Perils of embedding for sampling problems
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[1] M. Benedetti,et al. Estimation of effective temperatures in quantum annealers for sampling applications: A case study with possible applications in deep learning , 2015, 1510.07611.
[2] M. W. Johnson,et al. Phase transitions in a programmable quantum spin glass simulator , 2018, Science.
[3] M. W. Johnson,et al. Quantum annealing with manufactured spins , 2011, Nature.
[4] Rupak Biswas,et al. Quantum-Assisted Learning of Hardware-Embedded Probabilistic Graphical Models , 2016, 1609.02542.
[5] D. Landau,et al. Efficient, multiple-range random walk algorithm to calculate the density of states. , 2000, Physical review letters.
[6] Eleanor G. Rieffel,et al. Thermalization, Freeze-out, and Noise: Deciphering Experimental Quantum Annealers , 2017, 1703.03902.
[7] D. Landau,et al. Determining the density of states for classical statistical models: a random walk algorithm to produce a flat histogram. , 2001, Physical review. E, Statistical, nonlinear, and soft matter physics.
[8] Tad Hogg,et al. Quantum-assisted associative adversarial network: applying quantum annealing in deep learning , 2019, Quantum Machine Intelligence.
[9] M. Amin. Searching for quantum speedup in quasistatic quantum annealers , 2015, 1503.04216.
[10] Vicky Choi,et al. Minor-embedding in adiabatic quantum computation: II. Minor-universal graph design , 2010, Quantum Inf. Process..
[11] Walter Vinci,et al. Quantum variational autoencoder , 2018, Quantum Science and Technology.
[12] Yasunobu Nakamura,et al. Boltzmann sampling from the Ising model using quantum heating of coupled nonlinear oscillators , 2018, Scientific Reports.
[13] Steven H. Adachi,et al. Application of Quantum Annealing to Training of Deep Neural Networks , 2015, ArXiv.
[14] Joshua Job,et al. Restricted Boltzmann Machines for galaxy morphology classification with a quantum annealer , 2019, ArXiv.
[15] Itay Hen,et al. Analog errors in Ising machines , 2018, Quantum Science and Technology.
[16] Jack Raymond,et al. Global Warming: Temperature Estimation in Annealers , 2016, Front. ICT.
[17] Yasunobu Nakamura,et al. Boltzmann sampling from the Ising model using quantum heating of coupled nonlinear oscillators , 2017, Scientific Reports.
[18] E. Rieffel,et al. Power of Pausing: Advancing Understanding of Thermalization in Experimental Quantum Annealers , 2018, Physical Review Applied.
[19] A. Cavagna,et al. Spin-glass theory for pedestrians , 2005, cond-mat/0505032.
[20] S. Knysh,et al. Quantum Optimization of Fully-Connected Spin Glasses , 2014, 1406.7553.
[21] Vicky Choi,et al. Minor-embedding in adiabatic quantum computation: I. The parameter setting problem , 2008, Quantum Inf. Process..
[22] Daniel A. Lidar,et al. Limitations of error corrected quantum annealing in improving the performance of Boltzmann machines , 2020, Quantum Science and Technology.
[23] Daniel A. Lidar,et al. Analog errors in quantum annealing: doom and hope , 2019, npj Quantum Information.
[24] Martin Weigel,et al. Estimating the density of states of frustrated spin systems , 2018, New Journal of Physics.
[25] Daniel A. Lidar,et al. Demonstration of a Scaling Advantage for a Quantum Annealer over Simulated Annealing , 2017, Physical Review X.
[26] Roger Melko,et al. Quantum Boltzmann Machine , 2016, 1601.02036.