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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A tutorial on formulating and using qubo models
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Reformulating bi-level MINLP transport vulnerability analysis into QUBO form allows D-Wave quantum annealing to solve disruption scenarios on networks up to 6018 links in minutes, one to two orders of magnitude faster than classical metaheuristics.
Pauli Correlation Encoding with a trained problem-aware decoder achieves 75-100% near-optimal recovery on mRNA QUBO instances up to 152 variables and matches or exceeds simulator performance on IBM Heron processors for 694-745 variable cases.
Deep Boltzmann Quantum States with natural-gradient optimization and annealing-like training match exact or best-known solutions for large infinite-range Ising spin glasses and solve job shop scheduling instances.
A hybrid quantum framework decomposes CVRP into bounded-width knapsack subproblems, trains a reinforcement learning controller for Lagrangian multipliers, and uses a contextual bandit to adapt quantum hardware execution, yielding improved routing quality on standard test instances.
Constraint-aware initialization and hybrid XY-X mixer in QAOA for VRP yield lower average energies and higher feasible-solution ratios than standard QAOA across ideal, finite-shot, and noisy simulations.
A dual-valued phase shifter in linear optics creates variational cost landscapes with fewer local minima and outperforms prior linear-optical variational algorithms by mitigating barren plateaus.
PDQUBO is a new performance-driven QUBO method for feature selection in recommender systems that incorporates counterfactual performance impacts of features and pairs, is model-agnostic, and outperforms prior quantum and some classical baselines on CTR tasks.
A compact QUBO encoding derived via ILP reduces logical variables by thousands in AES, MD5, SHA1 and SHA256, with over 8x reduction for AES-256.
ESOP-based e-CNF encoding for quantum SAT oracles yields lower qubit counts, T-gate complexity, and circuit depth than standard CNF.
A hybrid classical-plus-quantum-inspired framework for cross-region renewable energy forecasting matches top baselines within 1% accuracy and separates calm versus stormy conditions with a 15-fold higher Fisher discriminant ratio than a tuned radial basis kernel.
Neural networks transform initial embeddings into feasible unit disk configurations for QUBO problems on Rydberg qubits and outperform the Gurobi solver in experiments.
A modified autoencoder with a custom embedding loss learns spatial mappings to solve the constrained unit disk problem for qubit embedding on neutral-atom quantum processors and outperforms classical solvers under fixed computation time.
BBQ-mIS decomposes graph coloring into parallel maximum independent set instances on Rydberg quantum hardware combined with classical branch-and-bound to produce proper colorings with few colors.
A penalty-free, fully quantum algorithm is proposed for finding ground and excited states of many-body Hamiltonians.
A unified local light-shifts encoding maps QUBO instances of SAT variants, set packing, quadratic assignment, clustering, and protein folding onto Rydberg annealers and solves them via optimized quantum annealing.
Hybrid impact-guided quantum decomposition for QUBO traffic zone partitioning evaluated on IBM Quantum System One, showing improved convergence over classical refinement but not outperforming direct quantum solutions.
Hybrid quantum-classical solver using Benders decomposition and QUBO reduces crude oil scheduling costs by 73-80% versus metaheuristics on 15 test instances while matching commercial solver speed.
Iterative cutting-plane generation and arc preprocessing reduce TSP model size and yield performance gains on classical, direct quantum, and hybrid D-Wave solvers.
The paper reviews QUDO, T-QUDO and HOBO formulations, provides explicit encodings between them, discusses limitations, and gives examples for knapsack, TSP and games including N-Queens and Peg Solitaire.
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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Quantum Optimisation for Transport Vulnerability Identification
Reformulating bi-level MINLP transport vulnerability analysis into QUBO form allows D-Wave quantum annealing to solve disruption scenarios on networks up to 6018 links in minutes, one to two orders of magnitude faster than classical metaheuristics.
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Pauli Correlation Encoding for mRNA Secondary Structure Prediction: Problem-Aware Decoding for Dense-Constraint QUBOs
Pauli Correlation Encoding with a trained problem-aware decoder achieves 75-100% near-optimal recovery on mRNA QUBO instances up to 152 variables and matches or exceeds simulator performance on IBM Heron processors for 694-745 variable cases.
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Solving Classical and Quantum Spin Glasses with Deep Boltzmann Quantum States
Deep Boltzmann Quantum States with natural-gradient optimization and annealing-like training match exact or best-known solutions for large infinite-range Ising spin glasses and solve job shop scheduling instances.
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Qubit-Scalable CVRP via Lagrangian Knapsack Decomposition and Noise-Aware Quantum Execution
A hybrid quantum framework decomposes CVRP into bounded-width knapsack subproblems, trains a reinforcement learning controller for Lagrangian multipliers, and uses a contextual bandit to adapt quantum hardware execution, yielding improved routing quality on standard test instances.
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Improving Feasibility in Quantum Approximate Optimization Algorithm for Vehicle Routing via Constraint-Aware Initialization and Hybrid XY-X Mixing
Constraint-aware initialization and hybrid XY-X mixer in QAOA for VRP yield lower average energies and higher feasible-solution ratios than standard QAOA across ideal, finite-shot, and noisy simulations.
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Mitigating the barren plateau problem in linear optics
A dual-valued phase shifter in linear optics creates variational cost landscapes with fewer local minima and outperforms prior linear-optical variational algorithms by mitigating barren plateaus.
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Performance-Driven QUBO for Recommender Systems on Quantum Annealers
PDQUBO is a new performance-driven QUBO method for feature selection in recommender systems that incorporates counterfactual performance impacts of features and pairs, is model-agnostic, and outperforms prior quantum and some classical baselines on CTR tasks.
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A compact QUBO encoding of computational logic formulae demonstrated on cryptography constructions
A compact QUBO encoding derived via ILP reduces logical variables by thousands in AES, MD5, SHA1 and SHA256, with over 8x reduction for AES-256.
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Performance Gains in Quantum SAT Solvers Using ESOP Encoding
ESOP-based e-CNF encoding for quantum SAT oracles yields lower qubit counts, T-gate complexity, and circuit depth than standard CNF.
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A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting
A hybrid classical-plus-quantum-inspired framework for cross-region renewable energy forecasting matches top baselines within 1% accuracy and separates calm versus stormy conditions with a 15-fold higher Fisher discriminant ratio than a tuned radial basis kernel.
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Neural-powered unit disk graph embedding: qubits connectivity for some QUBO problems
Neural networks transform initial embeddings into feasible unit disk configurations for QUBO problems on Rydberg qubits and outperform the Gurobi solver in experiments.
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Neural optimization for quantum architectures: graph embedding problems with Distance Encoder Networks
A modified autoencoder with a custom embedding loss learns spatial mappings to solve the constrained unit disk problem for qubit embedding on neutral-atom quantum processors and outperforms classical solvers under fixed computation time.
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BBQ-mIS: a parallel quantum algorithm for graph coloring problems
BBQ-mIS decomposes graph coloring into parallel maximum independent set instances on Rydberg quantum hardware combined with classical branch-and-bound to produce proper colorings with few colors.
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A penalty-free quantum algorithm to find energy eigenstates
A penalty-free, fully quantum algorithm is proposed for finding ground and excited states of many-body Hamiltonians.
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A Unified Local Light-shifts Encoding For Solving Optimization Problems on a Rydberg Annealer
A unified local light-shifts encoding maps QUBO instances of SAT variants, set packing, quadratic assignment, clustering, and protein folding onto Rydberg annealers and solves them via optimized quantum annealing.
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Impact-Driven Quantum Decomposition for Traffic Zone Partitioning: A Hybrid Gate-Model Framework
Hybrid impact-guided quantum decomposition for QUBO traffic zone partitioning evaluated on IBM Quantum System One, showing improved convergence over classical refinement but not outperforming direct quantum solutions.
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Solve Crude Oil Scheduling Problems by Using Quantum-Classical Hybrid Algorithms
Hybrid quantum-classical solver using Benders decomposition and QUBO reduces crude oil scheduling costs by 73-80% versus metaheuristics on 15 test instances while matching commercial solver speed.
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Cutting-plane methodology via quantum optimization for solving the Traveling Salesman Problem
Iterative cutting-plane generation and arc preprocessing reduce TSP model size and yield performance gains on classical, direct quantum, and hybrid D-Wave solvers.
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Introduction to QUDO, Tensor QUDO and HOBO formulations: Qudits, Equivalences, Knapsack Problem, Traveling Salesman Problem and Combinatorial Games
The paper reviews QUDO, T-QUDO and HOBO formulations, provides explicit encodings between them, discusses limitations, and gives examples for knapsack, TSP and games including N-Queens and Peg Solitaire.