Hamming Weight Operators and an adaptive QAOA variant confine evolution to feasible states by construction, delivering faster convergence and roughly half the gate count versus penalty methods on finance and physics tasks.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
quant-ph 3years
2026 3representative citing papers
Fourier-based LCU decomposes diagonal and non-diagonal unitaries into hardware-friendly forms for QAOA-style optimization, trading circuit depth for sampling overhead with performance guarantees.
Shot-based quantum encoding embeds data as a classical probability mixture over basis states, making the quantum layer a linear map on probabilities—structurally a classical MLP.
citing papers explorer
-
Constraint-Aware Quantum Optimization via Hamming Weight Operators
Hamming Weight Operators and an adaptive QAOA variant confine evolution to feasible states by construction, delivering faster convergence and roughly half the gate count versus penalty methods on finance and physics tasks.
-
Efficient Fourier-Based Linear Combination of Unitaries and Applications in Quantum Optimization
Fourier-based LCU decomposes diagonal and non-diagonal unitaries into hardware-friendly forms for QAOA-style optimization, trading circuit depth for sampling overhead with performance guarantees.
-
Shot-based quantum encoding: a data-loading paradigm for quantum neural networks
Shot-based quantum encoding embeds data as a classical probability mixture over basis states, making the quantum layer a linear map on probabilities—structurally a classical MLP.