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Code Review & Quality
Code review, refactoring, debugging, maintainability, and engineering quality
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- Random Dimension Reduction Quantum LearningRandom dimension reduction methodology for learning symmetric properties of quantum states. Black-box procedure that replaces dimension with maximum rank in sample complexity. Use when learning symmetric quantum properties, estimating state distances/fidelities, or reducing quantum tomography overhead.Votes: 0GitHub stars: 3
- Qutrit Entropy EstimationVon Neumann entropy estimation in multi-qutrit quantum systems via variational quantum algorithms and classical neural networks. Use when estimating quantum entropy for d-level systems (qudits), selecting VQA ansatze for entropy estimation, or benchmarking quantum vs classical approaches for quantum information metrics. Covers SU(3)-inspired hardware-efficient ansatze, parameter sweep methodology, and CNN-based density matrix entropy estimation.Votes: 0GitHub stars: 3
- Quantum Syndrome Adaptive DecodingAdaptive syndrome processing for quantum error correction decoding. Dynamically adjusts decoder parameters based on syndrome patterns and noise characteristics to improve logical qubit fidelity.Votes: 0GitHub stars: 3
- Quantum Spectral PdeQuantum Spectral Framework for solving PDEs using Quantum Block Encoding (QBE) with quantum reversible arithmetic. Exploits filter structure in Fourier space to solve second-order linear PDEs. Extends to quantum group Fourier transforms, wavelet-based analysis, and equivariant quantum neural networks (EQNNs). Use when: quantum PDE solvers, quantum spectral methods, QBE for differential equations, quantum Fourier analysis for PDEs, EQNN applications, arXiv:2604.25825.Votes: 0GitHub stars: 3
- Quantum Rl Process SynthesisQuantum-enhanced reinforcement learning methodology for process synthesis problems. Formalizes process design as MDP and introduces quantum RL algorithms with state encoding to decouple qubit requirements from problem size. Covers quantum DQN, state encoding, and classical-quantum benchmarking patterns.Votes: 0GitHub stars: 3
- Quantum Program LintingLLM-powered static analysis and linting for quantum programs. Use when: (1) analyzing quantum circuits for correctness and optimization opportunities, (2) detecting anti-patterns in quantum code (Qiskit, Cirq, Pennylane), (3) improving quantum program quality through automated review, (4) validating quantum algorithms before execution on hardware. Covers LLM-based linting rules, quantum circuit analysis, and best practices for quantum software engineering.Votes: 0GitHub stars: 3
- Quantum Program AnalysisLLM-powered analysis and quality assurance for quantum programs. Use when: (1) linting quantum circuits/programs beyond simple rule checks, (2) analyzing quantum algorithm correctness and optimization opportunities, (3) evaluating quantum program performance under noise models, (4) benchmarking quantum primitives under hardware-motivated noise, (5) distributed quantum algorithm compilation and analysis. Covers LLM-based quantum linting, FTPrimitiveBench noise benchmarking, and topological qua...Votes: 0GitHub stars: 3
- Quantum Pave ChemistryQuantumPave — hybrid quantum-classical workflow for computing additive binding energies using quantum-centric supercomputing. Demonstrates practical quantum chemistry application on real quantum processors by sampling dominant electronic configurations on a QPU and performing classical diagonalization on HPC resources.Votes: 0GitHub stars: 3
- Quantum Neuroscience Analysis量子神经科学跨学科分析方法。将量子计算方法应用于神经科学问题，包括量子神经网络(QNN)用于脑信号分析、量子图神经网络(QGNN)用于脑连接、量子算法优化神经动力学建模。激活关键词: quantum neuroscience, quantum neural network, quantum EEG, quantum brain, 量子神经科学, 量子脑科学, QNN neuroscience.Votes: 0GitHub stars: 3
- Quantum Neural Architecture SearchQuantum Neural Network Architecture Search (QNAS) skill for designing efficient quantum neural networks on NISQ hardware. Uses multi-objective optimization (NSGA-II) to balance accuracy, runtime efficiency, and circuit cutting overhead. Apply when designing quantum neural networks, optimizing hybrid quantum-classical architectures, or searching for Pareto-optimal quantum circuit configurations. Keywords: quantum neural network, QNN, quantum architecture search, variational quantum circuit, an...Votes: 0GitHub stars: 3
- Quantum Fingerprinting CommunicationExperimental demonstration of quantum advantage in communication complexity using quantum fingerprinting for Euclidean distance computation. Uses coherent state pulses in simultaneous message passing (SMP) model. Shows quantum advantage in transmitted information for input size 10^8 with amplitude modulation encoding and superconducting nanowire single-photon detectors. Activation: quantum fingerprinting, communication complexity, Euclidean distance, SMP model, coherent states, quantum advant...Votes: 0GitHub stars: 3
- Quantum Data Management PhysicsToolbox methodology for understanding the physics of quantum data management. Connects quantum device physical behavior to database problem structure and difficulty, evaluates quantum annealing for combinatorial optimization in data management. Use when analyzing quantum computing for database systems, evaluating quantum annealing for data management tasks, or studying physics-database problem mappings.Votes: 0GitHub stars: 3
- Quantum Data Centers EntanglementQuantum data center network design and entanglement distribution optimization. Analyze resource requirements for entanglement purification in multi-hop quantum networks.Votes: 0GitHub stars: 3
- Quantum Control Latent ManifoldEnd-to-end learning of quantum control on latent dynamical manifold using LSTM. Joint learning of system dynamics and control strategies in low-dimensional latent space, replacing iterative simulate-then-optimize paradigm. Activation: end-to-end quantum control, latent manifold learning, quantum control LSTM, adiabatic speedup, spin chain state transfer.Votes: 0GitHub stars: 3
- Physics Informed Quantum Error AttributionNeuro-fuzzy framework for quantum error attribution using physics-informed machine learning. Combines ANFIS with physics-grounded features (Bhattacharyya Veto, Data Processing Inequality) to distinguish software bugs from hardware noise in quantum processors. Validated on 156-qubit IBM Heron r2.Votes: 0GitHub stars: 3
- Noisy Group Neurons Synchronous ResettingNoisy Group Neurons (NGN) framework for high-performance spiking neural networks using population-level synchronous resetting and neural stochasticity. Combines NGN model with backpropagation learning based on mean-field dynamics to address spatiotemporal information loss and gradient mismatching in deep SNNs. Use when implementing or analyzing SNNs with stochastic resonance, synchronous resetting, or mean-field learning approaches.Votes: 0GitHub stars: 3
- Neural Inverse Design Srf CavityDeep neural network approaches for inverse design of superconducting radio-frequency (SRF) cavities and transmon qubits for bosonic quantum computation — mapping target device parameters to candidate geometries.Votes: 0GitHub stars: 3
- Neural Allocentric Intuitive Physics Prediction From Real Videos**arXiv ID:** 1809.03330 **Authors:** Zhihua Wang, Stefano Rosa, Yishu Miao, Zihang Lai, Linhai Xie, Andrew Markham, Niki Trigoni **Published:** 2018-09-07T10:33:56Z **Abstract:** Humans are able to make rich predictions about the future dynamics of physical objects from a glance. On the other hand, most existing computer vision approaches require strong assumptions about the underlying system, ad-hoc modeling, or annotated datasets, to carry out even simple predictions. To tackle this gap, w...Votes: 0GitHub stars: 3
- Mlref Module Reward Evolution FrameworkMLREF (Module Level Reward Evolution Framework) for efficient module reuse in reinforcement learning reward design via large language models. Use when designing reward functions for RL that need to evolve across iterations while preserving and reusing effective components.Votes: 0GitHub stars: 3
- Layerspecific Adaptive Learning Rates For Deep Networks**arXiv ID:** 1510.04609 **Authors:** Bharat Singh, Soham De, Yangmuzi Zhang, Thomas Goldstein, Gavin Taylor **Published:** 2015-10-15T16:31:46Z **Abstract:** The increasing complexity of deep learning architectures is resulting in training time requiring weeks or even months. This slow training is due in part to vanishing gradients, in which the gradients used by back-propagation are extremely large for weights connecting deep layers (layers near the output layer), and extremely small for sh...Votes: 0GitHub stars: 3
- Large Language Models And Emergence A Complex Systems Perspective**arXiv ID:** 2506.11135 **Authors:** David C. Krakauer, John W. Krakauer, Melanie Mitchell **Published:** 2025-06-10T19:31:26Z **Abstract:** Emergence is a concept in complexity science that describes how many-body systems manifest novel higher-level properties, properties that can be described by replacing high-dimensional mechanisms with lower-dimensional effective variables and theories. This is captured by the idea "more is different". Intelligence is a consummate emergent property manif...Votes: 0GitHub stars: 3
- Landscape Of Neural Architecture Search Across Sensors How Much Do They Differ**arXiv ID:** 2201.06321 **Authors:** Kalifou René Traoré, Andrés Camero, Xiao Xiang Zhu **Published:** 2022-01-17T10:14:39Z **Abstract:** With the rapid rise of neural architecture search, the ability to understand its complexity from the perspective of a search algorithm is desirable. Recently, Traoré et al. have proposed the framework of Fitness Landscape Footprint to help describe and compare neural architecture search problems. It attempts at describing why a search strategy might be suc...Votes: 0GitHub stars: 3
- Improving The Performance Of Piecewise Linear Separation Incremental Algorithms For Practical Hardware Implementations**arXiv ID:** 0712.3654 **Authors:** Alejandro Chinea Manrique De Lara, Juan Manuel Moreno, Arostegui Jordi Madrenas, Joan Cabestany **Published:** 2007-12-21T10:05:52Z **Abstract:** In this paper we shall review the common problems associated with Piecewise Linear Separation incremental algorithms. This kind of neural models yield poor performances when dealing with some classification problems, due to the evolving schemes used to construct the resulting networks. So as to avoid this undesir...Votes: 0GitHub stars: 3
- Im2win Memory Efficient Convolution On Simd Architectures**arXiv ID:** 2306.14320 **Authors:** Shuai Lu, Jun Chu, Xu T. Liu **Published:** 2023-06-25T19:21:10Z **Abstract:** Convolution is the most expensive operation among neural network operations, thus its performance is critical to the overall performance of neural networks. Commonly used convolution approaches, including general matrix multiplication (GEMM)-based convolution and direct convolution, rely on im2col for data transformation or do not use data transformation at all, respectively. H...Votes: 0GitHub stars: 3