The trainability boundary for variational quantum objectives is the affine regime; non-affine amplification-capable losses can mitigate barren plateaus when using coarse-grained statistics at polynomial widths.
Quantum Hamiltonian- Based Models and the Variational Quantum Thermalizer Algorithm, October 2019
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A literature review of VQAs covering ansatz design, classical optimization, barren plateaus, error mitigation strategies, and theoretical adaptations for fault-tolerant quantum computing.
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Trainability Beyond Linearity in Variational Quantum Objectives
The trainability boundary for variational quantum objectives is the affine regime; non-affine amplification-capable losses can mitigate barren plateaus when using coarse-grained statistics at polynomial widths.
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A Review of Variational Quantum Algorithms: Insights into Fault-Tolerant Quantum Computing
A literature review of VQAs covering ansatz design, classical optimization, barren plateaus, error mitigation strategies, and theoretical adaptations for fault-tolerant quantum computing.