{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:16:01Z","timestamp":1774314961088,"version":"3.50.1"},"reference-count":135,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T00:00:00Z","timestamp":1633651200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2021,10,8]],"date-time":"2021-10-08T00:00:00Z","timestamp":1633651200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["11825404"],"award-info":[{"award-number":["11825404"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["XDB28000000"],"award-info":[{"award-number":["XDB28000000"]}]}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2021,12,1]]},"DOI":"10.1088\/2632-2153\/ac28dd","type":"journal-article","created":{"date-parts":[[2021,9,21]],"date-time":"2021-09-21T22:24:37Z","timestamp":1632263077000},"page":"045027","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":70,"title":["Neural predictor based quantum architecture search"],"prefix":"10.1088","volume":"2","author":[{"given":"Shi-Xin","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6242-4218","authenticated-orcid":false,"given":"Chang-Yu","family":"Hsieh","sequence":"additional","affiliation":[]},{"given":"Shengyu","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hong","family":"Yao","sequence":"additional","affiliation":[]}],"member":"266","published-online":{"date-parts":[[2021,10,8]]},"reference":[{"key":"mlstac28ddbib1","article-title":"Variational quantum algorithms","author":"Cerezo","year":"2020"},{"key":"mlstac28ddbib2","article-title":"Noisy intermediate-scale quantum (NISQ) algorithms","author":"Bharti","year":"2021"},{"key":"mlstac28ddbib3","doi-asserted-by":"publisher","first-page":"79","DOI":"10.22331\/q-2018-08-06-79","article-title":"Quantum computing in the NISQ era and beyond","volume":"2","author":"Preskill","year":"2018","journal-title":"Quantum"},{"key":"mlstac28ddbib4","doi-asserted-by":"publisher","first-page":"4213","DOI":"10.1038\/ncomms5213","article-title":"A variational eigenvalue solver on a photonic quantum processor","volume":"5","author":"Peruzzo","year":"2014","journal-title":"Nat. Commun."},{"key":"mlstac28ddbib5","article-title":"Scalable quantum simulation of molecular energies","volume":"6","author":"O\u2019Malley","year":"2016","journal-title":"Phys. Rev. X"},{"key":"mlstac28ddbib6","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/18\/2\/023023","article-title":"The theory of variational hybrid quantum-classical algorithms","volume":"18","author":"McClean","year":"2016","journal-title":"New J. Phys."},{"key":"mlstac28ddbib7","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevResearch.1.023025","article-title":"Variational quantum eigensolver with fewer qubits","volume":"1","author":"Liu","year":"2019","journal-title":"Phys. Rev. Res."},{"key":"mlstac28ddbib8","doi-asserted-by":"publisher","DOI":"10.1103\/RevModPhys.92.015003","article-title":"Quantum computational chemistry","volume":"92","author":"McArdle","year":"2020","journal-title":"Rev. Mod. Phys."},{"key":"mlstac28ddbib9","doi-asserted-by":"publisher","first-page":"3007","DOI":"10.1038\/s41467-019-10988-2","article-title":"An adaptive variational algorithm for exact molecular simulations on a quantum computer","volume":"10","author":"Grimsley","year":"2019","journal-title":"Nat. Commun."},{"key":"mlstac28ddbib10","article-title":"A quantum approximate optimization algorithm","author":"Farhi","year":"2014"},{"key":"mlstac28ddbib11","doi-asserted-by":"publisher","first-page":"34","DOI":"10.3390\/a12020034","article-title":"From the quantum approximate optimization algorithm to a quantum alternating operator Ansatz","volume":"12","author":"Hadfield","year":"2019","journal-title":"Algorithms"},{"key":"mlstac28ddbib12","article-title":"Quantum approximate optimization algorithm: performance, mechanism and implementation on near-term devices","volume":"10","author":"Zhou","year":"2020","journal-title":"Phys. Rev. X"},{"key":"mlstac28ddbib13","article-title":"Quantum approximate optimization of non-planar graph problems on a planar superconducting processor","author":"Arute","year":"2020"},{"key":"mlstac28ddbib14","article-title":"Low depth mechanisms for quantum optimization","author":"McClean","year":"2020"},{"key":"mlstac28ddbib15","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/nature23474","article-title":"Quantum machine learning","volume":"549","author":"Biamonte","year":"2017","journal-title":"Nature"},{"key":"mlstac28ddbib16","article-title":"Classification with quantum neural networks on near term processors","author":"Farhi","year":"2018"},{"key":"mlstac28ddbib17","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.98.032309","article-title":"Quantum circuit learning","volume":"98","author":"Mitarai","year":"2018","journal-title":"Phys. Rev. A"},{"key":"mlstac28ddbib18","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.98.062324","article-title":"Differentiable learning of quantum circuit Born machines","volume":"98","author":"Liu","year":"2018","journal-title":"Phys. Rev. A"},{"key":"mlstac28ddbib19","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.121.040502","article-title":"Quantum generative adversarial learning","volume":"121","author":"Lloyd","year":"2018","journal-title":"Phys. Rev. Lett."},{"key":"mlstac28ddbib20","article-title":"Quantum Hamiltonian-based models and the variational quantum thermalizer algorithm","author":"Verdon","year":"2019"},{"key":"mlstac28ddbib21","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1038\/s41586-019-0980-2","article-title":"Supervised learning with quantum-enhanced feature spaces","volume":"567","author":"Havl\u00ed\u010dek","year":"2019","journal-title":"Nature"},{"key":"mlstac28ddbib22","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.122.040504","article-title":"Quantum machine learning in feature Hilbert spaces","volume":"122","author":"Schuld","year":"2019","journal-title":"Phys. Rev. Lett."},{"key":"mlstac28ddbib23","doi-asserted-by":"publisher","DOI":"10.1088\/2058-9565\/ab4eb5","article-title":"Parameterized quantum circuits as machine learning models","volume":"4","author":"Benedetti","year":"2019","journal-title":"Quantum Sci. Technol."},{"key":"mlstac28ddbib24","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1038\/s41534-019-0157-8","article-title":"A generative modeling approach for benchmarking and training shallow quantum circuits","volume":"5","author":"Benedetti","year":"2019","journal-title":"npj Quantum Inf."},{"key":"mlstac28ddbib25","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1038\/s41567-019-0648-8","article-title":"Quantum convolutional neural networks","volume":"15","author":"Cong","year":"2019","journal-title":"Nat. Phys."},{"key":"mlstac28ddbib26","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.101.032308","article-title":"Circuit-centric quantum classifiers","volume":"101","author":"Schuld","year":"2020","journal-title":"Phys. Rev. A"},{"key":"mlstac28ddbib27","article-title":"Power of data in quantum machine learning","author":"Huang","year":"2020"},{"key":"mlstac28ddbib28","doi-asserted-by":"publisher","first-page":"242","DOI":"10.1038\/nature23879","article-title":"Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets","volume":"549","author":"Kandala","year":"2017","journal-title":"Nature"},{"key":"mlstac28ddbib29","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.92.042303","article-title":"Progress towards practical quantum variational algorithms","volume":"92","author":"Wecker","year":"2015","journal-title":"Phys. Rev. A"},{"key":"mlstac28ddbib30","doi-asserted-by":"publisher","DOI":"10.1002\/qute.201900070","article-title":"Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms","volume":"2","author":"Sim","year":"2019","journal-title":"Adv. Quantum Technol."},{"key":"mlstac28ddbib31","article-title":"The quantum approximate optimization algorithm needs to see the whole graph: a typical case","author":"Farhi","year":"2020"},{"key":"mlstac28ddbib32","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.124.090504","article-title":"Reachability deficits in quantum approximate optimization","volume":"124","author":"Akshay","year":"2020","journal-title":"Phys. Rev. Lett."},{"key":"mlstac28ddbib33","article-title":"Single-qubit rotations in parameterized quantum circuits","author":"Rasmussen","year":"2020"},{"key":"mlstac28ddbib34","article-title":"Reachability deficits implicit in Google\u2019s quantum approximate optimization of graph problems","author":"Akshay","year":"2020"},{"key":"mlstac28ddbib35","article-title":"Taking human out of learning applications: a survey on automated machine learning","author":"Yao","year":"2018"},{"key":"mlstac28ddbib36","first-page":"1","article-title":"Neural architecture search: a survey","volume":"20","author":"Elsken","year":"2018","journal-title":"J. Mach. Learn. Res."},{"key":"mlstac28ddbib37","article-title":"A survey on neural architecture search","author":"Wistuba","year":"2019"},{"key":"mlstac28ddbib38","article-title":"Survey of neural architecture search: challenges and solutions","author":"Ren","year":"2020"},{"key":"mlstac28ddbib39","article-title":"Differentiable quantum architecture search","author":"Zhang","year":"2020"},{"key":"mlstac28ddbib40","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.116.230504","article-title":"Genetic algorithms for digital quantum simulations","volume":"116","author":"Las Heras","year":"2016","journal-title":"Phys. Rev. Lett."},{"key":"mlstac28ddbib41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1515\/qmetro-2017-0001","article-title":"Approximate quantum adders with genetic algorithms: an IBM quantum experience","volume":"4","author":"Li","year":"2017","journal-title":"Quantum Meas. Quantum Metrol."},{"key":"mlstac28ddbib42","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/aae94a","article-title":"Learning the quantum algorithm for state overlap","volume":"20","author":"Cincio","year":"2018","journal-title":"New J. Phys."},{"key":"mlstac28ddbib43","article-title":"Noise-resistant, hardware-efficient evolutionary variational quantum Eigensolver","author":"Rattew","year":"2019"},{"key":"mlstac28ddbib44","article-title":"Machine learning of noise-resilient quantum circuits","author":"Cincio","year":"2020"},{"key":"mlstac28ddbib45","article-title":"MoG-VQE: multiobjective genetic variational quantum eigensolver","author":"Chivilikhin","year":"2020"},{"key":"mlstac28ddbib46","article-title":"Markovian quantum neuroevolution for machine learning","author":"Lu","year":"2020"},{"key":"mlstac28ddbib47","article-title":"Reinforcement learning with neural networks for quantum feedback","volume":"8","author":"F\u00f6sel","year":"2018","journal-title":"Phys. Rev. X"},{"key":"mlstac28ddbib48","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1038\/s41534-019-0141-3","article-title":"Universal quantum control through deep reinforcement learning","volume":"5","author":"Niu","year":"2019","journal-title":"npj Quantum Inf."},{"key":"mlstac28ddbib49","article-title":"Quantum circuit structure learning","author":"Ostaszewski","year":"2019"},{"key":"mlstac28ddbib50","article-title":"Qubit-ADAPT-VQE: an adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor","author":"Tang","year":"2019"},{"key":"mlstac28ddbib51","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevResearch.2.023074","article-title":"Quantum optimization with a novel Gibbs objective function and ansatz architecture search","volume":"2","author":"Li","year":"2020","journal-title":"Phys. Rev. Res."},{"key":"mlstac28ddbib52","first-page":"883","article-title":"Understanding and simplifying one-shot architecture search","author":"Bender","year":"2018","journal-title":"ICML"},{"key":"mlstac28ddbib53","article-title":"Balanced one-shot neural architecture optimization","author":"Luo","year":"2019"},{"key":"mlstac28ddbib54","first-page":"544","article-title":"Single path one-shot neural architecture search with uniform sampling","author":"Guo","year":"2020","journal-title":"ECCV"},{"key":"mlstac28ddbib55","article-title":"DARTS: differentiable architecture search","author":"Liu","year":"2019","journal-title":"ICLR"},{"key":"mlstac28ddbib56","article-title":"Quantum circuit architecture search: error mitigation and trainability enhancement for variational quantum solvers","author":"Du","year":"2020"},{"key":"mlstac28ddbib57","article-title":"Peephole: predicting network performance before training","author":"Deng","year":"2017"},{"key":"mlstac28ddbib58","article-title":"Progressive neural architecture search","author":"Liu","year":"2017"},{"key":"mlstac28ddbib59","article-title":"ChamNet: towards efficient network design through platform-aware model adaptation","author":"Dai","year":"2019","journal-title":"CVPR"},{"key":"mlstac28ddbib60","article-title":"AlphaX: eXploring neural architectures with deep neural networks and Monte Carlo tree search","author":"Wang","year":"2019"},{"key":"mlstac28ddbib61","article-title":"Neural architecture search via Bayesian optimization with a neural network model","author":"White","year":"2019","journal-title":"NeurIPS"},{"key":"mlstac28ddbib62","article-title":"Bridging the Gap between sample-based and one-shot neural architecture search with BONAS","author":"Shi","year":"2019","journal-title":"NeurIPS"},{"key":"mlstac28ddbib63","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1007\/978-3-030-58526-6_39","article-title":"Neural Predictor for Neural Architecture Search","volume":"12374","author":"Wen","year":"2020","journal-title":"Lect. Notes Comput. Sci. Eng."},{"key":"mlstac28ddbib64","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-030-58601-0_12","article-title":"A generic graph-based neural architecture encoding scheme for predictor-based NAS","author":"Ning","year":"2020"},{"key":"mlstac28ddbib65","article-title":"Fitting the search space of weight-sharing nas with graph convolutional networks","author":"Chen","year":"2020"},{"key":"mlstac28ddbib66","article-title":"A study on encodings for neural architecture search","author":"White","year":"2020"},{"key":"mlstac28ddbib67","article-title":"Neural architecture performance prediction using graph neural networks","author":"Lukasik","year":"2020"},{"key":"mlstac28ddbib68","article-title":"BRP-NAS: prediction-based NAS using GCNs","author":"Dudziak","year":"2020"},{"key":"mlstac28ddbib69","article-title":"Efficient sampling for predictor-based neural architecture search","author":"Mauch","year":"2020"},{"key":"mlstac28ddbib70","article-title":"Random search and reproducibility for neural architecture search","author":"Li","year":"2019"},{"key":"mlstac28ddbib71","doi-asserted-by":"crossref","DOI":"10.1145\/3240508.3240588","article-title":"GNAS: a greedy neural architecture search method for multi-attribute learning","author":"Huang","year":"2018"},{"key":"mlstac28ddbib72","article-title":"Local search is state of the art for NAS benchmarks","author":"White","year":"2020"},{"key":"mlstac28ddbib73","article-title":"SGAS: sequential greedy architecture search","author":"Li","year":"2019"},{"key":"mlstac28ddbib74","first-page":"2902","article-title":"Large-scale evolution of image classifiers","author":"Real","year":"2017","journal-title":"ICML"},{"key":"mlstac28ddbib75","first-page":"1388","article-title":"Genetic CNN","author":"Xie","year":"2017","journal-title":"ICCV"},{"key":"mlstac28ddbib76","article-title":"Hierarchical representations for efficient architecture search","author":"Liu","year":"2018","journal-title":"ICLR"},{"key":"mlstac28ddbib77","doi-asserted-by":"publisher","first-page":"4780","DOI":"10.1609\/aaai.v33i01.33014780","article-title":"Regularized evolution for image classifier architecture search","author":"Real","year":"2019","journal-title":"AAAI"},{"key":"mlstac28ddbib78","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1038\/s42256-018-0006-z","article-title":"Designing neural networks through neuroevolution","volume":"1","author":"Stanley","year":"2019","journal-title":"Nat. Mach. Intell."},{"key":"mlstac28ddbib79","article-title":"Neural architecture search with reinforcement learning","author":"Zoph","year":"2017","journal-title":"ICLR"},{"key":"mlstac28ddbib80","article-title":"Designing neural network architectures using reinforcement learning","author":"Baker","year":"2017","journal-title":"ICLR"},{"key":"mlstac28ddbib81","first-page":"2787","article-title":"Efficient architecture search by network transformation","author":"Cai","year":"2018","journal-title":"AAAI"},{"key":"mlstac28ddbib82","first-page":"8697","article-title":"Learning transferable architectures for scalable image recognition","author":"Zoph","year":"2018","journal-title":"CVPR"},{"key":"mlstac28ddbib83","article-title":"Probabilistic neural architecture search","author":"Casale","year":"2019"},{"key":"mlstac28ddbib84","article-title":"Proxyless nas: direct neural architecture search on target task and hardware","author":"Cai","year":"2019","journal-title":"ICLR"},{"key":"mlstac28ddbib85","article-title":"SNAS: stochastic neural architecture search","author":"Xie","year":"2019","journal-title":"ICLR"},{"key":"mlstac28ddbib86","article-title":"Auto-encoding variational Bayes","author":"Kingma","year":"2014","journal-title":"ICLR"},{"key":"mlstac28ddbib87","first-page":"7816","article-title":"Neural architecture optimization","author":"Luo","year":"2018","journal-title":"NeurIPS"},{"key":"mlstac28ddbib88","article-title":"D-VAE: A variational autoencoder for directed acyclic graphs","author":"Zhang","year":"2019","journal-title":"NeurIPS"},{"key":"mlstac28ddbib89","article-title":"A variational-sequential graph autoencoder for neural architecture performance prediction","author":"Friede","year":"2019"},{"key":"mlstac28ddbib90","article-title":"NASGEM: neural architecture search via graph embedding method","author":"Cheng","year":"2020"},{"key":"mlstac28ddbib91","article-title":"Neural architecture optimization with graph VAE","author":"Li","year":"2020"},{"key":"mlstac28ddbib92","article-title":"Disentangled neural architecture search","author":"Zheng","year":"2020"},{"key":"mlstac28ddbib93","first-page":"1807","article-title":"A semi-supervised assessor of neural architectures","author":"Tang","year":"2020","journal-title":"CVPR"},{"key":"mlstac28ddbib94","doi-asserted-by":"publisher","DOI":"10.1021\/acs.chemrev.8b00803","article-title":"Quantum chemistry in the age of quantum computing","volume":"119","author":"Cao","year":"2019","journal-title":"Chem. Rev."},{"key":"mlstac28ddbib95","doi-asserted-by":"publisher","first-page":"156","DOI":"10.22331\/q-2019-07-01-156","article-title":"Variational Quantum Computation of Excited States","volume":"3","author":"Higgott","year":"2019","journal-title":"Quantum"},{"key":"mlstac28ddbib96","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevResearch.1.033062","article-title":"Subspace-search variational quantum eigensolver for excited states","volume":"1","author":"Nakanishi","year":"2019","journal-title":"Phys. Rev. Res."},{"key":"mlstac28ddbib97","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1038\/s41534-020-00347-1","article-title":"Unitary-coupled restricted Boltzmann machine ansatz for quantum simulations","volume":"7","author":"Hsieh","year":"2021","journal-title":"npj Quantum Inf."},{"key":"mlstac28ddbib98","article-title":"Efficient Variational Quantum Simulator Incorporating Active Error Minimization","volume":"7","author":"Li","year":"2017","journal-title":"Phys. Rev. X"},{"key":"mlstac28ddbib99","doi-asserted-by":"publisher","first-page":"191","DOI":"10.22331\/q-2019-10-07-191","article-title":"Theory of variational quantum simulation","volume":"3","author":"Yuan","year":"2019","journal-title":"Quantum"},{"key":"mlstac28ddbib100","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1038\/s41534-019-0187-2","article-title":"Variational ansatz-based quantum simulation of imaginary time evolution","volume":"5","author":"McArdle","year":"2019","journal-title":"npj Quantum Inf."},{"key":"mlstac28ddbib101","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1038\/s41534-020-00302-0","article-title":"Variational fast forwarding for quantum simulation beyond the coherence time","volume":"6","author":"C\u00eerstoiu","year":"2020","journal-title":"npj Quantum Inf."},{"key":"mlstac28ddbib102","article-title":"Real- and imaginary-time evolution with compressed quantum circuits","author":"Lin","year":"2020"},{"key":"mlstac28ddbib103","article-title":"Quantum assisted simulation of time dependent Hamiltonians","author":"Lau","year":"2021"},{"key":"mlstac28ddbib104","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.125.010501","article-title":"Variational quantum simulation of general processes","volume":"125","author":"Endo","year":"2020","journal-title":"Phys. Rev. Lett."},{"key":"mlstac28ddbib105","article-title":"Hardware-efficient variational quantum algorithms for time evolution","author":"Benedetti","year":"2020"},{"key":"mlstac28ddbib106","article-title":"Quantum assisted simulator","author":"Bharti","year":"2020"},{"key":"mlstac28ddbib107","doi-asserted-by":"crossref","DOI":"10.22331\/q-2021-07-28-512","article-title":"An efficient quantum algorithm for the time evolution of parameterized circuits","author":"Barison","year":"2021"},{"key":"mlstac28ddbib108","article-title":"Solving quantum statistical mechanics with variational autoregressive networks and quantum circuits","author":"Liu","year":"2019"},{"key":"mlstac28ddbib109","article-title":"Variational quantum Gibbs state preparation with a truncated Taylor series","author":"Wang","year":"2020"},{"key":"mlstac28ddbib110","article-title":"A variational quantum algorithm for preparing quantum Gibbs states","author":"Chowdhury","year":"2020"},{"key":"mlstac28ddbib111","doi-asserted-by":"publisher","first-page":"140","DOI":"10.22331\/q-2019-05-13-140","article-title":"Quantum-assisted quantum compiling","volume":"3","author":"Khatri","year":"2019","journal-title":"Quantum"},{"key":"mlstac28ddbib112","article-title":"Variational quantum gate optimization","author":"Heya","year":"2018"},{"key":"mlstac28ddbib113","doi-asserted-by":"publisher","DOI":"10.1088\/1367-2630\/ab784c","article-title":"Noise resilience of variational quantum compiling","volume":"22","author":"Sharma","year":"2020","journal-title":"New J. Phys."},{"key":"mlstac28ddbib114","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/978-3-030-14082-3_7","volume":"11413","author":"Anschuetz","year":"2019","journal-title":"Lect. Notes Comput. Sci. Eng."},{"key":"mlstac28ddbib115","article-title":"A hybrid scheme for prime factorization and its experimental implementation using IBM quantum processor","author":"Saxena","year":"2020"},{"key":"mlstac28ddbib116","article-title":"Analyzing the performance of variational quantum factoring on a superconducting quantum processor","author":"Karamlou","year":"2020"},{"key":"mlstac28ddbib117","article-title":"Near-term quantum algorithms for linear systems of equations","author":"Huang","year":"2019"},{"key":"mlstac28ddbib118","article-title":"Variational algorithms for linear algebra","author":"Xu","year":"2019"},{"key":"mlstac28ddbib119","article-title":"Variational quantum linear solver","author":"Bravo-Prieto","year":"2019"},{"key":"mlstac28ddbib120","article-title":"Solving nonlinear differential equations with differentiable quantum circuits","author":"Kyriienko","year":"2020"},{"key":"mlstac28ddbib121","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevA.101.010301","article-title":"Variational quantum algorithms for nonlinear problems","volume":"101","author":"Lubasch","year":"2020","journal-title":"Phys. Rev. A"},{"key":"mlstac28ddbib122","doi-asserted-by":"publisher","first-page":"3589","DOI":"10.1038\/srep03589","article-title":"From transistor to trapped-ion computers for quantum chemistry","volume":"4","author":"Yung","year":"2015","journal-title":"Sci. Rep."},{"key":"mlstac28ddbib123","doi-asserted-by":"publisher","first-page":"4812","DOI":"10.1038\/s41467-018-07090-4","article-title":"Barren plateaus in quantum neural network training landscapes","volume":"9","author":"McClean","year":"2018","journal-title":"Nat. Commun."},{"key":"mlstac28ddbib124","article-title":"Noise-induced Barren plateaus in variational quantum algorithms","author":"Wang","year":"2020"},{"key":"mlstac28ddbib125","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.119.180509","article-title":"Error mitigation for short-depth quantum circuits","volume":"119","author":"Temme","year":"2017","journal-title":"Phys. Rev. Lett."},{"key":"mlstac28ddbib126","doi-asserted-by":"publisher","first-page":"214","DOI":"10.22331\/q-2019-12-09-214","article-title":"An initialization strategy for addressing barren plateaus in parametrized quantum circuits","volume":"3","author":"Grant","year":"2019","journal-title":"Quantum"},{"key":"mlstac28ddbib127","article-title":"Entanglement devised Barren plateau mitigation","author":"Patti","year":"2020"},{"key":"mlstac28ddbib128","article-title":"Cost-function-dependent barren plateaus in shallow quantum neural networks","author":"Cerezo","year":"2020"},{"key":"mlstac28ddbib129","article-title":"Limitations of optimization algorithms on noisy quantum devices","author":"Franca","year":"2020"},{"key":"mlstac28ddbib130","article-title":"Quantum circuit design search","author":"Pirhooshyaran","year":"2020"},{"key":"mlstac28ddbib131","article-title":"A deep learning model for noise prediction on near-term quantum devices","author":"Zlokapa","year":"2020"},{"key":"mlstac28ddbib132","article-title":"Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms","author":"Xiao","year":"2017"},{"key":"mlstac28ddbib133","doi-asserted-by":"publisher","first-page":"1050","DOI":"10.1038\/s41567-020-0932-7","article-title":"Predicting many properties of a quantum system from very few measurements","volume":"16","author":"Huang","year":"2020","journal-title":"Nat. Phys."},{"key":"mlstac28ddbib134","article-title":"VSQL: variational shadow quantum learning for classification","author":"Li","year":"2020"},{"key":"mlstac28ddbib135","article-title":"TensorFlow quantum: a software framework for quantum machine learning","author":"Broughton","year":"2020"}],"container-title":["Machine Learning: Science and Technology"],"original-title":[],"link":[{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac28dd","content-type":"text\/html","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac28dd\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac28dd","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac28dd\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac28dd\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac28dd\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac28dd\/pdf","content-type":"application\/pdf","content-version":"am","intended-application":"similarity-checking"},{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac28dd\/pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,1,9]],"date-time":"2023-01-09T21:59:20Z","timestamp":1673301560000},"score":1,"resource":{"primary":{"URL":"https:\/\/iopscience.iop.org\/article\/10.1088\/2632-2153\/ac28dd"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,8]]},"references-count":135,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2021,10,8]]},"published-print":{"date-parts":[[2021,12,1]]}},"URL":"https:\/\/doi.org\/10.1088\/2632-2153\/ac28dd","relation":{},"ISSN":["2632-2153"],"issn-type":[{"value":"2632-2153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,10,8]]},"assertion":[{"value":"Neural predictor based quantum architecture search","name":"article_title","label":"Article Title"},{"value":"Machine Learning: Science and Technology","name":"journal_title","label":"Journal Title"},{"value":"paper","name":"article_type","label":"Article Type"},{"value":"\u00a9 2021 The Author(s). Published by IOP Publishing Ltd","name":"copyright_information","label":"Copyright Information"},{"value":"2021-07-06","name":"date_received","label":"Date Received","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-09-21","name":"date_accepted","label":"Date Accepted","group":{"name":"publication_dates","label":"Publication dates"}},{"value":"2021-10-08","name":"date_epub","label":"Online publication date","group":{"name":"publication_dates","label":"Publication dates"}}]}}