An efficient quantum algorithm for the time evolution of parameterized circuits
暂无分享,去创建一个
Giuseppe Carleo | Filippo Vicentini | Stefano Barison | G. Carleo | F. Vicentini | Stefano Barison | Giuseppe Carleo | Filippo Vicentini
[1] Tobias Haug,et al. Optimal training of variational quantum algorithms without barren plateaus , 2021, ArXiv.
[2] D. Coppersmith. An approximate Fourier transform useful in quantum factoring , 2002, quant-ph/0201067.
[3] Gavin E. Crooks,et al. Gradients of parameterized quantum gates using the parameter-shift rule and gate decomposition , 2019, 1905.13311.
[4] Patrick J. Coles,et al. Variational quantum algorithm for estimating the quantum Fisher information , 2020, Physical Review Research.
[5] M. Cerezo,et al. Variational quantum algorithms , 2020, Nature Reviews Physics.
[6] Patrick J. Coles,et al. Variational Hamiltonian Diagonalization for Dynamical Quantum Simulation , 2020, 2009.02559.
[7] E. Knill,et al. Quantum algorithms for fermionic simulations , 2000, cond-mat/0012334.
[8] B. Bauer,et al. Quantum Algorithms for Quantum Chemistry and Quantum Materials Science. , 2020, Chemical reviews.
[9] Elizabeth C. Behrman,et al. Quantum circuit representation of Bayesian networks , 2021, Expert Syst. Appl..
[10] E. Tosatti,et al. Optimization using quantum mechanics: quantum annealing through adiabatic evolution , 2006 .
[11] Maria Schuld,et al. Quantum Machine Learning in Feature Hilbert Spaces. , 2018, Physical review letters.
[12] Keisuke Fujii,et al. Quantum circuit learning , 2018, Physical Review A.
[13] M. Schuld,et al. Circuit-centric quantum classifiers , 2018, Physical Review A.
[14] F. Nori,et al. Quantum Simulation , 2013, Quantum Atom Optics.
[15] F. Verstraete,et al. Time-dependent variational principle for quantum lattices. , 2011, Physical review letters.
[16] Dries Sels,et al. Geometry and non-adiabatic response in quantum and classical systems , 2016, 1602.01062.
[17] I. Shparlinski,et al. Pseudoprime reductions of elliptic curves , 2009, Mathematical Proceedings of the Cambridge Philosophical Society.
[18] B. Clark,et al. Unitary block optimization for variational quantum algorithms , 2021, 2102.08403.
[19] Tsuyoshi Murata,et al. {m , 1934, ACML.
[20] Matthias Troyer,et al. Solving the quantum many-body problem with artificial neural networks , 2016, Science.
[21] H. Trotter. On the product of semi-groups of operators , 1959 .
[22] Marin Bukov,et al. Geometric Speed Limit of Accessible Many-Body State Preparation , 2018, Physical Review X.
[23] Ying Li,et al. Theory of variational quantum simulation , 2018, Quantum.
[24] Soonwon Choi,et al. Quantum convolutional neural networks , 2018, Nature Physics.
[25] Ying Li,et al. Efficient Variational Quantum Simulator Incorporating Active Error Minimization , 2016, 1611.09301.
[26] Garnet Kin-Lic Chan,et al. Quantum Imaginary Time Evolution, Quantum Lanczos, and Quantum Thermal Averaging , 2019 .
[27] Stephen K. Gray,et al. Noise-Resilient Quantum Dynamics Using Symmetry-Preserving Ansatzes , 2019, 1910.06284.
[28] P. Coveney,et al. Scalable Quantum Simulation of Molecular Energies , 2015, 1512.06860.
[29] G. Carleo,et al. Light-cone effect and supersonic correlations in one- and two-dimensional bosonic superfluids , 2013, 1310.2246.
[30] M. Suzuki,et al. General theory of fractal path integrals with applications to many‐body theories and statistical physics , 1991 .
[31] Patrick J. Coles,et al. Variational fast forwarding for quantum simulation beyond the coherence time , 2019, npj Quantum Information.
[32] Marcello Benedetti,et al. Hardware-efficient variational quantum algorithms for time evolution , 2020, Physical Review Research.
[33] J. Spall. Implementation of the simultaneous perturbation algorithm for stochastic optimization , 1998 .
[34] J. Stokes,et al. Quantum Natural Gradient , 2019, Quantum.
[35] Quantum Digital Signatures , 2001, quant-ph/0105032.
[36] Michael I. Jordan,et al. Advances in Neural Information Processing Systems 30 , 1995 .
[37] J. Gambetta,et al. Quantum equation of motion for computing molecular excitation energies on a noisy quantum processor , 2019, 1910.12890.
[38] S. White,et al. Real-time evolution using the density matrix renormalization group. , 2004, Physical review letters.
[39] R. Cleve,et al. Quantum fingerprinting. , 2001, Physical review letters.
[40] A. Green,et al. Real- and Imaginary-Time Evolution with Compressed Quantum Circuits , 2020, PRX Quantum.
[41] J. Herskowitz,et al. Proceedings of the National Academy of Sciences, USA , 1996, Current Biology.
[42] D. Abrams,et al. Simulation of Many-Body Fermi Systems on a Universal Quantum Computer , 1997, quant-ph/9703054.
[43] Roger Melko,et al. Quantum Boltzmann Machine , 2016, 1601.02036.
[44] Guifré Vidal. Efficient simulation of one-dimensional quantum many-body systems. , 2004, Physical review letters.
[45] W. Marsden. I and J , 2012 .
[46] Markus Heyl,et al. Quantum Many-Body Dynamics in Two Dimensions with Artificial Neural Networks. , 2020, Physical review letters.
[47] Michele Fabrizio,et al. Localization and Glassy Dynamics Of Many-Body Quantum Systems , 2011, Scientific Reports.
[48] A. D. McLachlan,et al. A variational solution of the time-dependent Schrodinger equation , 1964 .
[49] Jacob biamonte,et al. Quantum machine learning , 2016, Nature.
[50] Edward Grant,et al. An initialization strategy for addressing barren plateaus in parametrized quantum circuits , 2019, Quantum.
[51] Gavin E. Crooks,et al. Measuring Analytic Gradients of General Quantum Evolution with the Stochastic Parameter Shift Rule , 2020, Quantum.
[52] G. Vidal,et al. Time-dependent density-matrix renormalization-group using adaptive effective Hilbert spaces , 2004 .
[53] J. Gambetta,et al. Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets , 2017, Nature.
[54] Patrick J. Coles,et al. Cost function dependent barren plateaus in shallow parametrized quantum circuits , 2021, Nature Communications.
[55] Ivan Oseledets,et al. Unifying time evolution and optimization with matrix product states , 2014, 1408.5056.
[56] Travis S. Humble,et al. Quantum supremacy using a programmable superconducting processor , 2019, Nature.
[57] Patrick J. Coles,et al. Variational Quantum Linear Solver. , 2020 .
[58] Alán Aspuru-Guzik,et al. A variational eigenvalue solver on a photonic quantum processor , 2013, Nature Communications.
[59] Kevin Barraclough,et al. I and i , 2001, BMJ : British Medical Journal.
[60] A. Green,et al. Parallel quantum simulation of large systems on small NISQ computers , 2020, npj Quantum Information.
[61] Alán Aspuru-Guzik,et al. Quantum autoencoders for efficient compression of quantum data , 2016, 1612.02806.
[62] T. Martínez,et al. Hybrid Quantum/Classical Derivative Theory: Analytical Gradients and Excited-State Dynamics for the Multistate Contracted Variational Quantum Eigensolver , 2019, 1906.08728.
[63] J. Demmel. On condition numbers and the distance to the nearest ill-posed problem , 2015 .
[64] C. Gogolin,et al. Evaluating analytic gradients on quantum hardware , 2018, Physical Review A.
[65] Kishor Bharti,et al. Quantum Assisted Simulator , 2020 .
[66] I. Kassal,et al. Polynomial-time quantum algorithm for the simulation of chemical dynamics , 2008, Proceedings of the National Academy of Sciences.
[67] Kristan Temme,et al. Supervised learning with quantum-enhanced feature spaces , 2018, Nature.
[68] Iordanis Kerenidis,et al. q-means: A quantum algorithm for unsupervised machine learning , 2018, NeurIPS.
[69] P. Dirac. Note on Exchange Phenomena in the Thomas Atom , 1930, Mathematical Proceedings of the Cambridge Philosophical Society.
[70] M. Mézard,et al. Journal of Statistical Mechanics: Theory and Experiment , 2011 .
[71] Ryan Babbush,et al. Barren plateaus in quantum neural network training landscapes , 2018, Nature Communications.
[72] Peter W. Shor,et al. Algorithms for quantum computation: discrete logarithms and factoring , 1994, Proceedings 35th Annual Symposium on Foundations of Computer Science.