 558 (2021). 10.22331\u002Fq-2021-10-05-558\nArrasmith A, Holmes Z, Cerezo M, Coles PJ (2022) Equivalence of quantum barren plateaus to cost concentration and narrow gorges. Quantum Science and Technology 7(4):045015. https:\u002F\u002Fdoi.org\u002F10.1088\u002F2058-9565\u002Fac7d06\nBasu, S., Saha, A., Chakrabarti, A., Sur-Kolay, S.: \\(i\\)-QER: An Intelligent Approach towards Quantum Error Reduction. arXiv:2110.06347 (2022). 10.48550\u002FarXiv.2110.0634\nBeckey JL, Gigena N, Coles PJ, Cerezo M (2021) Computable and Operationally Meaningful Multipartite Entanglement Measures. Phys. Rev. Letters 127(14):140501. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPhysRevLett.127.140501\nBergholm, V., Izaac, J., Schuld, M., Gogolin, C., Alam, M.S., Ahmed, S., Arrazola, J.M., Blank, C., Delgado, A., Jahangiri, S., McKiernan, K., Meyer, J.J., Niu, Z., Száva, A., Killoran, N.: PennyLane: Automatic differentiation of hybrid quantum-classical computations. http:\u002F\u002Farxiv.org\u002Fabs\u002F1811.04968arXiv:1811.04968 (2020). 10.48550\u002FarXiv.1811.04968\nBotea A, Kishimoto A, Marinescu R (2018) On the Complexity of Quantum Circuit Compilation. Proceedings of the International Symposium on Combinatorial Search 9(1):138–142. https:\u002F\u002Fdoi.org\u002F10.1609\u002Fsocs.v9i1.18463\nBravyi S, Smith G, Smolin JA (2016) Trading Classical and Quantum Computational Resources. Phys. Rev. X 6(2):021043. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPhysRevX.6.021043\nBroers, L., Mathey, L.: Reducing Barren Plateaus in Quantum Algorithm Protocols. http:\u002F\u002Farxiv.org\u002Fabs\u002F2111.08085arXiv:2111.08085 (2021). 10.48550\u002FarXiv.2111.08085\nCerezo, M., Arrasmith, A., Babbush, R., Benjamin, S.C., Endo, S., Fujii, K., McClean, J.R., Mitarai, K., Yuan, X., Cincio, L., Coles, P.J.: Variational quantum algorithms. Nature Reviews Physics, 625–644 (2021). 10.1038\u002Fs42254-021-00348-9\nCerezo M, Sone A, Volkoff T, Cincio L, Coles PJ (2021) Cost function dependent barren plateaus in shallow parametrized quantum circuits. Nature Communications 12(1):1791. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-021-21728-w\nCong I, Choi S, Lukin MD (2019) Quantum convolutional neural networks. Nature Physics 15(12):1273–1278. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41567-019-0648-8\nEddins A, Motta M, Gujarati TP, Bravyi S, Mezzacapo A, Hadfield C, Sheldon S (2022) Doubling the size of quantum simulators by entanglement forging. PRX Quantum 3:010309. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPRXQuantum.3.010309\nFarhi, E., Goldstone, J., Gutmann, S.: A Quantum Approximate Optimization Algorithm. http:\u002F\u002Farxiv.org\u002Fabs\u002F1411.4028arXiv:1411.4028 (2014)\nFarhi, E., Neven, H.: Classification with Quantum Neural Networks on Near Term Processors. http:\u002F\u002Farxiv.org\u002Fabs\u002F1802.06002arXiv:1802.06002 (2018)\nFujii K, Mizuta K, Ueda H, Mitarai K, Mizukami W, Nakagawa YO (2022) Deep Variational Quantum Eigensolver: A Divide-And-Conquer Method for Solving a Larger Problem with Smaller Size Quantum Computers. PRX Quantum 3(1):010346. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPRXQuantum.3.010346\nGrant, E., Ostaszewski, M., Wossnig, L., Benedetti, M.: An initialization strategy for addressing barren plateaus in parametrized quantum circuits. Quantum 3, 214 (2019). 10.22331\u002Fq-2019-12-09-214\nGrant E, Benedetti M, Cao S, Hallam A, Lockhart J, Stojevic V, Green AG, Severini S (2018) Hierarchical quantum classifiers. npj Quantum. Information 4(1):17–19. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41534-018-0116-9\nHaferkamp, J., Faist, P., Kothakonda, N.B.T., Eisert, J., Yunger Halpern, N.: Linear growth of quantum circuit complexity. Nature Physics 18(5), 528–532 (2022). 10.1038\u002Fs41567-022-01539-6\nHolmes Z, Sharma K, Cerezo M, Coles PJ (2022) Connecting Ansatz Expressibility to Gradient Magnitudes and Barren Plateaus. PRX Quantum 3(1):010313. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPRXQuantum.3.010313\nKandala A, Mezzacapo A, Temme K, Takita M, Brink M, Chow JM, Gambetta JM (2017) Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets. Nature 549(7671):242–246. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnature23879\nKingma, D.P., Ba, J.: Adam: A Method for Stochastic Optimization. http:\u002F\u002Farxiv.org\u002Fabs\u002F1412.6980arXiv:1412.6980 (2017)\nLarocca, M., Ju, N., García-Martín, D., Coles, P.J., Cerezo, M.: Theory of overparametrization in quantum neural networks. arXiv:2109.11676 [quant-ph, stat] (2021). Accessed 2021-09-30\nLiu H-Y, Sun T-P, Wu Y-C, Han Y-J, Guo G-P (2023) Mitigating barren plateaus with transfer-learning-inspired parameter initializations. New Journal of Physics 25(1):013039. https:\u002F\u002Fdoi.org\u002F10.1088\u002F1367-2630\u002Facb58e\nMarshall, S.C., Gyurik, C., Dunjko, V.: High Dimensional Quantum Learning With Small Quantum Computers. http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.13739arXiv:2203.13739 (2022). 10.48550\u002FarXiv.2203.13739\nMcClean JR, Boixo S, Smelyanskiy VN, Babbush R, Neven H (2018) Barren plateaus in quantum neural network training landscapes. Nature Communications 9(1):4812. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-018-07090-4\nMitarai K, Negoro M, Kitagawa M, Fujii K (2018) Quantum circuit learning. Phys. Rev. A 98(3):032309. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPhysRevA.98.032309\nOrtiz Marrero C, Kieferová M, Wiebe N (2021) Entanglement-Induced Barren Plateaus. PRX. Quantum 2(4):040316. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPRXQuantum.2.040316\nPaszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., Chintala, S.: PyTorch: An Imperative Style, High-Performance Deep Learning Library. In: Wallach, H., Larochelle, H., Beygelzimer, A., Alché-Buc, F.d., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019). https:\u002F\u002Fproceedings.neurips.cc\u002Fpaper\u002F2019\u002Ffile\u002Fbdbca288fee7f92f2bfa9f7012727740-Paper.pdf\nPatti TL, Najafi K, Gao X, Yelin SF (2021) Entanglement devised barren plateau mitigation. Phys. Rev. Research 3(3):033090. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPhysRevResearch.3.033090\nPedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, Prettenhofer P, Weiss R, Dubourg V, Vanderplas J, Passos A, Cournapeau D, Brucher M, Perrot M, Duchesnay E (2011) Scikit-learn: Machine Learning in Python. Journal of Machine Learning Research 12(85):2825–2830\nPeng T, Harrow AW, Ozols M, Wu X (2020) Simulating Large Quantum Circuits on a Small Quantum Computer. Phys. Rev. Letters 125(15):150504. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPhysRevLett.125.150504\nPérez-Salinas, A., Cervera-Lierta, A., Gil-Fuster, E., Latorre, J.I.: Data re-uploading for a universal quantum classifier. 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PRX Quantum 3:020365. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPRXQuantum.3.020365\nSaleem, Z.H., Tomesh, T., Perlin, M.A., Gokhale, P., Suchara, M.: Quantum Divide and Conquer for Combinatorial Optimization and Distributed Computing. http:\u002F\u002Farxiv.org\u002Fabs\u002F2107.07532arXiv:2107.07532 (2021). 10.48550\u002FarXiv.2107.07532\nSchatzki, L., Arrasmith, A., Coles, P.J., Cerezo, M.: Entangled Datasets for Quantum Machine Learning. http:\u002F\u002Farxiv.org\u002Fabs\u002F2109.03400arXiv:2109.03400 (2021). 10.48550\u002FarXiv.2109.03400\nSchuld M, Bergholm V, Gogolin C, Izaac J, Killoran N (2019) Evaluating analytic gradients on quantum hardware. Phys. Rev. A 99(3):1–7. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPhysRevA.99.032331\nTang, W., Tomesh, T., Suchara, M., Larson, J., Martonosi, M.: CutQC: Using Small Quantum Computers for Large Quantum Circuit Evaluations. Proceedings of the 26th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 473–486 (2021). 10.1145\u002F3445814.3446758\nTreinish, M., Gambetta, J., Nation, P., Kassebaum, P., qiskit-bot, Rodríguez, D.M., González, S.d.l.P., Hu, S., Krsulich, K., Zdanski, L., Garrison, J., Yu, J., Gacon, J., McKay, D., Gomez, J., Capelluto, L., Travis-S-IBM, Marques, M., Panigrahi, A., Lishman, J., lerongil, Rahman, R.I., Wood, S., Bello, L., Itoko, T., Singh, D., Drew, Arbel, E., Schwarm, J., Daniel, J.: Qiskit: An Open-source Framework for Quantum Computing. Zenodo (2022). 10.5281\u002Fzenodo.6403335. https:\u002F\u002Fzenodo.org\u002Frecord\u002F6403335\nVolkoff T, Coles PJ (2021) Large gradients via correlation in random parameterized quantum circuits. Quantum Science and Technology 6(2):025008. https:\u002F\u002Fdoi.org\u002F10.1088\u002F2058-9565\u002Fabd891\nWang S, Fontana E, Cerezo M, Sharma K, Sone A, Cincio L, Coles PJ (2021) Noise-induced barren plateaus in variational quantum algorithms. Nature Communications 12(1):6961. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41467-021-27045-6\nWeidenfeller, J., Valor, L.C., Gacon, J., Tornow, C., Bello, L., Woerner, S., Egger, D.J.: Scaling of the quantum approximate optimization algorithm on superconducting qubit based hardware. Quantum 6, 870 (2022). 10.22331\u002Fq-2022-12-07-870\nWu, A., Li, G., Ding, Y., Xie, Y.: Mitigating Noise-Induced Gradient Vanishing in Variational Quantum Algorithm Training. arXiv:2111.13209 (2021)\nZhang, K., Hsieh, M.-H., Liu, L., Tao, D.: Gaussian initializations help deep variational quantum circuits escape from the barren plateau. http:\u002F\u002Farxiv.org\u002Fabs\u002F2203.09376arXiv:2203.09376 (2022). 10.48550\u002FarXiv.2203.09376\nZhang, K., Hsieh, M.-H., Liu, L., Tao, D.: Toward Trainability of Deep Quantum Neural Networks. http:\u002F\u002Farxiv.org\u002Fabs\u002F2112.15002http:\u002F\u002Farxiv.org\u002Fabs\u002F2112.15002arXiv:2112.15002 (2021)\nZhao, C., Gao, X.-S.: Analyzing the barren plateau phenomenon in training quantum neural networks with the ZX-calculus. Quantum 5, 466 (2021). 10.22331\u002Fq-2021-06-04-466",{"EN":168},"Barren plateaus appear to be a major obstacle for using variational quantum algorithms to simulate large-scale quantum systems or to replace traditional machine learning algorithms. They can be caused by multiple factors such as the expressivity of the ansatz, excessive entanglement, the locality of observables under consideration, or even hardware noise. We propose classical splitting of parametric ansatz circuits to avoid barren plateaus. Classical splitting is realized by subdividing an N qubit ansatz into multiple ansätze that consist of \n                \n                  \n                \n                $$\\mathcal {O}(\\log N)$$\n                \n               qubits. We show that such an approach allows for avoiding barren plateaus and carry out numerical experiments, and perform binary classification on classical and quantum datasets. Moreover, we propose an extension of the ansatz that is compatible with variational quantum simulations. Finally, we discuss a speed-up for gradient-based optimization and hardware implementation, robustness against noise and parallelization, making classical splitting an ideal tool for noisy intermediate scale quantum (NISQ) applications.",{"EN":170},"Classical splitting of parametrized quantum circuits",{"VOID":172},"10.1007\u002Fs42484-023-00118-z","PUBLICATION","VERIFIED","Auto 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M, Denil M, Gomez S, Hoffman MW, Pfau D, Schaul T, Shillingford B, De Freitas N (2016) Learning to learn by gradient descent by gradient descent. In: Advances in neural information processing systems\nBaxter J (2000) A model of inductive bias learning. J Artif Intell Res 12:149–198\nCarleo G, Troyer M (2017) Solving the quantum many-body problem with artificial neural networks. Science 355(6325):602–606\nCaruana R (1997) Multitask learning. Mach Learn 28(1):41–75\nFallah A, Mokhtari A, Ozdaglar A (2020) On the convergence theory of gradient-based model-agnostic meta-learning algorithms. In: International conference on artificial intelligence and statistics, pp 1082–1092\nFinn C, Abbeel P, Levine S (2017) Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th international conference on machine learning. JMLR. org, vol 70, pp 1126–1135\nFlennerhag S, Rusu AA, Pascanu R, Visin F, Yin H, Hadsell R (2019) Meta-learning with warped gradient descent. arXiv:1909.00025\nGomes J, McKiernan KA, Eastman P, Pande VS (2019) Classical quantum optimization with neural network quantum states. arXiv:1910.10675\nKakade SM (2001) A natural policy gradient. Adv Neural Inf process Syst 14\nLuo D, Clark BK (2019) Backflow transformations via neural networks for quantum many-body wave functions. Phys Rev Lett 122(22):226401\nMcCloskey M, Cohen NJ (1989) Catastrophic interference in connectionist networks: the sequential learning problem. In: Psychology of learning and motivation, vol 24, pp 109–165\nMcMillan WL (1965) Ground state of liquid he4. Phys Rev 138:442–451\nNichol A, Achiam J, Schulman J (2018) On first-order meta-learning algorithms. arXiv:1803.02999\nNomura Y, Darmawan AS, Yamaji Y, Imada M (2017) Restricted Boltzmann machine learning for solving strongly correlated quantum systems. Phys Rev B 96(20):205152\nPfau D, Spencer JS, Matthews AG, Foulkes WMC (2020) AB initio solution of the many-electron Schrödinger equation with deep neural networks. Phys Rev Res 2(3):033429\nRendl F, Rinaldi G, Wiegele A (2010) Solving max-cut to optimality by intersecting semidefinite and polyhedral relaxations. Math Program 121(2):307\nSharir O, Levine Y, Wies N, Carleo G, Shashua A (2020) Deep autoregressive models for the efficient variational simulation of many-body quantum systems. Phys Rev Lett 124(2):020503\nSorella S (1998) Green function Monte Carlo with stochastic reconfiguration. Phys Rev Lett 80 (20):4558–4561\nStokes J, Moreno JR, Pnevmatikakis EA, Carleo G (2020) Phases of two-dimensional spinless lattice fermions with first-quantized deep neural-network quantum states. Phys Rev B 102(20):205122\nThrun S, Pratt L (2012) Learning to learn. Springer Science and Business Media, Berlin\nVerdon G, Broughton M, McClean JR, Sung KJ, Babbush R, Jiang Z, Neven H, Mohseni M (2019) Learning to learn with quantum neural networks via classical neural networks. arXiv:1907.05415\nWierstra D, Schaul T, Glasmachers T, Sun Y, Peters J, Schmidhuber J (2014) Natural evolution strategies. J Mach Learn Res 15(1):949–980\nWilliams R (1988) Toward a theory of reinforcement-learning connectionist systems. Technical Report NU-CCS-88-3 Northeastern University\nWilliams RJ (1992) Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach Learn 8(3):229–256\nWilson M, Stromswold R, Wudarski F, Hadfield S, Tubman NM, Rieffel EG (2021) Optimizing quantum heuristics with meta-learning. Quantum Mach Intell 3(1):1–14\nZhao T, Carleo G, Stokes J, Veerapaneni S (2020a) Natural evolution strategies and variational Monte Carlo. Mach Learn Sci Technol 2(2):02–01\nZhao T, Stokes J, Knitter O, Chen B, Veerapaneni S (2020b) Meta variational Monte Carlo. Third Workshop on Machine Learning and the Physical Sciences (NeurIPS 2020)\nZhao T, De S, Chen B, Stokes J, Veerapaneni S (2021) Overcoming barriers to scalability in variational quantum Monte Carlo. In: The international conference for high performance computing, networking, storage, and analysis",{"EN":347},"Motivated by close analogies between meta-reinforcement learning (Meta-RL) and variational quantum Monte Carlo with disorder, we propose a learning problem and an associated notion of generalization, with applications in ground state determination for quantum systems described by random Hamiltonians. Specifically, we elaborate on a proposal of (Zhao et al. 2020b) interpreting the Hamiltonian disorder as task uncertainty for a Meta-RL agent. A model-agnostic meta-learning approach is proposed to solve the associated learning problem and numerical experiments in disordered quantum spin systems indicate that the resulting meta-variational Monte Carlo accelerates training and improves converged energies.",{"EN":349},"Meta-variational quantum Monte 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J (2018) Quantum computing in the NISQ era and beyond. Quantum 2:79\nArute F et al (2019) Quantum supremacy using a programmable superconducting processor. Nature 574(7779):505–510\nZhong H-S et al (2020) Quantum computational advantage using photons. Science 370(6523):1460–1463\nBroadbent A, Fitzsimons J, Kashefi E (2009) Universal blind quantum computation. In: 2009 50th Annual IEEE Symposium on Foundations of Computer Science. IEEE, pp 517–526\nArrighi P, Salvail L (2006) Blind quantum computation. Int J Quantum Inf 4(05):883–898\nMorimae T, Fujii K (2013) Blind quantum computation protocol in which alice only makes measurements. Phys Rev A 87(5):050301\nSchmidhuber J (2015) Deep learning in neural networks: An overview. Neural Netw 61:85–117\nRomero J, Olson JP, Aspuru-Guzik A (2017) Quantum autoencoders for efficient compression of quantum data. Quantum Sci Technol 2(4):045001\nBottou L (2012) Stochastic gradient descent tricks. In: Neural Networks: Tricks of the Trade. Springer, pp 421–436\nArmbrust M, Fox A, Griffith R, Joseph AD, Katz R, Konwinski A, Lee G, Patterson D, Rabkin A, Stoica I et al (2010) A view of cloud computing. Commun ACM 53(4):50–58\nBarz S, Kashefi E, Broadbent A, Fitzsimons JF, Zeilinger A, Walther P (2012) Demonstration of blind quantum computing. Science 335(6066):303–308\nYang Y, Chiribella G, Hayashi M (2020) Communication cost of quantum processes. IEEE J Sel Areas Inf Theory 1(2):387–400\nSheng Y-B, Zhou L (2017) Distributed secure quantum machine learning. Sci Bull 62(14):1025–1029\nBondarenko D, Feldmann P (2020) Quantum autoencoders to denoise quantum data. Phys Rev Lett 124(13):130502\nAchache T, Horesh L, Smolin J (2020) Denoising quantum states with Quantum Autoencoders–Theory and Applications . arXiv preprint arXiv:2012.14714\nNielsen MA, Chuang IL (1997) Programmable quantum gate arrays. Phys Rev Lett 79(2):321\nYang Y, Renner R, Chiribella G (2020) Optimal universal programming of unitary gates. Phys Rev Lett 125(21):210501\nChoi M-D (1975) Completely positive linear maps on complex matrices. Linear Algebra Appl 10(3):285–290\nJamiołkowski A (1972) Linear transformations which preserve trace and positive semidefiniteness of operators. Rep Math Phys 3(4):275–278\nNielsen MA, Chuang I (2002) Quantum computation and quantum information. American Association of Physics Teachers\nChiribella G, D’Ariano GM, Perinotti P (2008) Transforming quantum operations: Quantum supermaps. EPL Europhys Lett 83(3):30004\nChiribella G, D’Ariano GM, Perinotti P (2009) Theoretical framework for quantum networks. Phys Rev A 80(2):022339\nCong I, Choi S, Lukin MD (2019) Quantum convolutional neural networks. Nat Phys 15(12):1273–1278\nFarhi E, Goldstone J, Gutmann S (2014) A quantum approximate optimization algorithm. arXiv preprint arXiv:1411.4028\nDebnath S, Linke NM, Figgatt C, Landsman KA, Wright K, Monroe C (2016) Demonstration of a small programmable quantum computer with atomic qubits. Nature 536(7614):63–66\nKiefer J, Wolfowitz J et al (1952) Stochastic estimation of the maximum of a regression function. Ann Math Stat 23(3):462–466\nMcClean JR, Boixo S, Smelyanskiy VN, Babbush R, Neven H (2018) Barren plateaus in quantum neural network training landscapes. Nat Commun 9(1):4812\nBroughton M, Verdon G, McCourt T, Martinez AJ, Yoo JH, Isakov SV, Massey P, Halavati R, Niu MY, Zlokapa A et al (2020) Tensorflow quantum: A software framework for quantum machine learning. arXiv preprint arXiv:2003.02989\nBaxter RJ (2007) Exactly Solved Models in Statistical Mechanics. Courier Corporation\nWilliamson DF, Parker RA, Kendrick JS (1989) The box plot: a simple visual method to interpret data. Ann Intern Med 110(11):916–921\nBisio A, Chiribella G, D’Ariano GM, Facchini S, Perinotti P (2010) Optimal quantum learning of a unitary transformation. Phys Rev A 81(3):032324\nMo Y, Chiribella G (2019) Quantum-enhanced learning of rotations about an unknown direction. New J Phys 21(11):113003\nSedlák M, Ziman M (2020) Probabilistic storage and retrieval of qubit phase gates. Phys Rev A 102(3):032618\nBishop LS, Bravyi S, Cross A, Gambetta JM, Smolin J (2017) Quantum volume. Quantum Volume, Technical Report\nMoll N, Barkoutsos P, Bishop LS, Chow JM, Cross A, Egger DJ, Filipp S, Fuhrer A, Gambetta JM, Ganzhorn M et al (2018) Quantum optimization using variational algorithms on near-term quantum devices. Quantum Sci Technol 3(3):030503\nAllen-Zhu Z (2017) Natasha 2: Faster non-convex optimization than sgd. arXiv preprint arXiv:1708.08694\nSweke R, Wilde F, Meyer JJ, Schuld M, Fährmann PK, Meynard-Piganeau B, Eisert J (2020) Stochastic gradient descent for hybrid quantum-classical optimization. Quantum 4:314\nDevelopers Cirq (2022). Cirq Zenodo. https:\u002F\u002Fdoi.org\u002F10.5281\u002FZENODO.7465577\nMitarai K, Negoro M, Kitagawa M, Fujii K (2018) Quantum circuit learning. Physical Review A 98(3):032309",{"EN":449},"In the model of quantum cloud computing, the server executes a computation on the quantum data provided by the client. In this scenario, it is important to reduce the amount of quantum communication between the client and the server. A possible approach is to transform the desired computation into a compressed version that acts on a smaller number of qubits, thereby reducing the amount of data exchanged between the client and the server. Here we propose quantum autoencoders for quantum gates (QAEGate) as a method for compressing quantum computations. We illustrate it in concrete scenarios of single-round and multi-round communication and validate it through numerical experiments. A bonus of our method is it does not reveal any information about the server’s computation other than the information present in the output.",{"EN":451},"Quantum autoencoders for communication-efficient cloud 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China",{},{"id":20,"sortIndex":21,"affiliation":473,"properties":20},{"id":474,"createTime":475,"updateTime":475,"relativeEntities":476,"slug":20,"properties":477,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"12e23ff7-c058-4e4c-b740-edd0701d2cf5","2024-02-21T04:28:45.175+00:00",[],{"title":478},{"VI":479},"AI Technology Lab Department of Computer Science, The University of Hong Kong, Hong Kong, China",{"title":481},{"VI":482},"Yuexuan 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J, Das H, Milenkovic O, Orlitsky A, Pan S (2010) On reconstructing a string from its substring compositions. 1238–1242 07\nAghabozorgi S, Shirkhorshidi AS, Wah TY (2015) Time-series clustering – a decade review. Inf Syst 53:16–38\nAharonov D, Van Dam W, Kempe J, Landau Z, Lloyd S, Regev O (2008) Adiabatic quantum computation is equivalent to standard quantum computation. SIAM Rev 50(4):755–787\nAimeur E, Brassard G, Gambs S (2013) Quantum speed-up for unsupervised learning. Mach Learn 90(02):261–287\nAL G, LAN A, L G, JM I, Hausdorff amd PC, RG M, JE M, Moody G, C-K P, Stanley H (2003) Mit-bih long-term ecg database. physionet.org\u002Fcontent\u002Fltdb\u002F1.0.0\u002F. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220\nAlexander C, Shi L, Akhmametyeva S (2018) Using quantum mechanics to cluster time series. arXiv\nArute F, Arya K, Babbush R, Bacon D, Bardin JC, Barends R, Biswas R, Boixo S, Brandao FG, Buell DA et al (2019) Quantum supremacy using a programmable superconducting processor. Nature 574(7779):505–510\nBagnall A, Lines J, Bostrom A, Large J, Keogh E (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Mining Knowl Disc 31 (3):606–660\nBagnall A, Lines J, Vickers W, Keogh E The uea & ucr time series classification repository. www.timeseriesclassification.com. Accessed: 2020-02-01\nBarahona F (1982) On the computational complexity of ising spin glass models. J Phys A Math Gen 15(10):3241\nChinatown DHA (2020). . http:\u002F\u002Fwww.pedestrian.melbourne.vic.gov.au. Accessed: 2020-02-01\nChrist M, Braun N, Neuffer J, Kempa-Liehr AW (2018) Time series feature extraction on basis of scalable hypothesis tests (tsfresh – a python package). Neurocomputing 307:72–77\nChu S, Keogh EJ, Hart DM, Pazzani MJ (2002) Iterative deepening dynamic time warping for time series. 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In: Proceedings of the 4th world congress on intelligent control and automation (Cat. no.02EX527) volume 3, vol 3, pp 2190–2193",{"EN":645},"In this paper we develop methods to solve two problems related to time series (TS) analysis using quantum computing: reconstruction and classification. We formulate the task of reconstructing a given TS from a training set of data as an unconstrained binary optimization (QUBO) problem, which can be solved by both quantum annealers and gate-model quantum processors. We accomplish this by discretizing the TS and converting the reconstruction to a set cover problem, allowing us to perform a one-versus-all method of reconstruction. Using the solution to the reconstruction problem, we show how to extend this method to perform semi-supervised classification of TS data. We present results indicating our method is competitive with current semi- and unsupervised classification techniques, but using less data than classical techniques.",{"EN":647},"Semi-supervised time series classification method for quantum computing",{"VOID":649},"10.1007\u002Fs42484-021-00042-0","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42484-021-00042-0",[652,677,689,701,723],{"id":653,"sortIndex":21,"researcher":20,"roles":654,"affiliations":655,"properties":674},"96f140fa-5e0f-476a-ba5c-e05c0f9aee55",[181],[656,664],{"id":20,"sortIndex":21,"affiliation":657,"properties":20},{"id":658,"createTime":659,"updateTime":659,"relativeEntities":660,"slug":20,"properties":661,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"9deb95d2-4cdc-4d36-8478-dc81feb3c267","2024-02-06T12:31:38.217+00:00",[],{"title":662},{"VI":663},"Volkswagen Data:Lab, Munich, 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T, Boixo S, Lidar DA, Zanardi P (2012) Quantum adiabatic markovian master equations. 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Quantum Science and Technology 3(1):014004\nvan Apeldoorn J, Gilyén A (2018) Improvements in quantum sdp-solving with applications. arXiv:1804.05058\nvan Apeldoorn J, Gilyén A (2019) Quantum algorithms for zero-sum games. arXiv:1904.03180\nvan Apeldoorn J, Gilyén A, Gribling S, De Wolf R (2017) Sdp-solvers: Quantum Better upper and lower bounds. In: Foundations of computer science (FOCS), 2017 IEEE 58th annual symposium on. IEEE, pp 403–414\nVenuti LC, Albash T, Lidar DA, Zanardi P (2016) Adiabaticity in open quantum systems. Phys Rev A 93(3):032118\nWainwright MJ, Jordan MI et al (2008) Graphical models, exponential families, and variational inference. Foundations and Trends®; in Machine Learning 1(1–2):1–305\nWiebe N, Kapoor A, Svore KM (2014) Quantum deep learning. arXiv:1412.3489\nYu C-NJ, Joachims T (2009) Learning structural svms with latent variables. In: Proceedings of the 26th annual international conference on machine learning. ACM, pp 1169–1176\nYu CN (2011) Improved learning of structural support vector machines: training with latent variables and nonlinear kernels",{"EN":780},"",{"EN":782},"We introduce two quantum algorithms for solving structured prediction problems. We first show that a stochastic gradient descent that uses the quantum minimum finding algorithm and takes its probabilistic failure into account solves the structured prediction problem with a runtime that scales with the square root of the size of the label space, and in \n                \n                  \n                \n                $$\\tilde{O}\\left (1\u002F\\epsilon \\right )$$\n                \n               with respect to the precision, 𝜖, of the solution. Motivated by robust inference techniques in machine learning, we then introduce another quantum algorithm that solves a smooth approximation of the structured prediction problem with a similar quantum speedup in the size of the label space and a similar scaling in the precision parameter. In doing so, we analyze a variant of stochastic gradient descent for convex optimization in the presence of an additive error in the calculation of the gradients, and show that its convergence rate does not deteriorate if the additive errors are of the order \n                \n                  \n                \n                $$O(\\sqrt{\\epsilon})$$\n                \n              . This algorithm uses quantum Gibbs sampling at temperature Ω(𝜖) as a subroutine. Based on these theoretical observations, we propose a method for using quantum Gibbs samplers to combine feedforward neural networks with probabilistic graphical models for quantum machine learning. Our numerical results using Monte Carlo simulations on an image tagging task demonstrate the benefit of the approach.",{"EN":784},"Quantum algorithms for structured prediction",{"VOID":786},"10.1007\u002Fs42484-022-00078-w","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42484-022-00078-w",[789,822,844,856],{"id":790,"sortIndex":141,"researcher":20,"roles":791,"affiliations":792,"properties":819},"e87fafd9-6685-4263-980d-1130c5d2c389",[181],[793,801,809],{"id":20,"sortIndex":21,"affiliation":794,"properties":20},{"id":795,"createTime":796,"updateTime":796,"relativeEntities":797,"slug":20,"properties":798,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"bd07df20-646d-4db1-8997-54166f060cd3","2023-12-19T11:46:55.232+00:00",[],{"title":799},{"VI":800},"Institute for Quantum Computing, Waterloo, Canada",{"id":20,"sortIndex":21,"affiliation":802,"properties":20},{"id":803,"createTime":804,"updateTime":804,"relativeEntities":805,"slug":20,"properties":806,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"95ef704b-7228-4eca-8e96-dc507f7665d8","2024-01-10T03:00:32.049+00:00",[],{"title":807},{"VI":808},"1QB Information Technologies (1QBit), Vancouver, Canada",{"id":20,"sortIndex":21,"affiliation":810,"properties":20},{"id":811,"createTime":812,"updateTime":813,"relativeEntities":814,"slug":815,"properties":816,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"e48fce7d-ff78-4ab6-8499-bb1c68636fa6","2023-12-12T00:51:38.984+00:00","2025-02-02T08:59:35.586+00:00",[],"University-of-Waterloo-Waterloo-Canada",{"title":817},{"VI":818},"University of Waterloo, Waterloo, Canada",{"title":820},{"VI":821},"Pooya Ronagh",{"id":823,"sortIndex":138,"researcher":20,"roles":824,"affiliations":825,"properties":841},"83143643-db2c-41de-a09d-e247f0e2cd47",[181],[826,831],{"id":20,"sortIndex":21,"affiliation":827,"properties":20},{"id":803,"createTime":804,"updateTime":804,"relativeEntities":828,"slug":20,"properties":829,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":830},{"VI":808},{"id":20,"sortIndex":21,"affiliation":832,"properties":20},{"id":833,"createTime":834,"updateTime":835,"relativeEntities":836,"slug":837,"properties":838,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"caa12f99-ce0b-4431-81ec-d0697132dfdf","2023-12-23T15:30:21.798+00:00","2025-01-30T04:37:39.436+00:00",[],"University-of-British-Columbia-Vancouver-Canada",{"title":839},{"VI":840},"University of British Columbia, Vancouver, Canada",{"title":842},{"VI":843},"Michael P. Friedlander",{"id":845,"sortIndex":140,"researcher":20,"roles":846,"affiliations":847,"properties":853},"db21b4b9-85f0-4d9b-976c-fb3cd1a6622a",[181],[848],{"id":20,"sortIndex":21,"affiliation":849,"properties":20},{"id":803,"createTime":804,"updateTime":804,"relativeEntities":850,"slug":20,"properties":851,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":852},{"VI":808},{"title":854},{"VI":855},"Ehsan Iranmanesh",{"id":857,"sortIndex":21,"researcher":20,"roles":858,"affiliations":859,"properties":865},"e1c47253-e255-418e-bc73-886c5b2c1487",[181],[860],{"id":20,"sortIndex":21,"affiliation":861,"properties":20},{"id":803,"createTime":804,"updateTime":804,"relativeEntities":862,"slug":20,"properties":863,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":864},{"VI":808},{"title":866},{"VI":867},"Behrooz Sepehry",{"url":20,"publisher":869,"properties":20},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":870,"slug":10,"properties":871,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":875,"manageAffiliations":876,"indexDatabases":877,"url":20,"thumbnailPath":20,"statistic":892,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":872,"eissn":873,"title":874},{"VOID":13},{"VOID":15},{"EN":17},[],[],[878,885],{"id":89,"indexDatabase":879,"url":104,"indexYears":20,"academicFieldIds":884,"indexDatabaseRanking":20},{"id":91,"createTime":92,"updateTime":93,"relativeEntities":880,"label":881,"description":882,"key":100,"publicationTags":883,"standard":20},[],{"EN":96,"VI":96},{"VI":98,"EN":99},[102,103],[106,107],{"id":109,"indexDatabase":886,"url":122,"indexYears":123,"academicFieldIds":891,"indexDatabaseRanking":130},{"id":111,"createTime":112,"updateTime":113,"relativeEntities":887,"label":888,"description":889,"key":119,"publicationTags":890,"standard":20},[],{"EN":116,"VI":116},{"EN":116,"VI":118},[121],[125,126,127,128,129],{"impactFactor":21,"impactFactorByYear":893,"i10Index":135,"i10IndexLast5Year":135,"totalPublication":136,"totalPublicationByYear":894,"totalCitation":142,"totalCitationByYear":895,"totalCitationPerPublication":148,"totalCitationPerPublicationByYear":896,"hindexLast5Year":139,"hindex":139},{"2021":133,"2022":85,"2023":134},{"2019":138,"2020":138,"2021":139,"2022":135,"2023":140,"2024":141},{"2019":144,"2020":145,"2021":146,"2022":147,"2023":140},{"2019":150,"2020":151,"2021":152,"2022":153,"2023":140},"2022-09-01",2022,{"id":900,"createTime":901,"updateTime":902,"relativeEntities":903,"slug":904,"properties":905,"entityType":173,"verifyStatus":174,"verifyTime":902,"verifyNote":175,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":914,"fullTextUrl":20,"authors":915,"publicationType":301,"publisherRelationship":953,"citationCount":20,"citationInfo":20,"publishDate":987,"publishYear":988,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":338},"433e6783-c97a-4f82-8c6c-479abef54a27","2024-02-13T09:58:00.152+00:00","2024-12-20T22:39:17.171+00:00",[],"Pseudo-dimension-of-quantum-circuits",{"references":906,"abstract":908,"title":910,"doi":912},{"VOID":907},"Aaronson S (2007) The learnability of quantum states. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 463(2088):3089–3114. https:\u002F\u002Fdoi.org\u002F10.1098\u002Frspa.2007.0113\nAaronson S (2016) The complexity of quantum states and transformations: From quantum money to black holes. Electronic Colloquium on Computational Complexity (ECCC) 23:109\nAaronson S (2018) Shadow tomography of quantum states. http:\u002F\u002Fdl.acm.org\u002Fft_gateway.cfm?id=3188802&type=pdf\nAaronson S, Chen X, Hazan E, Kale S (2018) Online learning of quantum states. http:\u002F\u002Fdl.acm.org\u002Fft_gateway.cfm?id=3327572&type=pdf\nAlon N, Ben-David S, Cesa-Bianchi N, Haussler D (1997) Scale-sensitive dimensions, uniform convergence, and learnability. J ACM 44(4):615–631. https:\u002F\u002Fdoi.org\u002F10.1145\u002F263867.263927\nAnthony M, Bartlett PL (2000) Function learning from interpolation. Comb Probab Comput 9(3):213–225. https:\u002F\u002Fdoi.org\u002F10.1017\u002FS0963548300004247\nArunachalam S, de Wolf R (2017) Guest column: A survey of quantum learning theory. SIGACT News 48 , https:\u002F\u002Fpure.uva.nl\u002Fws\u002Ffiles\u002F25255496\u002Fp41_arunachalam.pdf\nBartlett PL, Long PM (1998) Prediction, learning, uniform convergence, and scale-sensitive dimensions. J Comput Sys Sci 56(2):174–190. https:\u002F\u002Fdoi.org\u002F10.1006\u002Fjcss.1997.1557\nBartlett PL, Mendelson S (2002) Rademacher and gaussian complexities: Risk bounds and structural results. J Mach Learn Res 3(Nov):463–482. http:\u002F\u002Fwww.jmlr.org\u002Fpapers\u002Fvolume3\u002Fbartlett02a\u002Fbartlett02a.pdf\nBlumer A, Ehrenfeucht A, Haussler D, Warmuth M K (1989) Learnability and the vapnik-chervonenkis dimension. J ACM 36(4):929–965. https:\u002F\u002Fdoi.org\u002F10.1145\u002F76359.76371\nCheng HC, Hsieh MH, Yeh PC (2016) The learnability of unknown quantum measurements. Quantum Information & Computation 16(7-8):615–656\nChung KM, Lin HH (2018) Sample efficient algorithms for learning quantum channels in pac model and the approximate state discrimination problem. arXiv:1810.10938\nGoldberg PW, Jerrum MR (1995) Bounding the vapnik-chervonenkis dimension of concept classes parameterized by real numbers. Mach Learn 18(2-3):131–148. https:\u002F\u002Fdoi.org\u002F10.1007\u002FBF00993408\nHanneke S (2016) The optimal sample complexity of pac learning. J Mach Learn Res 17(1):1319–1333. http:\u002F\u002Fdl.acm.org\u002Fft_gateway.cfm?id=2946683&type=pdf\nHeinosaari T, Ziman M (2013) The mathematical language of quantum theory: From uncertainty to entanglement. Cambridge University Press, Cambridge\nKarpinski M, Macintyre A (1997) Polynomial bounds for vc dimension of sigmoidal and general pfaffian neural networks. J Comput Sys Sci 54(1):169–176. https:\u002F\u002Fdoi.org\u002F10.1006\u002Fjcss.1997.1477\nKiani BT, Lloyd S, Maity R (2020) Learning unitaries by gradient descent. arXiv:2001.11897\nKoiran P (1996) VC dimension in circuit complexity. In: Cai J Y, Homer S (eds) Proceedings, Eleventh annual ieee conference on computational complexity. https:\u002F\u002Fdoi.org\u002F10.1109\u002FCCC.1996.507671. IEEE Computer Society Press, Los Alamitos, pp 81–85\nNielsen MA, Chuang IL (2010) Quantum computation and quantum information. Cambridge University Press, Cambridge and New York\nPollard D (1984) Convergence of stochastic processes. Springer Series in Statistics. Springer, New York\nRocchetto A (2017) Stabiliser states are efficiently pac-learnable. Quantum Information and Computation, 18\nRocchetto A, Aaronson S, Severini S, Carvacho G, Poderini D, Agresti I, Bentivegna M, Sciarrino F (2019) Experimental learning of quantum states. Science Advances 5(3), https:\u002F\u002Fdoi.org\u002F10.1126\u002Fsciadv.aau1946\nTorlai G, Mazzola G, Carrasquilla J, Troyer M, Melko R, Carleo G (2018) Neural-network quantum state tomography. Nat Phy 14(5):447–450 . https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41567-018-0048-5, https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41567-018-0048-5.pdf\nValiant LG (1984) A theory of the learnable. Commun ACM 27(11):1134–1142. https:\u002F\u002Fdoi.org\u002F10.1145\u002F1968.1972\nVapnik VN, Chervonenkis AY (1971) On the uniform convergence of relative frequencies of events to their probabilities. Theory of Probability & Its Applications 16(2):264–280. https:\u002F\u002Fdoi.org\u002F10.1137\u002F1116025\nWarren HE (1968) Lower bounds for approximation by nonlinear manifolds. Trans Am Math Soc 133(1):167. https:\u002F\u002Fdoi.org\u002F10.2307\u002F1994937",{"EN":909},"We characterize the expressive power of quantum circuits with the pseudo-dimension, a measure of complexity for probabilistic concept classes. We prove pseudo-dimension bounds on the output probability distributions of quantum circuits; the upper bounds are polynomial in circuit depth and number of gates. 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M, Garcia-Pintos D, Perdomo O, Leyton-Ortega V, Nam Y, Perdomo-Ortiz A (2019) A generative modeling approach for benchmarking and training shallow quantum circuits. npj Quantum Inf 5:45",{"doi":1103},"10.1038\u002Fs41534-019-0157-8",{"id":20,"text":1105,"url":20,"identifiers":1106},"Hamilton KE, Dumitrescu EF, Pooser RC (2018) Generative model benchmarks for superconducting qubits. arXiv:1811.09905",{},{"id":20,"text":1108,"url":20,"identifiers":1109},"Zhu D, Linke NM, Benedetti M, Landsman KA, Nguyen NH, Alderete CH, Perdomo-Ortiz A, Korda N, Garfoot A, Brecque C, Egan L, Perdomo O, Monroe C (2018) Training of quantum circuits on a hybrid quantum computer. arXiv:1812.08862",{},{"id":20,"text":1111,"url":20,"identifiers":1112},"Peruzzo A, McClean J, Shadbolt P, Yung M-H, Zhou X-Q, Love PJ, Aspuru-Guzik A, O’Brien JL (2014) A variational eigenvalue solver on a photonic quantum processor, vol 5. EP –",{"doi":1113},"10.1038\u002Fncomms5213",{"id":20,"text":1115,"url":20,"identifiers":1116},"McClean JR, Romero J, Babbush R, Aspuru-Guzik A (2016) The theory of variational hybrid quantum-classical algorithms. New J Phys 18:023023",{"doi":1117},"10.1088\u002F1367-2630\u002F18\u002F2\u002F023023",{"id":20,"text":1119,"url":20,"identifiers":1120},"Farhi SGE, Goldstone J (2014) A quantum approximate optimization algorithm. arXiv:1411.4028",{},{"id":20,"text":1122,"url":20,"identifiers":1123},"Higuchi A, Sudbery AW (2000) How entangled can two couples get? Phys Lett A 273:213–217",{"doi":1124},"10.1016\u002FS0375-9601(00)00480-1",{"id":20,"text":1126,"url":20,"identifiers":1127},"Alcazar J, Leyton-Ortega V, Perdomo-Ortiz A (2020) Classical versus quantum models in machine learning: insights from a finance application. 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J Lightwave Technol 36(20):4843–4855. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJLT.2018.2865109\nKiarashinejad Y, Abdollahramezani S, Zandehshahvar M, Hemmatyar O, Adibi A (2019) Deep learning reveals underlying physics of light–matter interactions in nanophotonic devices. Adv Theory Simul 2(9):1900088. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fadts.201900088\nKiarashinejad Y, Zandehshahvar M, Abdollahramezani S, Hemmatyar O, Pourabolghasem R, Adibi A (2020) Knowledge discovery in nanophotonics using geometric deep learning. Adv Intell Syst 2(2):1900132. https:\u002F\u002Fdoi.org\u002F10.1002\u002Faisy.201900132\nKilloran N, Bromley TR, Arrazola JM, Schuld M, Quesada N, Lloyd S (2019) Continuous-variable quantum neural networks. Phys Rev Res 1(3). https:\u002F\u002Fdoi.org\u002F10.1103\u002Fphysrevresearch.1.033063\nKingma DP, Ba J (2017) Adam: a method for stochastic optimization. Mach Learn, 1–15. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1412.6980v9\nLloyd S, Mohseni M, Rebentrost P (2014) Quantum principal component analysis. Nat Phys 10(9):631–633. https:\u002F\u002Fdoi.org\u002F10.1038\u002Fnphys3029\nLundervold AS, Lundervold A (2019) An overview of deep learning in medical imaging focusing on mri. Z Med Phys 29(2):102–127. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.zemedi.2018.11.002. Special Issue: Deep Learning in Medical Physics\nMa W, Cheng F, Liu Y (2018) Deep-learning-enabled on-demand design of chiral metamaterials. ACS Nano 12(6):6326–6334. https:\u002F\u002Fdoi.org\u002F10.1021\u002Facsnano.8b03569\nMari A, TR TB, Izaac J, Schuld M, Killoran N (2020) Transfer learning in hybrid classical-quantum neural networks. Quantum 4(340):1–13. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.1912.08278\nMcClean JR, Romero J, Babbush R, Aspuru-Guzik A (2016) The theory of variational hybrid quantum-classical algorithms. New J Phys 18(2):023023. https:\u002F\u002Fdoi.org\u002F10.1088\u002F1367-2630\u002F18\u002F2\u002F023023\nMitarai K, Negoro M, Kitagawa M, Fujii K (2018) Quantum circuit learning. Phys Rev A 98(3). https:\u002F\u002Fdoi.org\u002F10.1103\u002Fphysreva.98.032309\nMohanraj J, Velmurugan V, Sathiyan S, Sivabalan S (2018) All fiber-optic ultra-sensitive temperature sensor using few-layer mos2 coated d-shaped fiber. Opt Commun 406:139–144. https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.optcom.2017.06.011\nPondick JV, Woods JM, Xing J, Zhou Y, Cha JJ (2018) Stepwise sulfurization from moo3 to mos2 via chemical vapor deposition. ACS Appl Nano Mater 1(10):5655–5661. https:\u002F\u002Fdoi.org\u002F10.1021\u002Facsanm.8b01266\nPreskill J (2018) Quantum computing in the NISQ era and beyond. Quantum 2:79. https:\u002F\u002Fdoi.org\u002F10.22331\u002Fq-2018-08-06-79\nQuoc CN, Ho LB, Tran LN, Nguyen HQ (2022) Qsun: an open-source platform towards practical quantum machine learning applications. Mach Learn: Sci Technol, 1–18. https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2107.10541\nSchuld M, Sinayskiy I, Petruccione F (2016) Prediction by linear regression on a quantum computer. Phys Rev A 94:022342\nSchuld M, Bocharov A, Svore KM, Wiebe N (2020) Circuit-centric quantum classifiers. Phys Rev A 101(3). https:\u002F\u002Fdoi.org\u002F10.1103\u002Fphysreva.101.032308\nSchuld M, Killoran N (2019) Quantum machine learning in feature hilbert spaces. Phys Rev Lett 122(4). https:\u002F\u002Fdoi.org\u002F10.1103\u002Fphysrevlett.122.040504\nSchuld M, Sweke R, Meyer JJ (2021) Effect of data encoding on the expressive power of variational quantum-machine-learning models. Phys Rev A 103(3). https:\u002F\u002Fdoi.org\u002F10.1103\u002Fphysreva.103.032430\nSilva Ferreira A, Malheiros-Silveira GN, Hernández-Figueroa HE (2018) Computing optical properties of photonic crystals by using multilayer perceptron and extreme learning machine. J Lightwave Technol 36(18):4066–4073. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJLT.2018.2856364\nSim S, Johnson PD, Aspuru-Guzik A (2019) Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms. Adv Quantum Technol 2(12):1900070. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fqute.201900070\nSimões RDM, Huber P, Meier N, Smailov N, Füchslin RM, Stockinger K (2023) Experimental evaluation of quantum machine learning algorithms. IEEE Access 11:6197–6208. https:\u002F\u002Fdoi.org\u002F10.1109\u002FACCESS.2023.3236409\nSridevi S, Kanimozhi T, Ayyanar N, Chugh S, Valliammai M, Mohanraj J (2022) Deep learning based data augmentation and behavior prediction of photonic crystal fiber temperature sensor. IEEE Sens J 22(7):6832–6839. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJSEN.2022.3150240\nWiebe N, Braun D, Lloyd S (2012) Quantum algorithm for data fitting. Phys Rev Lett 109(5). https:\u002F\u002Fdoi.org\u002F10.1103\u002Fphysrevlett.109.050505\nYu F, Liu Q, Gan X, Hu M, Zhang T, Li C, Kang F, Terrones M, Lv R (2017) Ultrasensitive pressure detection of few-layer mos2. Adv Mater 29(4):1603266. https:\u002F\u002Fdoi.org\u002F10.1002\u002Fadma.201603266\nZelaci A, Yasli A, Kalyoncu C, Ademgil H (2021) Generative adversarial neural networks model of photonic crystal fiber based surface plasmon resonance sensor. J Lightwave Technol 39(5):1515–1522. https:\u002F\u002Fdoi.org\u002F10.1109\u002FJLT.2020.3035580\nZhao R, Wang S (2021) A review of quantum neural networks: methods, models, dilemma",{"EN":780},{"EN":1189},"In this research work, a quantum regression model (QRM) is proposed by combining an autoencoder and a dressed quantum circuit (DQC) to predict the behavior of fiber optic temperature sensors. As the experimental data gathered during our observations was limited to effectively train the proposed QRM model, we employed an autoencoder to expand the dataset. We examined the regression performance of the QRM by running multiple simulations by varying the quantum hyperparameters such as quantum depth \n                \n                  \n                \n                $$\\varvec{Q_{depth}}$$\n                \n              , number of shots \n                \n                  \n                \n                $$\\varvec{n_{shots}}$$\n                \n              , and the number of qubits \n                \n                  \n                \n                $$\\varvec{n_{qubits}}$$\n                \n               of the quantum node. Moreover, the regression performance with the unknown data exhibits high R-squared \n                \n                  \n                \n                $$\\varvec{(r^2)}$$\n                \n               as 0.965, high explained variance \n                \n                  \n                \n                $$\\varvec{(ExpVar)}$$\n                \n               as 0.969, and small maximum error \n                \n                  \n                \n                $$\\varvec{(MaxErr)}$$\n                \n               as 0.212 for 4 \n                \n                  \n                \n                $$\\varvec{Q_{depth}}$$\n                \n              , 1500 \n                \n                  \n                \n                $$\\varvec{n_{shots}}$$\n                \n               and 4 \n                \n                  \n                \n                $$\\varvec{n_{qubits}}$$\n                \n              . Additionally, we proved the superiority performance of the proposed QRM for predicting relative power as it is compared with four conventional machine learning regressors, namely artificial neural network (ANN) regressor, support vector regressor (SVR), decision tree (DT) regressor, and random forest (RF) regressor.",{"EN":1191},"Behavior prediction of fiber optic temperature sensor based on hybrid classical quantum regression model",{"VOID":1193},"10.1007\u002Fs42484-024-00150-7","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42484-024-00150-7",[1196,1212,1224,1236,1251,1263],{"id":1197,"sortIndex":222,"researcher":20,"roles":1198,"affiliations":1199,"properties":1209},"6d2f7f76-2829-46df-8fcf-779164418a01",[181],[1200],{"id":20,"sortIndex":21,"affiliation":1201,"properties":20},{"id":1202,"createTime":1203,"updateTime":1203,"relativeEntities":1204,"slug":1205,"properties":1206,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"e1b27d2d-00f5-47ef-aa1e-cf6263073554","2024-04-06T19:46:53.810+00:00",[],"Department-of-ECE-Veltech-Rangarajan-Dr-Sagunthala-R-and-D-Institute-of-Science-and-Technology-Chennai-India",{"title":1207},{"VI":1208},"Department of ECE, Veltech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology, Chennai, India",{"title":1210},{"VI":1211},"N. Vinodhkumar",{"id":1213,"sortIndex":141,"researcher":20,"roles":1214,"affiliations":1215,"properties":1221},"8788f2a8-4bc1-4f25-b260-0e2587c4712f",[181],[1216],{"id":20,"sortIndex":21,"affiliation":1217,"properties":20},{"id":1202,"createTime":1203,"updateTime":1203,"relativeEntities":1218,"slug":1205,"properties":1219,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1220},{"VI":1208},{"title":1222},{"VI":1223},"J. Mohanraj",{"id":1225,"sortIndex":21,"researcher":20,"roles":1226,"affiliations":1227,"properties":1233},"2e675249-ec43-47a4-a93a-94c5e9b36e74",[181],[1228],{"id":20,"sortIndex":21,"affiliation":1229,"properties":20},{"id":1202,"createTime":1203,"updateTime":1203,"relativeEntities":1230,"slug":1205,"properties":1231,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1232},{"VI":1208},{"title":1234},{"VI":1235},"T. Kanimozhi",{"id":1237,"sortIndex":140,"researcher":20,"roles":1238,"affiliations":1239,"properties":1248},"6754ef8c-7aaa-4314-ab52-d1d5d8746adc",[181],[1240],{"id":20,"sortIndex":21,"affiliation":1241,"properties":20},{"id":1242,"createTime":1243,"updateTime":1243,"relativeEntities":1244,"slug":20,"properties":1245,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"27d1ea59-7a65-41cf-98c6-0c4511906f65","2024-01-15T10:24:29.074+00:00",[],{"title":1246},{"VI":1247},"School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India",{"title":1249},{"VI":1250},"S. Sridevi",{"id":1252,"sortIndex":138,"researcher":20,"roles":1253,"affiliations":1254,"properties":1260},"f380a05f-0951-4215-a591-e624a11ce14a",[181],[1255],{"id":20,"sortIndex":21,"affiliation":1256,"properties":20},{"id":1202,"createTime":1203,"updateTime":1203,"relativeEntities":1257,"slug":1205,"properties":1258,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},[],{"title":1259},{"VI":1208},{"title":1261},{"VI":1262},"M. Valliammai",{"id":1264,"sortIndex":135,"researcher":20,"roles":1265,"affiliations":1266,"properties":1276},"d91ca1da-d96f-44ac-928b-358330e82d19",[181],[1267],{"id":20,"sortIndex":21,"affiliation":1268,"properties":20},{"id":1269,"createTime":1270,"updateTime":1270,"relativeEntities":1271,"slug":1272,"properties":1273,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"8c8f4f85-df41-4a6b-9ec5-7afaa637e2fa","2024-04-06T19:46:53.859+00:00",[],"Department-of-Computer-Science-Faculty-of-Engineering-and-Technology-Mettu-University-Mettu-Ethiopia",{"title":1274},{"VI":1275},"Department of Computer Science, Faculty of Engineering and Technology, Mettu University, Mettu, Ethiopia",{"title":1277},{"VI":1278},"Amirthalingam Sathasivam",{"url":20,"publisher":1280,"properties":20},{"id":6,"createTime":7,"updateTime":8,"relativeEntities":1281,"slug":10,"properties":1282,"entityType":18,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"subjectFields":1286,"manageAffiliations":1287,"indexDatabases":1288,"url":20,"thumbnailPath":20,"statistic":1303,"gsStatistic":20,"type":20,"analyzePriority":20},[],{"issn":1283,"eissn":1284,"title":1285},{"VOID":13},{"VOID":15},{"EN":17},[],[],[1289,1296],{"id":89,"indexDatabase":1290,"url":104,"indexYears":20,"academicFieldIds":1295,"indexDatabaseRanking":20},{"id":91,"createTime":92,"updateTime":93,"relativeEntities":1291,"label":1292,"description":1293,"key":100,"publicationTags":1294,"standard":20},[],{"EN":96,"VI":96},{"VI":98,"EN":99},[102,103],[106,107],{"id":109,"indexDatabase":1297,"url":122,"indexYears":123,"academicFieldIds":1302,"indexDatabaseRanking":130},{"id":111,"createTime":112,"updateTime":113,"relativeEntities":1298,"label":1299,"description":1300,"key":119,"publicationTags":1301,"standard":20},[],{"EN":116,"VI":116},{"EN":116,"VI":118},[121],[125,126,127,128,129],{"impactFactor":21,"impactFactorByYear":1304,"i10Index":135,"i10IndexLast5Year":135,"totalPublication":136,"totalPublicationByYear":1305,"totalCitation":142,"totalCitationByYear":1306,"totalCitationPerPublication":148,"totalCitationPerPublicationByYear":1307,"hindexLast5Year":139,"hindex":139},{"2021":133,"2022":85,"2023":134},{"2019":138,"2020":138,"2021":139,"2022":135,"2023":140,"2024":141},{"2019":144,"2020":145,"2021":146,"2022":147,"2023":140},{"2019":150,"2020":151,"2021":152,"2022":153,"2023":140},"2024-04-04",2024,{"id":1311,"createTime":1312,"updateTime":1313,"relativeEntities":1314,"slug":1315,"properties":1316,"entityType":173,"verifyStatus":174,"verifyTime":1313,"verifyNote":175,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21,"primaryUrl":1325,"fullTextUrl":20,"authors":1326,"publicationType":301,"publisherRelationship":1429,"citationCount":20,"citationInfo":20,"publishDate":1463,"publishYear":1464,"citationAnalyzeStatus":19,"lastCitationAnalyze":20,"indexDatabases":20,"openAccess":20,"references":20,"isForceReanalyzing":338},"957fc1c1-5f52-47db-ae12-cd3a62616689","2023-12-24T17:56:44.107+00:00","2025-02-14T22:15:24.320+00:00",[],"An-evolutionary-strategy-for-finding-effective-quantum-2-body-Hamiltonians-of-p-body-interacting-systems",{"references":1317,"abstract":1319,"title":1321,"doi":1323},{"VOID":1318},"Albash T, Lidar DA (2018) ., vol 90. https:\u002F\u002Fdoi.org\u002Fdoi\u002F10.1103\u002FRevModPhys.90.015002\nBapst V, Semerjian G (2012) . Journal of Statistical Mechanics: Theory and Experiment 2012(06):P06007. http:\u002F\u002Fstacks.iop.org\u002F1742-5468\u002F2012\u002Fi=06\u002Fa=P06007\nBiamonte JD (2008) . Phys Rev A 77:052331. https:\u002F\u002Fdoi.org\u002Fdoi\u002F10.1103\u002FPhysRevA.77.052331\nBrell CG, Flammia ST, Bartlett SD, Doherty AC (2011) . New J Phys 13(5):053039. https:\u002F\u002Fdoi.org\u002F10.1088\u002F1367-2630\u002F13\u002F5\u002F053039\nChoi V (2008) . Quantum Inf Process 7(5):193. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11128-008-0082-9\nChoi V (2011) . Quantum Inf Process 10(3):343. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11128-010-0200-3\nCook SA (1971) .. In: Proceedings of the 3rd annual ACM symposium on theory of computing. STOC ’71. ACM, New York, pp 151–158, https:\u002F\u002Fdoi.org\u002F10.1145\u002F800157.805047, (to appear in print)\ndel Campo A, Kim K (2019) . New J Phys 21(5):050201. https:\u002F\u002Fdoi.org\u002F10.1088\u002F1367-2630\u002Fab1437\nDerrida B (1981) . 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Nature 473:194\nHauke P, Katzgraber H G, Lechner W, Nishimori H, Oliver WD (2019) . arXiv:1903.06559\nHerrera F, Lozano M, Sánchez AM (2003) . International Journal of Intelligent Systems 18:309\nLeib M, Zoller P, Lechner W (2016) . Quantum Sci Technol 1(1):015008. https:\u002F\u002Fdoi.org\u002F10.1088\u002F2058-9565\u002F1\u002F1\u002F015008\nLucas A (2014) . Frontiers in Physics 2:5. https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffphy.2014.00005. https:\u002F\u002Fwww.frontiersin.org\u002Farticle\u002F10.3389\u002Ffphy.2014.00005\nMoscato P (1989) Caltech concurrent computation program. C3P Report 826:1989\nO’Driscoll L, Nichols R, Knott PA (2019) Quantum Machine Intelligence. https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs42484-019-00003-8\nOhkuwa M, Nishimori H, Lidar DA (2018) . Phys Rev A 98:022314. https:\u002F\u002Fdoi.org\u002Fdoi\u002F10.1103\u002FPhysRevA.98.022314\nPassarelli G, De Filippis G, Cataudella V, Lucignano P (2018) . Phys Rev A 97:022319. https:\u002F\u002Fdoi.org\u002Fdoi\u002F10.1103\u002FPhysRevA.97.022319\nPassarelli G, Cataudella V, Lucignano P (2019) Improving quantum annealing of the ferromagnetic p-spin model through pausing. Phys Rev B 100(2):024302. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPhysRevB.100.024302. https:\u002F\u002Fdoi.org\u002Fdoi\u002F10.1103\u002FPhysRevB.100.024302\nPassarelli G, De Filippis G, Cataudella V, Lucignano P. (2019) . arXiv:1901.07787\nRezakhani A T, Kuo W J, Hamma A, Lidar D A, Zanardi P (2009) . Phys Rev Lett 103:080502. https:\u002F\u002Fdoi.org\u002F10.1103\u002FPhysRevLett.103.080502\nSeki Y, Nishimori H (2012) . Phys Rev E 85:051112. https:\u002F\u002Fdoi.org\u002Fdoi\u002F10.1103\u002FPhysRevE.85.051112\nSeoane B, Nishimori H (2012) . Journal of Physics A: Mathematical and Theoretical 45(43):435301. http:\u002F\u002Fstacks.iop.org\u002F1751-8121\u002F45\u002Fi=43\u002Fa=435301\nSusa Y, Yamashiro Y, Yamamoto M, Hen I, Lidar DA, Nishimori H (2018) . Phys Rev A 98:042326. https:\u002F\u002Fdoi.org\u002Fdoi\u002F10.1103\u002FPhysRevA.98.042326\nTanahashi K, Takayanagi S, Motohashi T, Tanaka S (2019) . Journal of the Physical Society of Japan 88(6):061010. https:\u002F\u002Fdoi.org\u002F10.7566\u002FJPSJ.88.061010\nYao X (1993) . Microprocessing and Microprogramming 38(1):707. https:\u002F\u002Fdoi.org\u002F10.1016\u002F0165-6074(93)90215-7. http:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002F0165607493902157. Proceedings Euromicro 93 Open System Design: Hardware, Software and Applications",{"EN":1320},"Embedding p-body interacting models onto the 2-body networks implemented on commercial quantum annealers is a relevant issue. For highly interacting models, requiring a number of ancilla qubits, that can be sizable and make unfeasible (if not impossible) to simulate such systems. In this manuscript, we propose an alternative to minor embedding, developing a new approximate procedure based on genetic algorithms, allowing to decouple the p-body in terms of 2-body interactions. A set of preliminary numerical experiments demonstrates the feasibility of our approach for the ferromagnetic p-spin model and paves the way towards the application of evolutionary strategies to more complex quantum models.",{"EN":1322},"An evolutionary strategy for finding effective quantum 2-body Hamiltonians of p-body interacting systems",{"VOID":1324},"10.1007\u002Fs42484-019-00011-8","https:\u002F\u002Flink.springer.com\u002Farticle\u002F10.1007\u002Fs42484-019-00011-8",[1327,1352,1381,1393,1405,1417],{"id":1328,"sortIndex":222,"researcher":20,"roles":1329,"affiliations":1330,"properties":1349},"572aa25e-4aa6-4dea-9126-a186673df2a7",[181],[1331,1341],{"id":1332,"sortIndex":140,"affiliation":1333,"properties":1340},"0fc75b1f-341f-4bbb-84c0-9f5eb6ab9789",{"id":1334,"createTime":1335,"updateTime":1335,"relativeEntities":1336,"slug":20,"properties":1337,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"3fdc7ed6-b00a-4063-87e1-e85d67c36346","2023-12-29T12:58:58.320+00:00",[],{"title":1338},{"VI":1339},"CNR-SPIN, Naples, Italy",{},{"id":20,"sortIndex":21,"affiliation":1342,"properties":20},{"id":1343,"createTime":1344,"updateTime":1344,"relativeEntities":1345,"slug":20,"properties":1346,"entityType":73,"verifyStatus":19,"verifyTime":20,"verifyNote":20,"syncStatus":19,"languages":20,"translateLanguages":20,"viewCount":21},"e2f5512f-3859-4bcc-8354-09da1c347435","2023-12-24T17:56:44.325+00:00",[],{"title":1347},{"VI":1348},"Dipartimento di Fisica “E. 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OSDI 16:265–283\nAdaQuantum is available and free to use on GitHub. https:\u002F\u002Fgithub.com\u002Fpaulk444\u002FAdaQuantum, (2019)\nArrazola JM, Bromley TR, Izaac J, Myers CR, Brádler K, Killoran N (2019) Machine learning method for state preparation and gate synthesis on photonic quantum computers. Quantum Science and Technology 4:024004\nBarnett SM, Radmore PM (2002) Methods in Theoretical Quantum Optics, vol 15. Oxford University Press, Oxford\nBartley TJ, Donati G, Spring JB, Jin X-M, Barbieri M, Datta A, Smith BJ, Walmsley IA (2012) Multiphoton state engineering by heralded interference between single photons and coherent states. Phys Rev A 86(4):043820\nBiamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S (2017) Quantum machine learning. Nature 549(7671):195\nClaudon J, Bleuse J, Malik NS, Bazin M, Jaffrennou P, Gregersen N, Sauvan C, Lalanne P, Gérard J (2010) A highly efficient single-photon source based on a quantum dot in a photonic nanowire. Nature Photon 4(3):174–177\nDeep K, Thakur M (2007) A new mutation operator for real coded genetic algorithms. Appl Math Comput 193(1):211–230\nDunjko V, Briegel HJ (2017) Machine learning and artificial intelligence in the quantum domain. arXiv:1709.02779\nEtesse J, Bouillard M, Kanseri B, Tualle-Brouri R (2015) Experimental generation of squeezed cat states with an operation allowing iterative growth. Phys Rev Lett 114(19):193602\nGerrits T, Glancy S, Clement TS, Calkins B, Lita AE, Miller AJ, Migdall AL, Nam SW, Mirin RP, Knill E (2010) Generation of optical coherent-state superpositions by number-resolved photon subtraction from the squeezed vacuum. Phys Rev A 82(3):031802\nGhose S, Sanders BC (2007) Non-gaussian ancilla states for continuous variable quantum computation via gaussian maps. J Mod Opt 54(6):855–869\nGottesman D, Kitaev A, Preskill J (2001) Encoding a qubit in an oscillator. Phys Rev A 64(1):012310\nGu M, Weedbrook C, Menicucci NC, Ralph TC, van Loock P (2009) Quantum computing with continuous-variable clusters. Phys Rev A 79(6):062318\nHall MJW, Wiseman HM (2012) Heisenberg-style bounds for arbitrary estimates of shift parameters including prior information. New J Phys 14(3):033040\nHall MJW, Berry DW, Zwierz M, Wiseman HM (2012) Universality of the Heisenberg limit for estimates of random phase shifts. Phys Rev A 85(4):041802\nHuang K (2015) Optical Hybrid Architectures for Quantum Information Processing. PhD thesis l’École Normale supérieure de Paris and East China Normal University.\nHuang K, Le Jeannic H, Ruaudel J, Verma VB, Shaw MD, Marsili F, Nam SW, Wu E, Zeng H, Jeong Y-C et al (2015) Optical synthesis of large-amplitude squeezed coherent-state superpositions with minimal resources. Phys Rev Lett 115(2):023602\nHumphreys PC, Metcalf BJ, Gerrits T, Hiemstra T, Lita AE, Nunn J, Nam SW, Datta A, Kolthammer WS, Walmsley IA (2015) Tomography of photon-number resolving continuous-output detectors. arXiv:1502.07649\nJiang L-y, Guo Q, Xu X-x, Cai M, Yuan W, Duan Z-l (2016) Dynamics and nonclassical properties of an opto-mechanical system prepared in four-headed cat state and number state. Opt Commun 369:179–188\nJin Y (2011) Surrogate-assisted evolutionary computation: Recent advances and future challenges. Swarm Evol Comput 1(2):61–70\nKingma DP, Ba J (2014) Adam: A Method for Stochastic Optimization. arXiv:1412.6980 [cs.LG], 12\nKnott P, Proctor T, Hayes A, Cooling J, Dunningham J (2016) Practical quantum metrology with large precision gains in the low-photon-number regime. Phys. Rev. A 93(3):033859\nKnott PA (2016) A search algorithm for quantum state engineering and metrology. New J Phys 18(7):073033\nKok P, Lovett BW (2010) Introduction to Optical Quantum Information Processing. Cambridge University Press, Cambridge\nKrenn M, Malik M, Fickler R, Lapkiewicz R, Zeilinger A (2016) Automated search for new quantum experiments. Phys Rev Lett 116(9):090405\nLee S-Y, Lee C-W, Lee J, Nha H (2015) Quantum phase estimation using a class of entangled states: NOON-type states. arXiv:1505.06000\nLee S-Y, Lee C-W, Nha H, Kaszlikowski D (2015) Quantum phase estimation using a multi-headed cat state. JOSA B 32(6):1186–1192\nMehmet M, Ast S, Eberle T, Steinlechner S, Vahlbruch H, Schnabel R (2011) Squeezed light at 1550 nm with a quantum noise reduction of 12.3 db. Opt Express 19(25):25763–25772\nMelnikov AA, Nautrup HP, Krenn M, Dunjko V, Tiersch M, Zeilinger A, Briegel HJ (2018) Active learning machine learns to create new quantum experiments. In: Proceedings of the National Academy of Sciences, p 201714936\nMorin O, D’Auria V, Fabre C, Laurat J (2012) High-fidelity single-photon source based on a type II optical parametric oscillator. Opt Lett 37(17):3738–3740\nMüller K, Rundquist A, Fischer KA, Sarmiento T, Lagoudakis KG, Kelaita YA, Sanchez Muñoz C, del Valle E, Laussy FP, Vučković J (2015) Coherent generation of nonclassical light on chip via detuned photon blockade. Phys Rev Lett 114(23):233601\nNichols R, Mineh L, Rubio J, Matthews JCF, Knott PA (2018) Designing quantum experiments with a genetic algorithm. arXiv:1812.01032\nNielsen MA, Chuang IL (2010) Quantum Computation and Quantum Information. Cambridge University Press, Cambridge\nOur DNN and quantum-state generator on GitHub. https:\u002F\u002Fgithub.com\u002Flewis-od\u002FQuantum-Optics, (2018)\nOurjoumtsev A, Tualle-brouri R, Grangier P (2006) Quantum homodyne tomography of a two-photon Fock state. Phys Rev Lett 96(21):213601\nOurjoumtsev A, Jeong H, Tualle-Brouri R, Grangier P (2007) Generation of optical ’schrödinger cats’ from photon number states. Nature 448(7155):784\nParis MGA (1996) Displacement operator by beam splitter. Phys Lett A 217(2):78–80\nRivas A, Luis A (2012) Sub-heisenberg estimation of non-random phase shifts. New J Phys 14(9):093052\nRubio J, Knott P, Dunningham J (2018) Non-asymptotic analysis of quantum metrology protocols beyond the Cramér–rao bound. Journal of Physics Communications 2(1):015027\nSabapathy KK, Qi H, Izaac J, Weedbrook C (2018) Near-deterministic production of universal quantum photonic gates enhanced by machine learning. arXiv:1809.04680\nSabapathy KK, Weedbrook C (2018) On states as resource units for universal quantum computation with photonic architectures. Phys Rev A 97(6):062315\nSchrödinger E (1935) Die gegenwärtige situation in der quantenmechanik. Naturwissenschaften 23(49):823–828\nSchuld M, Sinayskiy I, Petruccione F (2015) An introduction to quantum machine learning. Contemp Phys 56(2):172–185\nTakagi R, Zhuang Q (2018) Convex resource theory of non-gaussianity. Phys Rev A 97:062337\nT M Inc., Global optimization toolbox: user’s guide (r2018a). https:\u002F\u002Fuk.mathworks.com\u002Fhelp\u002Fgads\u002Findex.htmlhttps:\u002F\u002Fuk.mathworks.com\u002Fhelp\u002Fgads\u002Findex.html. Last accessed 2018-07-17",{"EN":1475},"We introduce a hybrid machine learning algorithm for designing quantum optics experiments to produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schrödinger cat states and cubic phase states, all to a fidelity of over 96%. Here, we specifically focus on designing realistic experiments, and hence all of the algorithm’s designs only contain experimental elements that are available with current technology. 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