{"total":20,"items":[{"citing_arxiv_id":"2605.23138","ref_index":30,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Classical State Preparation for Variational Quantum Algorithms via Reinforcement Learning","primary_cat":"quant-ph","submitted_at":"2026-05-22T01:24:54+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":7.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20163","ref_index":9,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Pauli Correlation Encoding for mRNA Secondary Structure Prediction: Problem-Aware Decoding for Dense-Constraint QUBOs","primary_cat":"quant-ph","submitted_at":"2026-05-19T17:49:58+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.16202","ref_index":23,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Performance Gains in Quantum SAT Solvers Using ESOP Encoding","primary_cat":"quant-ph","submitted_at":"2026-05-15T17:17:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"ESOP-based e-CNF encoding for quantum SAT oracles yields lower qubit counts, T-gate complexity, and circuit depth than standard CNF.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.15899","ref_index":83,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Solving Classical and Quantum Spin Glasses with Deep Boltzmann Quantum States","primary_cat":"cond-mat.dis-nn","submitted_at":"2026-05-15T12:30:07+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"lable variational manifold (see also Fig. 1 in Ref [82]). In contrast, an evolution on a restricted manifold may even improve results in some cases [80]. In principle, variational simulations of QA can be achieved by solving real-time TDVP Eqs. (3) for a large annealing timeτ, as theoretically characterized [82] and 5 numerically tested within a Matrix Product State [83] manifold. However, the numerical integration of TDVP equations is computationally demanding for ANNs and would require small time steps, making it challenging for large system sizes and long annealing timesτ. Variational Quantum Annealing (VQA) [45] is a dif- ferent strategy that relies on iterativelyoptimizingthe variational ansatz to approximate the instantaneous adi-"},{"citing_arxiv_id":"2605.09032","ref_index":46,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting","primary_cat":"cs.CL","submitted_at":"2026-05-09T16:16:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.07627","ref_index":22,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"A Unified Local Light-shifts Encoding For Solving Optimization Problems on a Rydberg Annealer","primary_cat":"quant-ph","submitted_at":"2026-05-08T11:56:12+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"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.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"exclusion, through the coefficientsc ij and the penaltiesP 1, P2 respectively. For a chain of lengthL,N=L(L−1)/2 binary variables are required to define a spin Hamiltonian; hence, a linear parameterpis mapped to a pair (i, j) to facilitate the construction of the Hamiltonian. From Eq. 14 and Eq. 2, Vpq =P 2 (ifp, q∈ C), V pq = 0 (otherwise) ∆p =−(P 1 −c p)− X q:(p,q)∈C P2 2 − 1 2 X p̸=q Vpq.(22) After mapping the problems to the corresponding target Rydberg Hamiltonians, they are solved by temporally varying the laser parameters to reach the specific choice of detuning values at the end of the protocol, while minimizing the energy of the full system. However, there is a caveat to this scheme due to the geometry restrictions of the physical system."},{"citing_arxiv_id":"2605.04736","ref_index":4,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Neural-powered unit disk graph embedding: qubits connectivity for some QUBO problems","primary_cat":"quant-ph","submitted_at":"2026-05-06T10:34:52+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Neural networks transform initial embeddings into feasible unit disk configurations for QUBO problems on Rydberg qubits and outperform the Gurobi solver in experiments.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.03565","ref_index":16,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Neural optimization for quantum architectures: graph embedding problems with Distance Encoder 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quantum hardware combined with classical branch-and-bound to produce proper colorings with few colors.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.01127","ref_index":6,"ref_count":2,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Impact-Driven Quantum Decomposition for Traffic Zone Partitioning: A Hybrid Gate-Model Framework","primary_cat":"quant-ph","submitted_at":"2026-05-01T21:54:13+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.26459","ref_index":3,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Solve Crude Oil Scheduling Problems by Using Quantum-Classical Hybrid Algorithms","primary_cat":"quant-ph","submitted_at":"2026-04-29T09:15:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"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.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.22194","ref_index":43,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Qubit-Scalable CVRP via Lagrangian Knapsack Decomposition and Noise-Aware Quantum 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cutting-plane generation and arc preprocessing reduce TSP model size and yield performance gains on classical, direct quantum, and hybrid D-Wave solvers.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.07218","ref_index":8,"ref_count":1,"confidence":0.9,"is_internal_anchor":false,"paper_title":"Improving Feasibility in Quantum Approximate Optimization Algorithm for Vehicle Routing via Constraint-Aware Initialization and Hybrid XY-X Mixing","primary_cat":"cs.ET","submitted_at":"2026-04-08T15:39:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"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 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QUBO encoding derived via ILP reduces logical variables by thousands in AES, MD5, SHA1 and SHA256, with over 8x reduction for AES-256.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}