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- Qaoa Feasibility Penalty SchedulingFeasibility-driven QAOA with penalty scheduling. Introduces Λ-lr-QAOA (per-penalty linear-ramp schedules) and piecewise-ramp QAOA (two-segment piecewise schedules) for constrained optimization. Promotes penalty weights from external hyperparameters to internal variational parameters. Activation: feasibility-driven QAOA, penalty scheduling, constrained QAOA, Λ-lr-QAOA, piecewise-ramp QAOA, MWIS optimizationVotes: 0GitHub stars: 3
- Qaoa Cvar Portfolio BenchmarkHardware benchmarking methodology for comparing quantum portfolio optimization algorithms (HE-VQNN vs WS-QAOA) on NISQ devices, focusing on CVaR (Conditional Value at Risk) portfolio optimization and the expressibility-coherence trade-off.Votes: 0GitHub stars: 3
- Photon Heralded Quantum Error CharacterizationAnalytic perturbative framework for characterizing small Markovian errors in probabilistic, photon-heralded quantum operations between non-interacting emitters. Extends the Zero-Photon-Generation (ZPG) framework with closed-form perturbative solutions for process matrices and Pauli error weights up to leading order. Bridges physical imperfections to abstract Pauli noise models for fault-tolerant quantum computing. Use when: designing photon-heralded gates, characterizing quantum error channel...Votes: 0GitHub stars: 3
- Lie Algebra Quantum Control InterpolationLie algebra-based quantum optimal control interpolation methodology. Combines Lie group theory with feed-forward neural networks to generate quantum optimal control pulses for arbitrary unitary operations, bypassing explicit optimization at inference time. Demonstrated on superconducting qubits (2-4 qubits) and applied to Trotter propagators for neutrino collective flavor oscillations. Use when: quantum control pulse generation, scalable quantum simulation, Lie group control, neural network c...Votes: 0GitHub stars: 3
- Lean Quantum Formal VerificationAI-assisted formal verification methodology for quantum information theory using Lean 4 theorem proverVotes: 0GitHub stars: 3
- Iterative Tensor Network TransformationsNonlinear tensor train ops via iterative transforms.Votes: 0GitHub stars: 3
- Higher Order Quantum Optimization FinanceHigher-order binary optimization (HOBO) methodology for legally-constrained financial optimization problems. Applied to collateral allocation with CSA eligibility, margin requirements, and concentration limits.Votes: 0GitHub stars: 3
- Hardware Tailored Qec Resource EstimationHardware-tailored resource estimation methodology for magic-state distillation on silicon spin qubit platforms. Combines bottom-up noise modeling with top-down application requirements to evaluate physical-to-logical qubit overhead. Supports surface, color, and biased error-correcting codes. arXiv: 2605.28936Votes: 0GitHub stars: 3
- Fpqc Sac Low Snr Financial RlFPQC-SAC methodology — Parameterized Quantum Circuit (PQC) front-end for Soft Actor-Critic (SAC) in low-signal-to-noise-ratio financial reinforcement learning. Addresses Q-value overestimation and policy collapse in noisy financial markets through quantum feature representations that provide inductive bias.Votes: 0GitHub stars: 3
- Distributed Qaoa SimulatorDistributed Quantum Approximate Optimization Algorithm (DQAOA) simulator for QUBO problems across multiple QPUs. Supports monolithic and distributed QAOA execution modes with configurable QPU capacities, cross-QPU coupling handling, and runtime optimizations. Activation: distributed QAOA, DQAOA simulator, QUBO optimization, multi-QPU quantum, quantum unit commitmentVotes: 0GitHub stars: 3
- Cim Lwe Qubo CryptanalysisCIM-BDD methodology for LWE cryptanalysis via penalty-free QUBO reduction on Coherent Ising Machines. Use when: analyzing Learning With Errors (LWE) problem security, reducing lattice problems to QUBO for quantum annealing/Ising machines, performing penalty-free mapping of cryptanalytic problems, designing hybrid quantum-classical cryptanalysis workflows, evaluating post-quantum cryptography parameter security. Core insight: algebraic elimination of the secret + nearest-plane decomposition yi...Votes: 0GitHub stars: 3
- Certified Higher Order Qaoa CollateralCR-HO-QAOA framework for certified higher-order quantum collateral allocation with CSA-aware constraints and feasible-subspace mixers. Uses higher-order binary models for margin requirements, concentration limits, and substitution structure, with CP-SAT certification. Use when: collateral optimization, margin-aware quantum optimization, CSA constraints, higher-order QAOA with certification, quantum-classical hybrid solver.Votes: 0GitHub stars: 3
- Green Wearable Computing PhysicsTowards Green Wearable Computing: A Physics-Aware Spiking Neural Network for Energy-Efficient IMU-ba... Activation: 物理感知, spiking, physics-aware, snnVotes: 0GitHub stars: 3
- Exploratory Experience Shapes The Geometry Of PredDerived from arXiv:2605.27929 - Exploratory Experience Shapes the Geometry of Predictive RepresentationsVotes: 0GitHub stars: 3
- Devotg Temporal Graph ConnectomicsDevoTG: Temporal Graph Neural Networks for modeling C. elegans developmental connectomics, capturing dynamic wiring through continuous-time and discrete-time dynamic graphs.Votes: 0GitHub stars: 3
- Continuous Spiking Graph Neural Networks**arXiv ID:** 2404.01897 **Authors:** Nan Yin, Mengzhu Wan, Li Shen, Hitesh Laxmichand Patel, Baopu Li, Bin Gu, Huan Xiong **Published:** 2024-04-02T12:36:40Z **Abstract:** Continuous graph neural networks (CGNNs) have garnered significant attention due to their ability to generalize existing discrete graph neural networks (GNNs) by introducing continuous dynamics. They typically draw inspiration from diffusion-based methods to introduce a novel propagation scheme, which is analyzed using ord...Votes: 0GitHub stars: 3
- A Logical Reconception Of Neural Networks Hamiltonian Bitwise Partwhole Architecture**arXiv ID:** 2602.04911 **Authors:** E Bowen, R Granger, A Rodriguez **Published:** 2026-02-04T01:16:37Z **Abstract:** We introduce a simple initial working system in which relations (such as part-whole) are directly represented via an architecture with operating and learning rules fundamentally distinct from standard artificial neural network methods. Arbitrary data are straightforwardly encoded as graphs whose edges correspond to codes from a small fixed primitive set of elemental pairwise...Votes: 0GitHub stars: 3
- Vacuum Entanglement ExtractionVacuum entanglement extraction protocols from quantum field theory. Covers local operation protocols for harvesting entanglement from vacuum states and applications to distributed quantum computing and quantum networking. Use when: vacuum entanglement, entanglement harvesting, quantum field theory communication, distributed quantum computing, quantum networking, vacuum resource, QFT entanglement, local operations entanglement.Votes: 0GitHub stars: 3
- Topological Quantum ComputingDesign quantum computing systems using topological structures. Apply 3-manifold topology, surface topology, and knotted quantum states for information protection. Activation: topological quantum, topology quantum computing, 拓扑量子计算, 量子拓扑, topological qubit, anyon braiding.Votes: 0GitHub stars: 3
- Thoughtseeds Dual Process MeditationA computational phenomenology framework for modeling focused-attention meditation using dual-process active inference and hierarchical Markov-blanket architecture. Use when modeling meditation states, attentional dynamics, or cognitive phenomenology with latent mental content representations.Votes: 0GitHub stars: 3
- Tensor Network Readout MitigationTensor network (MPO) framework for characterizing and mitigating correlated readout errors in quantum processors. Use when modeling readout noise beyond uncorrelated approximations, estimating nonlocal observables with correlated measurement errors, or integrating readout mitigation with quantum error correction decoders. Triggers: tensor network readout, MPO readout error, correlated measurement error, readout mitigation, matrix product operator calibration, classical shadows readout, noise-...Votes: 0GitHub stars: 3
- Tensor Network Neurological PredictorTensor Network Feature Engineering methodology for multi-class neurological disorder prediction from MRI data. Uses tensor network decompositions to extract high-dimensional features from sparse medical imaging. Activation: tensor network MRI, neurological disorder prediction, tensor feature engineering, multi-class brain disorder, MRI tensor decomposition.Votes: 0GitHub stars: 3
- Tensor Network Frontend Quantum MedicalTensor-network frontend methodology for quantum-enhanced federated medical diagnosis. Combines MPS, TTN, and MERA tensor networks for client-side compression with quantum-enhanced processor (QEP) refinement for medical image classification.Votes: 0GitHub stars: 3
- Supertrust Foundational Alignment Mutual Trust Must Replace Permanent Control For Safe Superintelligence**arXiv ID:** 2407.20208 **Authors:** James M. Mazzu **Published:** 2024-07-29T17:39:52Z **Abstract:** It's widely expected that humanity will someday create AI systems vastly more intelligent than us, leading to the unsolved alignment problem of "how to control superintelligence." However, this commonly expressed problem is not only self-contradictory and likely unsolvable, but current strategies to ensure permanent control effectively guarantee that superintelligent AI will distrust humanit...Votes: 0GitHub stars: 3