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Hybrid quantum-classical optimization using near-term quantum technology is an emerging direction for exploring quantum advantage in high-dimensional systems.
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Cmos compatible fabrication methods for submicron josephson junction qubits
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Charge-insensitive qubit design derived from the cooper pair box
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Simple pulses for elimination of leakage in weakly nonlinear qubits
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Fluxonium: Single cooper-pair circuit free of charge offsets
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Optimized driving of superconducting artificial atoms for improved single-qubit gates
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Reduced phase error through optimized control of a superconducting qubit
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Adaptive subgradient methods for online learning and stochastic optimization
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Quantum computing and the entanglement frontier
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Adadelta: an adaptive learning rate method
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T. & Hinton, G · 2012
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On stochastic gradient and subgradient methods with adaptive steplength sequences
Yousefian, F., Nedić, A. & Shanbhag, U. V · 2012
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Randomized smoothing for stochastic optimization
Duchi, J. C., Bartlett, P. L. & Wainwright, M. J · 2012
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Efficient measurement of quantum gate error by interleaved randomized benchmarking
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On the importance of initialization and momentum in deep learning
Sutskever, I., Martens, J., Dahl, G. & Hinton, G · 2013
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Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Ghadimi, S. & Lan, G · 2013
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Stochastic first-and zeroth-order methods for nonconvex stochastic programming
Ghadimi, S. & Lan, G · 2013
Unsupervised machine learning on a hybrid quantum computer
Otterbach, J. et al · 2017
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Random gradient-free minimization of convex functions
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Quantum machine learning
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Training of quantum circuits on a hybrid quantum computer
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Observation of topological phenomena in a programmable lattice of 1,800 qubits
King, A. D. et al · 2018
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Self-verifying variational quantum simulation of the lattice schwinger model
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Optimal quantum control using randomized benchmarking
Kelly, J. et al · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. & Ba, J · 2014
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Equilibrated adaptive learning rates for non-convex optimization
Dauphin, Y., De Vries, H. & Bengio, Y · 2015
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Optimal rates for zero-order convex optimization: The power of two function evaluations
Duchi, J. C., Jordan, M. I., Wainwright, M. J. & Wibisono, A · 2015
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Scalable quantum simulation of molecular energies
O’Malley, P. J. et al · 2016
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Demonstration of a small programmable quantum computer with atomic qubits
Debnath, S. et al · 2016
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Kokail, C. et al · 2018
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Characterizing quantum supremacy in near-term devices
Boixo, S. et al · 2018
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Quantum computing in the nisq era and beyond
Preskill, J · 2018
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Validating quantum computers using randomized model circuits
Cross, A. W., Bishop, L. S., Sheldon, S., Nation, P. D. & Gambetta, J. M · 2018
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A blueprint for demonstrating quantum supremacy with superconducting qubits
Neill, C. et al · 2018
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Coherence properties of the 0- π \pi qubit
Groszkowski, P. et al · 2018
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Quantum chemistry in the age of quantum computing
Cao, Y. et al · 2018
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Quantum generative adversarial learning in a superconducting quantum circuit
Hu, L. et al · 2019
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Quantum convolutional neural networks
Cong, I., Choi, S. & Lukin, M. D · 2019
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Benchmarking an 11-qubit quantum computer
Wright, K. et al · 2019
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On the convergence of adam and beyond
Reddi, S. J., Kale, S. & Kumar, S · 2019
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Experimental realization of self-guided quantum process tomography
Hou, Z. et al · 2019
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Control and coherence time enhancement of the 0– π \pi qubit
Di Paolo, A., Grimsmo, A. L., Groszkowski, P., Koch, J. & Blais, A · 2019
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