A compact xor_1 gadget enforces exactly-one constraints on Rydberg arrays via fixed-detuning blockade, cutting detuning range by up to 99% and atom/connectivity overhead by up to 54% versus QUBO for gate assignment and N-queens.
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UNVERDICTED 5representative citing papers
Tensor network simulations act as effective surrogate models for training QAOA on large 2D lattices, overcoming limits of parameter transfer from small instances and remaining classically feasible with moderate bond dimensions.
DEAL boosts success rates in quantum combinatorial optimization by up to 14% over QAOA on superconducting qubits via direct parameter-to-angle mapping, entanglement ansatz, and ZNE.
The QuaST Decision Tree is a configurable modular system that automates recommendations for hybrid quantum algorithms, featuring a module for assessing variational algorithm feasibility through scalability analysis.
Classical solvers solve random Ising models on heavy-hex graphs efficiently, with Gurobi showing linear or weakly quadratic scaling up to 100k variables and simulated annealing showing exponential time-to-solution without cubic terms.
citing papers explorer
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Efficient mapping of multi-constraint satisfaction problems to Rydberg platforms
A compact xor_1 gadget enforces exactly-one constraints on Rydberg arrays via fixed-detuning blockade, cutting detuning range by up to 99% and atom/connectivity overhead by up to 54% versus QUBO for gate assignment and N-queens.
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Tensor network surrogate models for variational quantum computation
Tensor network simulations act as effective surrogate models for training QAOA on large 2D lattices, overcoming limits of parameter transfer from small instances and remaining classically feasible with moderate bond dimensions.
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Direct entanglement ansatz learning (DEAL) with ZNE on error-prone superconducting qubits
DEAL boosts success rates in quantum combinatorial optimization by up to 14% over QAOA on superconducting qubits via direct parameter-to-angle mapping, entanglement ansatz, and ZNE.
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The QuaST Decision Tree: Achieving Automation With Data-Based Recommendations
The QuaST Decision Tree is a configurable modular system that automates recommendations for hybrid quantum algorithms, featuring a module for assessing variational algorithm feasibility through scalability analysis.
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Classical Combinatorial Optimization Scaling for Random Ising Models on 2D Heavy-Hex Graphs
Classical solvers solve random Ising models on heavy-hex graphs efficiently, with Gurobi showing linear or weakly quadratic scaling up to 100k variables and simulated annealing showing exponential time-to-solution without cubic terms.