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.
Improving Variational Quantum Optimization using CVaR.Quantum, 4:256, April 2020
4 Pith papers cite this work. Polarity classification is still indexing.
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CCD-QAOA incorporates counterdiabatic terms into the QAOA ansatz and shows higher approximation ratios than standard XY-mixer, Grover-mixer, and penalty QAOA for portfolio problems with budget and risk constraints.
Adaptive CVaR sampling, two-stage optimization, and a heavy-hex-native deep-chain ansatz layout improve VQA results on dynamic portfolio optimization, with the new layout best on IBM hardware, though no quantum advantage over classical solvers is found.
Numerical benchmarks identify a minimum problem size where variational quantum circuits for Max-Cut outperform sampling on average, with quantified separation from greedy methods and instance-level performance correlations.
citing papers explorer
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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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Constrained Counterdiabatic Quantum Approximate Optimization Algorithm for Portfolio Optimization
CCD-QAOA incorporates counterdiabatic terms into the QAOA ansatz and shows higher approximation ratios than standard XY-mixer, Grover-mixer, and penalty QAOA for portfolio problems with budget and risk constraints.
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VQA for Dynamic Portfolio Optimization: Sampling Strategies, Optimizer Scheduling, and Hardware-Aware Ansatz Design
Adaptive CVaR sampling, two-stage optimization, and a heavy-hex-native deep-chain ansatz layout improve VQA results on dynamic portfolio optimization, with the new layout best on IBM hardware, though no quantum advantage over classical solvers is found.
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Benchmarking Variational Quantum Algorithms for Combinatorial Optimization in Practice
Numerical benchmarks identify a minimum problem size where variational quantum circuits for Max-Cut outperform sampling on average, with quantified separation from greedy methods and instance-level performance correlations.