CRiSP uses neural-guided MCTS and curriculum learning to insert Clifford prefixes before parameterized rotations in VQAs, yielding mean 3.17x and max 45x gains in energy accuracy on 22-qubit QAOA benchmarks versus prior Clifford initializers.
Rieffel, Davide Ven- turelli, and Rupak Biswas
9 Pith papers cite this work. Polarity classification is still indexing.
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Truncated-binary encoding approximates high-cardinality CFN problems as low-degree HUBO Hamiltonians with an L^∞ error bound, conditions preserving the global minimum, and a smoothness-based criterion for choosing the cutoff.
Exhaustively parametrised feasibility-respecting quantum circuits can reach every feasible solution to problems like TSP with certainty using fixed parameters by leveraging group actions and generating sequences.
A qubit-efficient colored-permutation encoding for CVRP enables Constraint-Enhanced QAOA to recover verified optimal solutions on benchmarks without additional capacity qubits.
CE-QAOA with finite layers achieves dimension-free success probability bounds q0 ≥ x/(1+x) via Fejér filtering under a wrapped phase-separation condition.
Closed-form tight penalty coefficients for two QUBO reformulations of max-k-cut that depend on the weighted degrees of graph vertices.
Hybrid Iterative-QAOA warm starts improve shipment delivery by up to 12% and cut drive distance by 6% on real logistics data when fed to a classical solver.
Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.
Graph contraction reduces TSP instances to smaller sub-problems solvable by quantum annealers, shown via Path Integral Monte Carlo simulation and D-Wave hardware.
citing papers explorer
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Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning
CRiSP uses neural-guided MCTS and curriculum learning to insert Clifford prefixes before parameterized rotations in VQAs, yielding mean 3.17x and max 45x gains in energy accuracy on 22-qubit QAOA benchmarks versus prior Clifford initializers.
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Truncated-Binary Encoding: Spectral Degree Reduction of Combinatorial Optimization Problems for Quantum Hardware
Truncated-binary encoding approximates high-cardinality CFN problems as low-degree HUBO Hamiltonians with an L^∞ error bound, conditions preserving the global minimum, and a smoothness-based criterion for choosing the cutoff.
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Exhaustive and feasible parametrisation with applications to the travelling salesperson problem
Exhaustively parametrised feasibility-respecting quantum circuits can reach every feasible solution to problems like TSP with certainty using fixed parameters by leveraging group actions and generating sequences.
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Optimal, Qubit-Efficient Quantum Vehicle Routing via Colored-Permutations
A qubit-efficient colored-permutation encoding for CVRP enables Constraint-Enhanced QAOA to recover verified optimal solutions on benchmarks without additional capacity qubits.
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Finite-Depth, Finite-Shot Guarantees for Constrained Quantum Optimization via Fej\'er Filtering
CE-QAOA with finite layers achieves dimension-free success probability bounds q0 ≥ x/(1+x) via Fejér filtering under a wrapped phase-separation condition.
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Characterizing QUBO Reformulations of the Max-k-Cut Problem for Quantum Computing
Closed-form tight penalty coefficients for two QUBO reformulations of max-k-cut that depend on the weighted degrees of graph vertices.
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Hybrid Quantum-Classical Optimization Workflows for the Shipment Selection Problem
Hybrid Iterative-QAOA warm starts improve shipment delivery by up to 12% and cut drive distance by 6% on real logistics data when fed to a classical solver.
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Going off Pattern? QAOA Parameter Heuristics and Potentials of Parsimony
Numerical experiments on QAOA show optimal parameters often break expected patterns, performance becomes less parameter-sensitive with depth, and component-wise iterative fixing performs competitively or better at low depth.
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A Hybrid Classical-Quantum Annealing Algorithm for the TSP
Graph contraction reduces TSP instances to smaller sub-problems solvable by quantum annealers, shown via Path Integral Monte Carlo simulation and D-Wave hardware.