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Many fundamental properties of a quantum system are captured by its Hamiltonian and ground state.
Reducibility among combinatorial problems
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Optimal brain damage
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Hardware-efficient variational quantum eigensolver for small molecules and quantum magnets
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Classification with quantum neural networks on near term processors
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
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Snip: Single-shot network pruning based on connection sensitivity
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Barren plateaus in quantum neural network training landscapes
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Quantum computing in the nisq era and beyond
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Absence of barren plateaus in quantum convolutional neural networks
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The dilemma of quantum neural networks
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Effect of data encoding on the expressive power of variational quantum-machine-learning models
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Exploiting symmetry reduces the cost of training qaoa
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Classical symmetries and the quantum approximate optimization algorithm
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Efficient symmetry-preserving state preparation circuits for the variational quantum eigensolver algorithm
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Learning unitaries by gradient descent
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On barren plateaus and cost function locality in variational quantum algorithms
AV Uvarov and Jacob D Biamonte · 2021
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Large gradients via correlation in random parameterized quantum circuits
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Noise-induced barren plateaus in variational quantum algorithms
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Han Zheng, Zimu Li, Junyu Liu, Sergii Strelchuk, and Risi Kondor · 2021
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Beyond barren plateaus: Quantum variational algorithms are swamped with traps
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Noisy intermediate-scale quantum algorithms
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Restrictions on realizable unitary operations imposed by symmetry and locality
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A convergence theory for over-parameterized variational quantum eigensolvers
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On the Super-exponential Quantum Speedup of Equivariant Quantum Machine Learning Algorithms with SU( d d ) Symmetry
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