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- Object Representations As Fixed Points Training Iterative Refinement Algorithms With Implicit Differentiation**arXiv ID:** 2207.00787 **Authors:** Michael Chang, Thomas L. Griffiths, Sergey Levine **Published:** 2022-07-02T10:00:35Z **Abstract:** Iterative refinement -- start with a random guess, then iteratively improve the guess -- is a useful paradigm for representation learning because it offers a way to break symmetries among equally plausible explanations for the data. This property enables the application of such methods to infer representations of sets of entities, such as objects in physica...Votes: 0GitHub stars: 3
- Multitask Photonic Reservoir Computing Wavelength Division Multiplexing For Parallel Computing With A Silicon Microring Resonator**arXiv ID:** 2407.21189 **Authors:** Bernard J. Giron Castro, Christophe Peucheret, Darko Zibar, Francesco Da Ros **Published:** 2024-07-30T20:54:07Z **Abstract:** Nowadays, as the ever-increasing demand for more powerful computing resources continues, alternative advanced computing paradigms are under extensive investigation. Significant effort has been made to deviate from conventional Von Neumann architectures. In-memory computing has emerged in the field of electronics as a possible solu...Votes: 0GitHub stars: 3
- Metageneralization For Multiparty Privacy Learning To Identify Anomaly Multimedia Traffic In Graynet**arXiv ID:** 2201.03027 **Authors:** Satoshi Nato, Yiqiang Sheng **Published:** 2022-01-09T14:51:34Z **Abstract:** Identifying anomaly multimedia traffic in cyberspace is a big challenge in distributed service systems, multiple generation networks and future internet of everything. This letter explores meta-generalization for a multiparty privacy learning model in graynet to improve the performance of anomaly multimedia traffic identification. The multiparty privacy learning model in graynet...Votes: 0GitHub stars: 3
- Kanerva Extending The Kanerva Machine With Differentiable Locally Block Allocated Latent Memory**arXiv ID:** 2103.03905 **Authors:** Jason Ramapuram, Yan Wu, Alexandros Kalousis **Published:** 2021-02-20T18:40:40Z **Abstract:** Episodic and semantic memory are critical components of the human memory model. The theory of complementary learning systems (McClelland et al., 1995) suggests that the compressed representation produced by a serial event (episodic memory) is later restructured to build a more generalized form of reusable knowledge (semantic memory). In this work we develop a ne...Votes: 0GitHub stars: 3
- A Novel Metaheuristic Optimization Algorithm Inspired By The Spread Of Viruses**arXiv ID:** 2006.06282 **Authors:** Zhixi Li, Vincent Tam **Published:** 2020-06-11T09:35:28Z **Abstract:** According to the no-free-lunch theorem, there is no single meta-heuristic algorithm that can optimally solve all optimization problems. This motivates many researchers to continuously develop new optimization algorithms. In this paper, a novel nature-inspired meta-heuristic optimization algorithm called virus spread optimization (VSO) is proposed. VSO loosely mimics the spread of viru...Votes: 0GitHub stars: 3
- Research Paper Pattern ExtractorExtract reusable research skill patterns from knowledge graph paper analysis. Uses PageRank, Louvain, and vector search to identify important papers and research clusters, then distills patterns into new skills. Activation: extract research pattern, research skill extractor, 研究模式提炼, paper pattern analysis.Votes: 0GitHub stars: 3
- Qldpc Full Extractor ConstructionFull extractor construction for logical processing in Hypergraph Product (HGP) codes — surgery systems for measuring arbitrary logical Pauli operators on QLDPC code blocks. Enables Pauli-based computation without compilation overhead. Use when: QLDPC code processing, logical operator measurement, hypergraph product codes, fault-tolerant quantum memory, quantum error correction, Pauli-based computation.Votes: 0GitHub stars: 3
- Model Based Diffusion Policy OptimizationModel-Based Diffusion Policy Optimization (MBDPO) methodology for scaling world-model reinforcement learning. Unifies search and policy optimization through diffusion policy representations addressing structural misalignment. Use for world-model RL, diffusion-based policy learning, offline pretraining, model-based RL scaling.Votes: 0GitHub stars: 3
- D2evo Dual Difficulty Self EvolutionD²Evo methodology — Dual Difficulty-Aware Self-Evolution for data-efficient reinforcement learning in LLM reasoning. Addresses Effective Data Scarcity and Dynamic Difficulty Shifts by automatically selecting medium-difficulty samples via dual difficulty scoring (performance-based + entropy-based). Use when: data-efficient RL post-training for LLMs, curriculum-free self-evolution, difficulty-aware sample selection, GRPO data optimization, RL training data management. Activation: D2Evo, dual di...Votes: 0GitHub stars: 3
- Tsp Quantum PreprocessingPreprocessing methodology for combinatorial optimization (TSP and beyond) that reduces problem size by restricting candidate arcs to lowest-cost neighbors. Applicable to both classical solvers and quantum optimization frameworks (QAOA, quantum annealing).Votes: 0GitHub stars: 3
- Tensor Network Linear AlgebraTensor network dimension reduction methodology for provably solving exponential-scale linear algebra problems including trace estimation and eigenvalue approximation at dimension up to 2^200.Votes: 0GitHub stars: 3
- Scalable On Hardware Qnn TrainingScalable on-hardware training methodology for Quantum Neural Networks (QNNs) using linear-cost gradient estimation via block encoding + Hadamard test. Solves the quadratic parameter-shift bottleneck, enabling clinical data applications like missing patient data imputation. Use when: QNN training on quantum hardware, clinical quantum ML, gradient estimation optimization, block encoding for gradients, quantum parameter shift alternatives, healthcare quantum computing.Votes: 0GitHub stars: 3
- Scalable Mp Quantum GnnScalable Message-Passing Quantum Graph Neural Networks methodology — building quantum GNNs with message passing, permutation equivariance, and Weisfeiler-Leman hierarchy placement. Enables pre-training on small graphs and cost-effective readout as graphs grow.Votes: 0GitHub stars: 3
- Safety Critical Quantum ControlSafety-critical control framework for quantum systems with formal guarantees. Combines control barrier functions with quantum dynamics to ensure quantum states remain within safe operational regions during control operations.Votes: 0GitHub stars: 3
- Real Time Qec System StackReal-time Quantum Error Correction (QEC) system stack architecture and engineering methodology. Six-layer reference architecture from syndrome acquisition to logical operations with latency budget modeling.Votes: 0GitHub stars: 3
- Qubo Hybrid Optimization SchedulingQUBO-based hybrid quantum-classical optimization methodology for coordinated scheduling problems. Formulates time-dependent operational constraints as quadratic unconstrained binary optimization, then solves with quantum annealing validated by classical simulation. Use when formulating scheduling/assignment problems for quantum optimization, building hybrid quantum-classical solvers, or modeling time-dependent constraints in QUBO form.Votes: 0GitHub stars: 3
- Quantum Witness ExpansionWitness Expansion framework for unified quantum resource detection. Constructs nonlinear criteria for detecting quantum resources (coherence, entanglement, magic, non-Gaussianity) associated with a group of free unitaries. Applies to both pure and mixed states via polynomial functions estimable from multiple state copies. Use when: detecting quantum resources, constructing entanglement witnesses, benchmarking quantum devices, analyzing stabilizer entropy, fermionic non-Gaussianity, or mixed-s...Votes: 0GitHub stars: 3
- Quantum Verifiable Blind ComputingComparative analysis and design framework for verifiable blind quantum computing (VBQC) client architectures. Covers emission-based, measurement-based, and rotation-based client designs with single-server, single-client protocols using measurement-based quantum computation. Evaluates security proofs, protocol execution rates, error behavior, and hardware cost trade-offs. Activation: verifiable blind quantum computing, VBQC client architecture, blind quantum computation, quantum client design,...Votes: 0GitHub stars: 3
- Quantum Trace Maps 3d3D Quantum Trace Map methodology — homomorphism from skein modules of triangulated 3-manifolds to quantum gluing modules, unifying constructions by Garoufalidis-Yu and Panitch-Park.Votes: 0GitHub stars: 3
- Quantum Systems EngineeringSystems engineering patterns for quantum computing systems. Covers hybrid quantum-classical architecture design, distributed quantum computing, robust quantum control systems, and quantum system modeling. Use when designing quantum computing systems, analyzing distributed quantum architectures, or implementing robust control for quantum hardware. Also covers: dissipative thermal state preparation with error bounds, analytical two-pulse molecular qubit control, adaptive feedback for bistable q...Votes: 0GitHub stars: 3
- Quantum Systems Engineering ApproachSystems engineering methodology for quantum processor design. Integrates hardware architecture, control software, error correction, and resource management into a unified engineering framework. Covers scalability, reliability, and performance optimization for quantum systems.Votes: 0GitHub stars: 3
- Quantum Stochastic Walk PortfolioQuantum Stochastic Walk (QSW) optimizer for portfolio optimization — embeds assets in weighted covariance graph, derives weights from walk stationary distribution, achieving 15% Sharpe improvement and 90% turnover reduction vs classical mean-variance.Votes: 0GitHub stars: 3
- Quantum Software Engineering PracticeQuantum software engineering practical patterns for FPGA-based quantum control systems with AI integration. Covers hardware-aware compilation, real-time feedback control, and fault-tolerant quantum computing workflows. Activation: quantum software engineering, FPGA quantum control, quantum AI integration, quantum compilation, real-time quantum feedback.Votes: 0GitHub stars: 3
- Quantum Rl Scuc QsampleHybrid quantum-classical reinforcement learning for Security-Constrained Unit Commitment (SCUC). Uses Bernoulli hybrid soft actor-critic (HSAC) with quantum-sampled feature augmentation for economic dispatch in power systems. Activation: quantum RL SCUC, unit commitment optimization, quantum-sampled features, hybrid soft actor-critic, economic dispatch quantum, power grid optimization quantumVotes: 0GitHub stars: 3