Variational quantum algorithms

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[5]  Patrick J. Coles,et al.  A semi-agnostic ansatz with variable structure for quantum machine learning , 2021, ArXiv.

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[28]  Nathan Wiebe,et al.  Entanglement Induced Barren Plateaus , 2020, PRX Quantum.

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[38]  Raul Garcia-Patron,et al.  Limitations of optimization algorithms on noisy quantum devices , 2020, Nature Physics.

[39]  Patrick J. Coles,et al.  Correlation-Informed Permutation of Qubits for Reducing Ansatz Depth in the Variational Quantum Eigensolver , 2020, PRX Quantum.

[40]  Patrick J. Coles,et al.  Variational Hamiltonian Diagonalization for Dynamical Quantum Simulation , 2020, 2009.02559.

[41]  S. Benjamin,et al.  Quantum analytic descent , 2020, Physical Review Research.

[42]  Luca Dellantonio,et al.  Simulating 2D Effects in Lattice Gauge Theories on a Quantum Computer , 2020, PRX Quantum.

[43]  Masoud Mohseni,et al.  Low-Depth Mechanisms for Quantum Optimization , 2020, PRX Quantum.

[44]  Maria Schuld,et al.  Effect of data encoding on the expressive power of variational quantum-machine-learning models , 2020, Physical Review A.

[45]  Patrick J. Coles,et al.  Higher order derivatives of quantum neural networks with barren plateaus , 2020, Quantum Science and Technology.

[46]  Patrick J. Coles,et al.  Impact of Barren Plateaus on the Hessian and Higher Order Derivatives. , 2020 .

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[49]  Yvette de Sereville,et al.  Exploring entanglement and optimization within the Hamiltonian Variational Ansatz , 2020, PRX Quantum.

[50]  Patrick Huembeli,et al.  Characterizing the loss landscape of variational quantum circuits , 2020, Quantum Science and Technology.

[51]  A. Blais,et al.  Quantum-optimal-control-inspired ansatz for variational quantum algorithms , 2020, 2008.01098.

[52]  Patrick J. Coles,et al.  Noise-induced barren plateaus in variational quantum algorithms , 2020, Nature Communications.

[53]  Yuya O. Nakagawa,et al.  Deep Variational Quantum Eigensolver: a divide-and-conquer method for solving a larger problem with smaller size quantum computers , 2020, 2007.10917.

[54]  Pedro C. S. Costa,et al.  Compilation of Fault-Tolerant Quantum Heuristics for Combinatorial Optimization , 2020, 2007.07391.

[55]  Patrick J. Coles,et al.  Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets , 2020, Physical review letters.

[56]  V. Ulyantsev,et al.  MoG-VQE: Multiobjective genetic variational quantum eigensolver , 2020, 2007.04424.

[57]  E. Kashefi,et al.  The Born supremacy: quantum advantage and training of an Ising Born machine , 2020, npj Quantum Information.

[58]  Qi Zhao,et al.  Quantum Simulation with Hybrid Tensor Networks. , 2020, Physical review letters.

[59]  Zhenyu Cai,et al.  Multi-exponential error extrapolation and combining error mitigation techniques for NISQ applications , 2020, npj Quantum Information.

[60]  Patrick J. Coles,et al.  Machine Learning of Noise-Resilient Quantum Circuits , 2020, PRX Quantum.

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[70]  S. Benjamin,et al.  Learning-Based Quantum Error Mitigation , 2020, PRX Quantum.

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[73]  George Siopsis,et al.  Quantum computation of an interacting fermionic model , 2020, Quantum Science and Technology.

[74]  Earl T. Campbell,et al.  Efficient quantum measurement of Pauli operators in the presence of finite sampling error , 2020, Quantum.

[75]  Patrick J. Coles,et al.  Operator Sampling for Shot-frugal Optimization in Variational Algorithms , 2020, 2004.06252.

[76]  D. Bacon,et al.  Quantum approximate optimization of non-planar graph problems on a planar superconducting processor , 2020, Nature Physics.

[77]  Tyler Y Takeshita,et al.  Hartree-Fock on a superconducting qubit quantum computer , 2020, Science.

[78]  Patrick J. Coles,et al.  Variational quantum state eigensolver , 2020, npj Quantum Information.

[79]  A. Green,et al.  Parallel quantum simulation of large systems on small NISQ computers , 2020, npj Quantum Information.

[80]  David Von Dollen,et al.  TensorFlow Quantum: A Software Framework for Quantum Machine Learning , 2020, ArXiv.

[81]  Masoud Mohseni,et al.  Layerwise learning for quantum neural networks , 2020, Quantum Machine Intelligence.

[82]  Stuart Hadfield,et al.  Characterizing local noise in QAOA circuits , 2020, IOP SciNotes.

[83]  R. Kueng,et al.  Predicting many properties of a quantum system from very few measurements , 2020, Nature Physics.

[84]  J. Latorre,et al.  Scaling of variational quantum circuit depth for condensed matter systems , 2020, Quantum.

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[86]  Xiao Yuan,et al.  Mitigating Realistic Noise in Practical Noisy Intermediate-Scale Quantum Devices , 2020, Physical Review Applied.

[87]  S. Lloyd,et al.  Quantum embeddings for machine learning , 2020, 2001.03622.

[88]  Akira Sone,et al.  Cost-Function-Dependent Barren Plateaus in Shallow Quantum Neural Networks , 2020, ArXiv.

[89]  Lei Wang,et al.  Solving quantum statistical mechanics with variational autoregressive networks and quantum circuits , 2019, Mach. Learn. Sci. Technol..

[90]  Jin-Guo Liu,et al.  Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design , 2019, Quantum.

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[92]  L. Banchi,et al.  Noise-Assisted Variational Hybrid Quantum-Classical Optimization , 2019, 1912.06744.

[93]  Chris Cade,et al.  Strategies for solving the Fermi-Hubbard model on near-term quantum computers , 2019, 1912.06007.

[94]  Keisuke Fujii,et al.  Experimental quantum kernel trick with nuclear spins in a solid , 2019, npj Quantum Information.

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[96]  Prasanna Balaprakash,et al.  Learning to Optimize Variational Quantum Circuits to Solve Combinatorial Problems , 2019, AAAI.

[97]  Harper R. Grimsley,et al.  qubit-ADAPT-VQE: An adaptive algorithm for constructing hardware-efficient ansatze on a quantum processor , 2019, 1911.10205.

[98]  Gabriel N. Perdue,et al.  Quantum computing for neutrino-nucleus scattering , 2019, Physical Review D.

[99]  Simon C. Benjamin,et al.  Variational Circuit Compiler for Quantum Error Correction , 2019, Physical Review Applied.

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[106]  Robert Koenig,et al.  Obstacles to State Preparation and Variational Optimization from Symmetry Protection. , 2019, 1910.08980.

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[108]  Stephen K. Gray,et al.  Noise-Resilient Quantum Dynamics Using Symmetry-Preserving Ansatzes , 2019, 1910.06284.

[109]  Patrick J. Coles,et al.  Variational fast forwarding for quantum simulation beyond the coherence time , 2019, 1910.04292.

[110]  Zhenyu Cai,et al.  Resource Estimation for Quantum Variational Simulations of the Hubbard Model , 2019, 1910.02719.

[111]  Jack Hidary,et al.  Quantum Hamiltonian-Based Models and the Variational Quantum Thermalizer Algorithm , 2019, ArXiv.

[112]  Maria Schuld,et al.  Stochastic gradient descent for hybrid quantum-classical optimization , 2019, Quantum.

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[116]  Patrick J. Coles,et al.  An Adaptive Optimizer for Measurement-Frugal Variational Algorithms , 2019, Quantum.

[117]  K. B. Whaley,et al.  A non-orthogonal variational quantum eigensolver , 2019, New Journal of Physics.

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[119]  P. Rebentrost,et al.  Near-term quantum algorithms for linear systems of equations with regression loss functions , 2019, New Journal of Physics.

[120]  Patrick J. Coles,et al.  Variational Quantum Linear Solver , 2019, Quantum.

[121]  Ying Li,et al.  Variational algorithms for linear algebra. , 2019, Science bulletin.

[122]  Jacob Biamonte,et al.  On the universality of the quantum approximate optimization algorithm , 2019, Quantum Information Processing.

[123]  Yuchun Wu,et al.  Effects of Quantum Noise on Quantum Approximate Optimization Algorithm , 2019, Chinese Physics Letters.

[124]  J. Stokes,et al.  Quantum Natural Gradient , 2019, Quantum.

[125]  Margaret Martonosi,et al.  $O(N^3)$ Measurement Cost for Variational Quantum Eigensolver on Molecular Hamiltonians , 2019, IEEE Transactions on Quantum Engineering.

[126]  Yuya O. Nakagawa,et al.  Variational quantum algorithm for nonequilibrium steady states , 2019, 1908.09836.

[127]  B'alint Koczor,et al.  Variational-state quantum metrology , 2019, New Journal of Physics.

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[131]  Kunal Sharma,et al.  Noise resilience of variational quantum compiling , 2019, New Journal of Physics.

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[133]  Stuart Hadfield,et al.  Optimizing quantum heuristics with meta-learning , 2019, Quantum Machine Intelligence.

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[136]  J. Joo,et al.  Variational quantum algorithms for nonlinear problems , 2019, Physical Review A.

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[138]  Michele Mosca,et al.  Pauli Partitioning with Respect to Gate Sets. , 2019, 1907.07859.

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[143]  Patrick J. Coles,et al.  Variational Quantum Fidelity Estimation , 2019, Quantum.

[144]  Peter D. Johnson,et al.  Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum‐Classical Algorithms , 2019, Advanced Quantum Technologies.

[145]  Ilya Safro,et al.  Multistart Methods for Quantum Approximate optimization , 2019, 2019 IEEE High Performance Extreme Computing Conference (HPEC).

[146]  M. B. Hastings,et al.  Classical and quantum bounded depth approximation algorithms , 2019, Quantum Inf. Comput..

[147]  L. DiCarlo,et al.  Calculating energy derivatives for quantum chemistry on a quantum computer , 2019, npj Quantum Information.

[148]  Diego Garc'ia-Mart'in,et al.  Quantum singular value decomposer , 2019, 1905.01353.

[149]  Nicholas J. Mayhall,et al.  Efficient symmetry-preserving state preparation circuits for the variational quantum eigensolver algorithm , 2019, npj Quantum Information.

[150]  S. Lloyd,et al.  Variational quantum unsampling on a quantum photonic processor , 2019, Nature Physics.

[151]  Ken M. Nakanishi,et al.  Subspace variational quantum simulator , 2019, Physical Review Research.

[152]  Peter L. McMahon,et al.  A Jacobi Diagonalization and Anderson Acceleration Algorithm For Variational Quantum Algorithm Parameter Optimization. , 2019, 1904.03206.

[153]  Elham Kashefi,et al.  The Born supremacy: quantum advantage and training of an Ising Born machine , 2019, npj Quantum Information.

[154]  Keisuke Fujii,et al.  Sequential minimal optimization for quantum-classical hybrid algorithms , 2019, Physical Review Research.

[155]  Ryan Babbush,et al.  Decoding quantum errors with subspace expansions , 2019, Nature Communications.

[156]  Edward Grant,et al.  An initialization strategy for addressing barren plateaus in parametrized quantum circuits , 2019, Quantum.

[157]  J. Biamonte.  Universal variational quantum computation , 2019, Physical Review A.

[158]  Terry Farrelly,et al.  Training deep quantum neural networks , 2019, Nature Communications.

[159]  Ryan Babbush,et al.  Increasing the Representation Accuracy of Quantum Simulations of Chemistry without Extra Quantum Resources , 2019, Physical Review X.

[160]  Lei Wang,et al.  Variational quantum eigensolver with fewer qubits , 2019, Physical Review Research.

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[162]  T. Martínez,et al.  Quantum Computation of Electronic Transitions Using a Variational Quantum Eigensolver. , 2019, Physical review letters.

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[166]  S. Lloyd.  Quantum approximate optimization is computationally universal , 2018, 1812.11075.

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[169]  Y. Li,et al.  Variational Quantum Simulation of General Processes. , 2018, Physical review letters.

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[212]  Maria Schuld,et al.  Quantum Machine Learning in Feature Hilbert Spaces. , 2018, Physical review letters.

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[234]  Isaac H. Kim.  Holographic quantum simulation , 2017, 1702.02093.

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