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Optimizing parameterized quantum circuits is a key routine in using near-term quantum devices.
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Quantum Recommendation Systems
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Quantum Computing in the NISQ era and beyond
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A generative modeling approach for benchmarking and training shallow quantum circuits
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Pennylane: Automatic differentiation of hybrid quantum-classical computations
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Benedetti, M., Garcia-Pintos, D., Nam, Y. & Perdomo-Ortiz, A · 2018
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Optimization methods for large-scale machine learning
Bottou, L., Curtis, F. E. & Nocedal, J · 2018
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On sampling rates in simulation-based recursions
Pasupathy, R., Glynn, P., Ghosh, S. & Hashemi, F · 2018
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A progressive batching l-BFGS method for machine learning
Bollapragada, R., Nocedal, J., Mudigere, D., Shi, H.-J. & Tang, P. T. P · 2018
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Quantum circuit learning
Mitarai, K., Negoro, M., Kitagawa, M. & Fujii, K · 2018
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High dimensional bayesian optimization via additive models with overlapping groups
Rolland, P. T. Y., Scarlett, J., Bogunovic, I. & Cevher, V · 2018
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Bergholm, V. et al · 2020
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Noise resilience of variational quantum compiling
Sharma, K., Khatri, S., Cerezo, M. & Coles, P. J · 2020
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Avoiding local minima in variational quantum eigensolvers with the natural gradient optimizer
Wierichs, D., Gogolin, C. & Kastoryano, M · 2020
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Learning with optimized random features: Exponential speedup by quantum machine learning without sparsity and low-rank assumptions
Yamasaki, H., Subramanian, S., Sonoda, S. & Koashi, M · 2020
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Variational quantum algorithms
Cerezo, M. et al · 2021
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Hybrid quantum-classical algorithms and quantum error mitigation
Endo, S., Cai, Z., Benjamin, S. C. & Yuan, X · 2021
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Quantum approximate optimization of non-planar graph problems on a planar superconducting processor
Harrigan, M. P. et al · 2021
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Efficient and noise resilient measurements for quantum chemistry on near-term quantum computers
Huggins, W. J. et al · 2021
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Efficient estimation of pauli observables by derandomization
Huang, H.-Y., Kueng, R. & Preskill, J · 2021
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Optimizing quantum heuristics with meta-learning
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Structure optimization for parameterized quantum circuits
Ostaszewski, M., Grant, E. & Benedetti, M · 2021
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Meta-variational quantum eigensolver: Learning energy profiles of parameterized hamiltonians for quantum simulation
Cervera-Lierta, A., Kottmann, J. S. & Aspuru-Guzik, A · 2021
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Variational quantum algorithm with information sharing
Self, C. N. et al · 2021
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Optimal training of variational quantum algorithms without barren plateaus
Haug, T. & Kim, M. S · 2021
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Adaptive shot allocation for fast convergence in variational quantum algorithms
Gu, A., Lowe, A., Dub, P. A., Coles, P. J. & Arrasmith, A · 2021
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Low-Depth Gradient Measurements Can Improve Convergence in Variational Hybrid Quantum-Classical Algorithms
Harrow, A. W. & Napp, J. C · 2021
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Training variational quantum algorithms is np-hard
Bittel, L. & Kliesch, M · 2021
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eprint https://github.com/SheffieldML/GPy (2021)
GPy: Gaussian processes framework in python · 2021
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Cost function dependent barren plateaus in shallow parametrized quantum circuits
Cerezo, M., Sone, A., Volkoff, T., Cincio, L. & Coles, P. J · 2021
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Entanglement-induced barren plateaus
Ortiz Marrero, C., Kieferová, M. & Wiebe, N · 2021
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https://quantum-computing.ibm.com/ (2021)
IBM Quantum Experience · 2021
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https://github.com/Qiskit/qiskit-terra/tree/main/qiskit/test/mock/backends (2021)
IBM Quantum Backends · 2021
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Measurement cost of metric-aware variational quantum algorithms
van Straaten, B. & Koczor, B · 2021
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Yamasaki, H. & Sonoda, S · 2021
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Noisy intermediate-scale quantum algorithms
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Quantum analytic descent
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https://github.com/Qiskit (2022)
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https://qiskit.org/documentation/stubs/qiskit.providers.aer.noise.NoiseModel.html (2022)
IBM Quantum Noise Model · 2022
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