BloGDiT introduces blocked Gibbs-style denoising in diffusion transformers to enable large targeted edits for constraint satisfaction and optimization, matching or exceeding prior methods on Sudoku, graph coloring, MIS, and MaxCut.
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A tutorial on formulating and using qubo models
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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.
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.
Introduces Λ-lr-QAOA and piecewise-ramp QAOA that promote penalty schedules to variational parameters and use a feasibility-driven loss on budget-constrained MWIS satellite planning instances.
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.
This paper presents a new open-source distributed QAOA simulator for QUBO problems that includes variable allocation across QPUs, runtime optimizations, a Streamlit GUI, and demonstrations of consistent results with monolithic QAOA on benchmarks and a power unit commitment application.
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.
Empirical tests show QUBO-SA and QAOA-inspired schedulers lose feasibility beyond 10-15 tasks while MILP, CP-SAT, GA and HEFT remain robust on the same instances.
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.
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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.