Optimizing quantum heuristics with meta-learning
暂无分享,去创建一个
[1] Stuart Hadfield,et al. On the Representation of Boolean and Real Functions as Hamiltonians for Quantum Computing , 2018, ACM Transactions on Quantum Computing.
[2] P. Barkoutsos,et al. Entanglement production and convergence properties of the variational quantum eigensolver , 2020, 2003.12490.
[3] Stuart Hadfield,et al. Characterizing local noise in QAOA circuits , 2020, IOP SciNotes.
[4] Yuchun Wu,et al. Effects of Quantum Noise on Quantum Approximate Optimization Algorithm , 2019, Chinese Physics Letters.
[5] Masoud Mohseni,et al. Learning to learn with quantum neural networks via classical neural networks , 2019, ArXiv.
[6] Murphy Yuezhen Niu,et al. Optimizing QAOA: Success Probability and Runtime Dependence on Circuit Depth , 2019, 1905.12134.
[7] Tad Hogg,et al. From Ans\"atze to Z-gates: a NASA View of Quantum Computing , 2019, 1905.02860.
[8] Nicholas C. Rubin,et al. $XY$-mixers: analytical and numerical results for QAOA , 2019, 1904.09314.
[9] Fei Yan,et al. A quantum engineer's guide to superconducting qubits , 2019, Applied Physics Reviews.
[10] Edward Grant,et al. An initialization strategy for addressing barren plateaus in parametrized quantum circuits , 2019, Quantum.
[11] G. Guerreschi,et al. QAOA for Max-Cut requires hundreds of qubits for quantum speed-up , 2018, Scientific Reports.
[12] C. Gogolin,et al. Evaluating analytic gradients on quantum hardware , 2018, Physical Review A.
[13] Giacomo Nannicini,et al. Performance of hybrid quantum/classical variational heuristics for combinatorial optimization , 2018, Physical review. E.
[14] David J. Schwab,et al. A high-bias, low-variance introduction to Machine Learning for physicists , 2018, Physics reports.
[15] Rupak Biswas,et al. From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz , 2017, Algorithms.
[16] H. Neven,et al. Barren plateaus in quantum neural network training landscapes , 2018, Nature Communications.
[17] T. Monz,et al. Quantum Chemistry Calculations on a Trapped-Ion Quantum Simulator , 2018, Physical Review X.
[18] Joshua Achiam,et al. On First-Order Meta-Learning Algorithms , 2018, ArXiv.
[19] John Preskill,et al. Quantum Computing in the NISQ era and beyond , 2018, Quantum.
[20] Andrew W. Cross,et al. Quantum optimization using variational algorithms on near-term quantum devices , 2017, Quantum Science and Technology.
[21] J. McClean,et al. Strategies for quantum computing molecular energies using the unitary coupled cluster ansatz , 2017, Quantum Science and Technology.
[22] Ben Moseley,et al. Bayesian optimisation for variational quantum eigensolvers , 2018 .
[23] Michael Broughton,et al. A quantum algorithm to train neural networks using low-depth circuits , 2017, 1712.05304.
[24] Rupak Biswas,et al. Quantum Approximate Optimization with Hard and Soft Constraints , 2017 .
[25] Quoc V. Le,et al. Neural Optimizer Search with Reinforcement Learning , 2017, ICML.
[26] J. Gambetta,et al. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets , 2017, Nature.
[27] Misha Denil,et al. Learned Optimizers that Scale and Generalize , 2017, ICML.
[28] Xi Chen,et al. Evolution Strategies as a Scalable Alternative to Reinforcement Learning , 2017, ArXiv.
[29] Masoud Mohseni,et al. Commercialize quantum technologies in five years , 2017, Nature.
[30] Sangram Ganguly,et al. DeepSD: Generating High Resolution Climate Change Projections through Single Image Super-Resolution , 2017, KDD.
[31] Hong Yu,et al. Meta Networks , 2017, ICML.
[32] Mikhail Smelyanskiy,et al. Practical optimization for hybrid quantum-classical algorithms , 2017, 1701.01450.
[33] Brian Moritz,et al. Numerical evidence of fluctuating stripes in the normal state of high-Tc cuprate superconductors , 2016, Science.
[34] Misha Denil,et al. Learning to Learn without Gradient Descent by Gradient Descent , 2016, ICML.
[35] Hugo Larochelle,et al. Optimization as a Model for Few-Shot Learning , 2016, ICLR.
[36] Roman Neruda,et al. Evolution Strategies for Deep Neural Network Models Design , 2017, ITAT.
[37] C A Nelson,et al. Learning to Learn , 2017, Encyclopedia of Machine Learning and Data Mining.
[38] N. Rubin. A Hybrid Classical/Quantum Approach for Large-Scale Studies of Quantum Systems with Density Matrix Embedding Theory , 2016, 1610.06910.
[39] Daan Wierstra,et al. Meta-Learning with Memory-Augmented Neural Networks , 2016, ICML.
[40] Marcin Andrychowicz,et al. Learning to learn by gradient descent by gradient descent , 2016, NIPS.
[41] M. Hastings,et al. Training A Quantum Optimizer , 2016, 1605.05370.
[42] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[43] P. Coveney,et al. Scalable Quantum Simulation of Molecular Energies , 2015, 1512.06860.
[44] Aaron Klein,et al. Efficient and Robust Automated Machine Learning , 2015, NIPS.
[45] M. Hastings,et al. Progress towards practical quantum variational algorithms , 2015, 1507.08969.
[46] Michael I. Jordan,et al. Machine learning: Trends, perspectives, and prospects , 2015, Science.
[47] Andrey E. Antipov,et al. Solutions of the Two-Dimensional Hubbard Model: Benchmarks and Results from a Wide Range of Numerical Algorithms , 2015, 1505.02290.
[48] William Stafford Noble,et al. Machine learning applications in genetics and genomics , 2015, Nature Reviews Genetics.
[49] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[50] Alán Aspuru-Guzik,et al. A variational eigenvalue solver on a photonic quantum processor , 2013, Nature Communications.
[51] Bogdan Gabrys,et al. Metalearning: a survey of trends and technologies , 2013, Artificial Intelligence Review.
[52] Jordi Torres,et al. Towards energy-aware scheduling in data centers using machine learning , 2010, e-Energy.
[53] G. Evans,et al. Learning to Optimize , 2008 .
[54] E. Knill,et al. Optimal quantum measurements of expectation values of observables , 2006, quant-ph/0607019.
[55] Ricardo Vilalta,et al. A Perspective View and Survey of Meta-Learning , 2002, Artificial Intelligence Review.
[56] J. Spall,et al. Theoretical framework for comparing several popular stochastic optimization approaches , 2002 .
[57] Paul Charbonneau,et al. An Introduction to Genetic Algorithms for Numerical Optimization , 2002 .
[58] Eric Jones,et al. SciPy: Open Source Scientific Tools for Python , 2001 .
[59] Robert Krauthgamer,et al. A polylogarithmic approximation of the minimum bisection , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.
[60] Giorgio Gambosi,et al. Complexity and approximation: combinatorial optimization problems and their approximability properties , 1999 .
[61] Sepp Hochreiter,et al. The Vanishing Gradient Problem During Learning Recurrent Neural Nets and Problem Solutions , 1998, Int. J. Uncertain. Fuzziness Knowl. Based Syst..
[62] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[63] J. Nocedal,et al. A Limited Memory Algorithm for Bound Constrained Optimization , 1995, SIAM J. Sci. Comput..
[64] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.
[65] H. Schulz. Interacting fermions in one dimension: from weak to strong correlation , 1993, cond-mat/9302006.
[66] David Beasley,et al. An overview of genetic algorithms: Part 1 , 1993 .
[67] J. Spall. Multivariate stochastic approximation using a simultaneous perturbation gradient approximation , 1992 .
[68] Yoshua Bengio,et al. Learning a synaptic learning rule , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.
[69] John A. Nelder,et al. A Simplex Method for Function Minimization , 1965, Comput. J..
[70] J. Hubbard. Electron correlations in narrow energy bands , 1963, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.
[71] E. Wigner,et al. Über das Paulische Äquivalenzverbot , 1928 .