Fetching the paper…
Reading the bibliography…
With the increased focus on quantum circuit learning for near-term applications on quantum devices, in conjunction with unique challenges presented by cost function landscapes of parametrized quantum circuits, strategies for effective training are becoming increasingly important.
1904
Earlier work this paper cites.
1907
Earlier work this paper cites.
D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,”
1986
Earlier work this paper cites.
S. E. Fahlman and C. Lebiere, “The cascade-correlation learning architecture,”
1990
Earlier work this paper cites.
G. E. Hinton, S. Osindero, and Y.-W. Teh, “A Fast Learning Algorithm for Deep Belief Nets,”
2006
Earlier work this paper cites.
E. Knill, G. Ortiz, and R. D. Somma, “Optimal quantum measurements of expectation values of observables,”
2007
Earlier work this paper cites.
Y. Bengio, Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy layer-wise training of deep networks,”
2007
Earlier work this paper cites.
A. W. Harrow and R. A. Low, “Random Quantum Circuits are Approximate 2-designs,”
2009
Earlier work this paper cites.
E. Farhi, J. Goldstone, and S. Gutmann, “A Quantum Approximate Optimization Algorithm,”
2014
Earlier work this paper cites.
A. Peruzzo, J. McClean, P. Shadbolt, M.-H. Yung, X.-Q. Zhou, P. J. Love, A. Aspuru-Guzik, and J. L. O’brien, “A variational eigenvalue solver on a photonic quantum processor,”
2014
Earlier work this paper cites.
M.-H. Yung, J. Casanova, A. Mezzacapo, J. McClean, L. Lamata, A. Aspuru-Guzik, and E. Solano, “From transistor to trapped-ion computers for quantum chemistry,”
2014
Earlier work this paper cites.
D. Wecker, M. B. Hastings, and M. Troyer, “Progress towards practical quantum variational algorithms,”
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,”
2015
Earlier work this paper cites.
J. R. McClean, J. Romero, R. Babbush, and A. Aspuru-Guzik, “The theory of variational hybrid quantum-classical algorithms,”
2016
Earlier work this paper cites.
P. J. J. O’Malley, R. Babbush, I. D. Kivlichan, J. Romero, J. R. McClean, R. Barends, J. Kelly, P. Roushan, A. Tranter, N. Ding, et al. , “Scalable quantum simulation of molecular energies,”
2016
Earlier work this paper cites.
J. Romero, J. P. Olson, and A. Aspuru-Guzik, “Quantum autoencoders for efficient compression of quantum data,”
2017
Earlier work this paper cites.
A. Kandala, A. Mezzacapo, K. Temme, M. Takita, M. Brink, J. M. Chow, and J. M. Gambetta, “Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets,”
2017
Earlier work this paper cites.
J. R. McClean, M. E. Kimchi-Schwartz, J. Carter, and W. A. de Jong, “Hybrid quantum-classical hierarchy for mitigation of decoherence and determination of excited states,”
2017
Cited alongside, same era.
Y. Shen, X. Zhang, S. Zhang, J.-N. Zhang, M.-H. Yung, and K. Kim, “Quantum implementation of the unitary coupled cluster for simulating molecular electronic structure,”
2017
Cited alongside, same era.
2017
Cited alongside, same era.
E. Grant, M. Benedetti, S. Cao, A. Hallam, J. Lockhart, V. Stojevic, A. G. Green, and S. Severini, “Hierarchical quantum classifiers,”
2018
Cited alongside, same era.
J.-G. Liu and L. Wang, “Differentiable learning of quantum circuit Born machines,”
V. Havlíček, A. D. Córcoles, K. Temme, A. W. Harrow, A. Kandala, J. M. Chow, and J. M. Gambetta, “Supervised learning with quantum-enhanced feature spaces,”
2019
Later among the works it cites.
S. Hadfield, Z. Wang, B. O’Gorman, E. Rieffel, D. Venturelli, R. Biswas, S. Hadfield, Z. Wang, B. O’Gorman, E. G. Rieffel, D. Venturelli, and R. Biswas, “From the Quantum Approximate Optimization Algorithm to a Quantum Alternating Operator Ansatz,”
2019
Later among the works it cites.
G. Nannicini, “Performance of hybrid quantum-classical variational heuristics for combinatorial optimization,”
2019
Later among the works it cites.
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
Z. Wang, S. Hadfield, Z. Jiang, and E. G. Rieffel, “Quantum approximate optimization algorithm for maxcut: A fermionic view,”
2018
Cited alongside, same era.
C. Hempel, C. Maier, J. Romero, J. McClean, T. Monz, H. Shen, P. Jurcevic, B. P. Lanyon, P. Love, R. Babbush, A. Aspuru-Guzik, R. Blatt, and C. F. Roos, “Quantum chemistry calculations on a trapped-ion quantum simulator,”
2018
Cited alongside, same era.
N. C. Rubin, R. Babbush, and J. McClean, “Application of fermionic marginal constraints to hybrid quantum algorithms,”
2018
Cited alongside, same era.
J. I. Colless, V. V. Ramasesh, D. Dahlen, M. S. Blok, M. E. Kimchi-Schwartz, J. R. McClean, J. Carter, W. A. de Jong, and I. Siddiqi, “Computation of molecular spectra on a quantum processor with an error-resilient algorithm,”
2018
Cited alongside, same era.
R. Santagati, J. Wang, A. A. Gentile, S. Paesani, N. Wiebe, J. R. McClean, S. Morley-Short, P. J. Shadbolt, D. Bonneau, J. W. Silverstone, D. P. Tew, X. Zhou, J. L. O’Brien, and M. G. Thompson, “Witnessing eigenstates for quantum simulation of hamiltonian spectra,”
2018
Cited alongside, same era.
E. Farhi and H. Neven, “Classification with quantum neural networks on near term processors,”
2018
Cited alongside, same era.
J. R. McClean, S. Boixo, V. N. Smelyanskiy, R. Babbush, and H. Neven, “Barren plateaus in quantum neural network training landscapes,”
2018
Cited alongside, same era.
2019
Later among the works it cites.
E. Grant, L. Wossnig, M. Ostaszewski, and M. Benedetti, “An initialization strategy for addressing barren plateaus in parametrized quantum circuits,”
2019
Later among the works it cites.
M. Schuld, V. Bergholm, C. Gogolin, J. Izaac, and N. Killoran, “Evaluating analytic gradients on quantum hardware,”
2019
Later among the works it cites.
H. R. Grimsley, S. E. Economou, E. Barnes, and N. J. Mayhall, “An adaptive variational algorithm for exact molecular simulations on a quantum computer,”
2019
Later among the works it cites.
2020
Closest in time.
M. Streif and M. Leib, “Training the quantum approximate optimization algorithm without access to a quantum processing unit,”
2020
Closest in time.
J. R. McClean, Z. Jiang, N. C. Rubin, R. Babbush, and H. Neven, “Decoding quantum errors with subspace expansions,”
2020
Closest in time.
T. Takeshita, N. C. Rubin, Z. Jiang, E. Lee, R. Babbush, and J. R. McClean, “Increasing the representation accuracy of quantum simulations of chemistry without extra quantum resources,”
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.
2020
Closest in time.