TL;DR
A novel Quantum Alternating Operator Ansatz for the Graph Edit Distance Problem (QAOAz-GED) with the block-ring mixer and hot-start strategy is proposed, which achieves competitive performance compared with QAOA and QAOAz in terms of hamming error and solution accuracy, and reduces the mean hamming error in several tested settings.
Efficient end-to-end entanglement distribution in quantum information networks requires routing under limited resources and fidelity constraints. We study entanglement routing as a fidelity-constrained unsplittable multicommodity flow problem that maximizes the number of admitted requests. As a proof of concept, we int...
A hybrid quantum-classical column-and-constraint generation (QCCG) framework for solving two-stage adaptive robust optimization problems with binary first-stage decisions and linear recourse under polyhedral uncertainty and introduces a bound-adjustment mechanism that yields valid lower and upper bounds and provides a...
Feasibility-preserving mixer Hamiltonians offer an attractive alternative to penalty encodings for constrained quantum optimization, yet how enforcing constraints alters the state space reachable by the Quantum Approximate Optimization Algorithm (QAOA) remains largely unexplored. We address this question within the con...
A Transformer-based intrinsic optimization framework for QAOA, in which the optimizer itself is learned and embedded directly into the hybrid quantum-classical loop, demonstrating that Transformer-based intrinsic optimization can provide a structured and transferable mechanism for improving the classical component of h...
This work studies quantum random access optimization (QRAO), a special case of the Pauli correlation encoding (PCE) framework that assigns up to three binary variables to the Pauli observables of each qubit, with the packing choices determining the compressed Hamiltonian to be optimized.
We introduce quantum tilted walks, a quantum algorithmic framework for solving exact combinatorial optimization problems. The framework applies an average of powers of a tilted Hamiltonian that biases the discriminant matrix of a base Markov chain (mixer) with the objective function. Our starting point is quantum short...
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