The effects of quantum hardware properties on the performances of variational quantum learning algorithms
暂无分享,去创建一个
[1] F. Gargiulo,et al. Best practices for portfolio optimization by quantum computing, experimented on real quantum devices , 2023, Scientific reports.
[2] Y. M. Rhee,et al. Orbital-optimized pair-correlated electron simulations on trapped-ion quantum computers , 2022, npj Quantum Information.
[3] Patrick J. Coles,et al. Challenges and opportunities in quantum machine learning , 2022, Nature Computational Science.
[4] T. Miki,et al. Variational Ansatz preparation to avoid CNOT-gates on noisy quantum devices for combinatorial optimizations , 2022, AIP Advances.
[5] George H. Booth,et al. The Variational Quantum Eigensolver: A review of methods and best practices , 2021, Physics Reports.
[6] M. Motta,et al. Emerging quantum computing algorithms for quantum chemistry , 2021, WIREs Computational Molecular Science.
[7] Cedric Yen-Yu Lin,et al. General parameter-shift rules for quantum gradients , 2021, Quantum.
[8] Patrick J. Coles. Seeking quantum advantage for neural networks , 2021, Nature Computational Science.
[9] Giovanni Acampora,et al. Deep neural networks for quantum circuit mapping , 2021, Neural Computing and Applications.
[10] C. Gogolin,et al. Local, expressive, quantum-number-preserving VQE ansätze for fermionic systems , 2021, New Journal of Physics.
[11] Patrick J. Coles,et al. Cost function dependent barren plateaus in shallow parametrized quantum circuits , 2021, Nature Communications.
[12] Chang-Yu Hsieh,et al. Neural predictor based quantum architecture search , 2021, Mach. Learn. Sci. Technol..
[13] M. Cerezo,et al. Variational quantum algorithms , 2020, Nature Reviews Physics.
[14] M. Cerezo,et al. Effect of barren plateaus on gradient-free optimization , 2020, Quantum.
[15] Stefan Woerner,et al. The power of quantum neural networks , 2020, Nature Computational Science.
[16] Patrick J. Coles,et al. Noise-induced barren plateaus in variational quantum algorithms , 2020, Nature Communications.
[17] P. Barkoutsos,et al. Quantum orbital-optimized unitary coupled cluster methods in the strongly correlated regime: Can quantum algorithms outperform their classical equivalents? , 2019, The Journal of chemical physics.
[18] G. Pryde,et al. Photonic quantum information processing: A concise review , 2019, Applied Physics Reviews.
[19] Margaret Martonosi,et al. Noise-Adaptive Compiler Mappings for Noisy Intermediate-Scale Quantum Computers , 2019, ASPLOS.
[20] Fred W. Glover,et al. Quantum Bridge Analytics I: a tutorial on formulating and using QUBO models , 2018, 4OR.
[21] Guang-Can Guo,et al. Quantum Neural Network States: A Brief Review of Methods and Applications , 2018, Advanced Quantum Technologies.
[22] Kristan Temme,et al. Supervised learning with quantum-enhanced feature spaces , 2018, Nature.
[23] Simone Severini,et al. Hierarchical quantum classifiers , 2018, npj Quantum Information.
[24] Ryan Babbush,et al. Barren plateaus in quantum neural network training landscapes , 2018, Nature Communications.
[25] J. Gambetta,et al. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets , 2017, Nature.
[26] S. Lloyd,et al. Quantum machine learning , 2016, Nature.
[27] Alán Aspuru-Guzik,et al. A variational eigenvalue solver on a photonic quantum processor , 2013, Nature Communications.
[28] Andrew Lucas,et al. Ising formulations of many NP problems , 2013, Front. Physics.
[29] F. Schmidt-Kaler,et al. Quantum computing with trapped ions , 2008, 0809.4368.
[30] J. Clarke,et al. Superconducting quantum bits , 2008, Nature.
[31] Sun Xiao-ling,et al. A branch-and-bound algorithm for discrete multi-factor portfolio optimization model , 2008 .
[32] Harry Markowitz,et al. Portfolio Selection , 1971 .