{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/quantum-machine-learning/papers/2","list_of":"/task/quantum-machine-learning","task":"Quantum Machine Learning","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":7,"rows_per_page":100,"rows":[101,200],"of":699,"counts":{"archive_papers_tagged":699,"with_a_code_link":140,"where_syntology_ran_a_sample":17,"not_listed_spam_title":0,"listed":699,"listed_where_code_ran":17,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":15,"every_run_a_failure_of_syntologys_instrument":2,"listed_with_a_run_with_no_instrument_failure":15,"listed_every_run_a_failure_of_syntologys_instrument":2,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/quantum-machine-learning","prev":"/task/quantum-machine-learning","next":"/task/quantum-machine-learning/papers/3","papers":[{"url":"/paper/scalable-quantum-neural-networks-for","slug":"scalable-quantum-neural-networks-for","title":"Scalable Quantum Neural Networks for Classification","date":"2022-08-04","arxiv_id":"2208.07719","repositories_listed":1,"syntology":null},{"url":"/paper/learning-distributed-quantum-state","slug":"learning-distributed-quantum-state","title":"Quantum Machine Learning for Distributed Quantum Protocols with Local Operations and Noisy Classical Communications","date":"2022-07-22","arxiv_id":"2207.11354","repositories_listed":1,"syntology":null},{"url":"/paper/verifying-fairness-in-quantum-machine","slug":"verifying-fairness-in-quantum-machine","title":"Verifying Fairness in Quantum Machine Learning","date":"2022-07-22","arxiv_id":"2207.11173","repositories_listed":1,"syntology":null},{"url":"/paper/variational-quantum-approximate-support","slug":"variational-quantum-approximate-support","title":"Variational Quantum Approximate Support Vector Machine with Inference Transfer","date":"2022-06-29","arxiv_id":"2206.14507","repositories_listed":1,"syntology":null},{"url":"/paper/design-and-implementation-of-a-quantum-kernel","slug":"design-and-implementation-of-a-quantum-kernel","title":"Design and Implementation of a Quantum Kernel for Natural Language Processing","date":"2022-05-13","arxiv_id":"2205.06409","repositories_listed":1,"syntology":null},{"url":"/paper/speeding-up-learning-quantum-states-through","slug":"speeding-up-learning-quantum-states-through","title":"Speeding up Learning Quantum States through Group Equivariant Convolutional Quantum Ansätze","date":"2021-12-14","arxiv_id":"2112.07611","repositories_listed":1,"syntology":null},{"url":"/paper/discriminating-quantum-states-with-quantum","slug":"discriminating-quantum-states-with-quantum","title":"Discriminating Quantum States with Quantum Machine Learning","date":"2021-12-01","arxiv_id":"2112.00313","repositories_listed":1,"syntology":null},{"url":"/paper/importance-of-kernel-bandwidth-in-quantum","slug":"importance-of-kernel-bandwidth-in-quantum","title":"Importance of Kernel Bandwidth in Quantum Machine Learning","date":"2021-11-09","arxiv_id":"2111.05451","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-machine-learning-beyond-kernel","slug":"quantum-machine-learning-beyond-kernel","title":"Quantum machine learning beyond kernel methods","date":"2021-10-25","arxiv_id":"2110.13162","repositories_listed":1,"syntology":null},{"url":"/paper/entangled-datasets-for-quantum-machine","slug":"entangled-datasets-for-quantum-machine","title":"Entangled Datasets for Quantum Machine Learning","date":"2021-09-08","arxiv_id":"2109.03400","repositories_listed":1,"syntology":null},{"url":"/paper/optimizing-quantum-variational-circuits-with","slug":"optimizing-quantum-variational-circuits-with","title":"Optimizing Quantum Variational Circuits with Deep Reinforcement Learning","date":"2021-09-07","arxiv_id":"2109.03188","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/optimizing-quantum-variational-circuits-with#ran","syntology_url":"https://syntology.ai/paper/2109.03188","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.03188"}},"official":{"repos":["lockwo/rl_qvc_opt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/large-scale-quantum-machine-learning","slug":"large-scale-quantum-machine-learning","title":"Quantum machine learning of large datasets using randomized measurements","date":"2021-08-02","arxiv_id":"2108.01039","repositories_listed":1,"syntology":null},{"url":"/paper/a-quantum-algorithm-for-training-wide-and","slug":"a-quantum-algorithm-for-training-wide-and","title":"A quantum algorithm for training wide and deep classical neural networks","date":"2021-07-19","arxiv_id":"2107.09200","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-data-compression-and-quantum-cross","slug":"quantum-data-compression-and-quantum-cross","title":"Quantum Data Compression and Quantum Cross Entropy","date":"2021-06-25","arxiv_id":"2106.13823","repositories_listed":1,"syntology":null},{"url":"/paper/trainable-discrete-feature-embeddings-for","slug":"trainable-discrete-feature-embeddings-for","title":"Trainable Discrete Feature Embeddings for Variational Quantum Classifier","date":"2021-06-17","arxiv_id":"2106.09415","repositories_listed":1,"syntology":null},{"url":"/paper/variational-quanvolutional-neural-networks","slug":"variational-quanvolutional-neural-networks","title":"Variational Quanvolutional Neural Networks with enhanced image encoding","date":"2021-06-14","arxiv_id":"2106.07327","repositories_listed":1,"syntology":null},{"url":"/paper/the-dilemma-of-quantum-neural-networks","slug":"the-dilemma-of-quantum-neural-networks","title":"The dilemma of quantum neural networks","date":"2021-06-09","arxiv_id":"2106.04975","repositories_listed":1,"syntology":null},{"url":"/paper/the-inductive-bias-of-quantum-kernels","slug":"the-inductive-bias-of-quantum-kernels","title":"The Inductive Bias of Quantum Kernels","date":"2021-06-07","arxiv_id":"2106.03747","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-perceptron-revisited-computational","slug":"quantum-perceptron-revisited-computational","title":"Quantum Perceptron Revisited: Computational-Statistical Tradeoffs","date":"2021-06-04","arxiv_id":"2106.02496","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-federated-learning-with-quantum-data","slug":"quantum-federated-learning-with-quantum-data","title":"Quantum Federated Learning with Quantum Data","date":"2021-05-30","arxiv_id":"2106.00005","repositories_listed":1,"syntology":null},{"url":"/paper/optimal-training-of-variational-quantum","slug":"optimal-training-of-variational-quantum","title":"Optimal training of variational quantum algorithms without barren plateaus","date":"2021-04-29","arxiv_id":"2104.14543","repositories_listed":1,"syntology":null},{"url":"/paper/a-semi-agnostic-ansatz-with-variable","slug":"a-semi-agnostic-ansatz-with-variable","title":"A semi-agnostic ansatz with variable structure for quantum machine learning","date":"2021-03-11","arxiv_id":"2103.06712","repositories_listed":1,"syntology":null},{"url":"/paper/advantages-and-bottlenecks-of-quantum-machine","slug":"advantages-and-bottlenecks-of-quantum-machine","title":"Advantages and Bottlenecks of Quantum Machine Learning for Remote Sensing","date":"2021-01-26","arxiv_id":"2101.10657","repositories_listed":1,"syntology":null},{"url":"/paper/information-theoretic-bounds-on-quantum","slug":"information-theoretic-bounds-on-quantum","title":"Information-theoretic bounds on quantum advantage in machine learning","date":"2021-01-07","arxiv_id":"2101.02464","repositories_listed":1,"syntology":null},{"url":"/paper/vsql-variational-shadow-quantum-learning-for","slug":"vsql-variational-shadow-quantum-learning-for","title":"VSQL: Variational Shadow Quantum Learning for Classification","date":"2020-12-15","arxiv_id":"2012.08288","repositories_listed":1,"syntology":null},{"url":"/paper/power-of-data-in-quantum-machine-learning","slug":"power-of-data-in-quantum-machine-learning","title":"Power of data in quantum machine learning","date":"2020-11-03","arxiv_id":"2011.01938","repositories_listed":1,"syntology":null},{"url":"/paper/a-rigorous-and-robust-quantum-speed-up-in","slug":"a-rigorous-and-robust-quantum-speed-up-in","title":"A rigorous and robust quantum speed-up in supervised machine learning","date":"2020-10-05","arxiv_id":"2010.02174","repositories_listed":1,"syntology":null},{"url":"/paper/the-effect-of-data-encoding-on-the-expressive","slug":"the-effect-of-data-encoding-on-the-expressive","title":"The effect of data encoding on the expressive power of variational quantum machine learning models","date":"2020-08-19","arxiv_id":"2008.08605","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-one-class-classification-with-a","slug":"quantum-one-class-classification-with-a","title":"Quantum One-class Classification With a Distance-based Classifier","date":"2020-07-31","arxiv_id":"2007.16200","repositories_listed":1,"syntology":null},{"url":"/paper/recurrent-quantum-neural-networks","slug":"recurrent-quantum-neural-networks","title":"Recurrent Quantum Neural Networks","date":"2020-06-25","arxiv_id":"2006.14619","repositories_listed":1,"syntology":null},{"url":"/paper/variational-quantum-gibbs-state-preparation","slug":"variational-quantum-gibbs-state-preparation","title":"Variational quantum Gibbs state preparation with a truncated Taylor series","date":"2020-05-18","arxiv_id":"2005.08797","repositories_listed":1,"syntology":null},{"url":"/paper/eigen-component-analysis-a-quantum-theory","slug":"eigen-component-analysis-a-quantum-theory","title":"Eigen component analysis: A quantum theory incorporated machine learning technique to find linearly maximum separable components","date":"2020-03-23","arxiv_id":"2003.10199","repositories_listed":1,"syntology":null},{"url":"/paper/evaluation-of-parameterized-quantum-circuits","slug":"evaluation-of-parameterized-quantum-circuits","title":"Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability","date":"2020-03-22","arxiv_id":"2003.09887","repositories_listed":1,"syntology":null},{"url":"/paper/variational-quantum-circuits-for-quantum","slug":"variational-quantum-circuits-for-quantum","title":"Variational Quantum Circuits for Quantum State Tomography","date":"2019-12-16","arxiv_id":"1912.07286","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-wasserstein-generative-adversarial","slug":"quantum-wasserstein-generative-adversarial","title":"Quantum Wasserstein Generative Adversarial Networks","date":"2019-10-31","arxiv_id":"1911.00111","repositories_listed":1,"syntology":{"n":9,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/quantum-wasserstein-generative-adversarial#ran","syntology_url":"https://syntology.ai/paper/1911.00111","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.00111"}},"official":{"repos":["yiminghwang/qWGAN"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/a-framework-for-deep-energy-based","slug":"a-framework-for-deep-energy-based","title":"Quantum enhancements for deep reinforcement learning in large spaces","date":"2019-10-28","arxiv_id":"1910.12760","repositories_listed":1,"syntology":null},{"url":"/paper/quantum-adiabatic-machine-learning-with","slug":"quantum-adiabatic-machine-learning-with","title":"Quantum adiabatic machine learning with zooming","date":"2019-08-13","arxiv_id":"1908.04480","repositories_listed":1,"syntology":null},{"url":"/paper/expressive-power-of-tensor-network","slug":"expressive-power-of-tensor-network","title":"Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning","date":"2019-07-08","arxiv_id":"1907.03741","repositories_listed":1,"syntology":null},{"url":"/paper/variational-quantum-circuits-and-deep","slug":"variational-quantum-circuits-and-deep","title":"Variational Quantum Circuits for Deep Reinforcement Learning","date":"2019-06-30","arxiv_id":"1907.00397","repositories_listed":1,"syntology":null},{"url":"/paper/a-quantum-inspired-classical-algorithm-for","slug":"a-quantum-inspired-classical-algorithm-for","title":"A quantum-inspired classical algorithm for recommendation systems","date":"2018-07-10","arxiv_id":"1807.04271","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-quantum-inspired-classical-algorithm-for#ran","syntology_url":"https://syntology.ai/paper/1807.04271","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.04271"}},"official":null}},{"url":null,"slug":"stochastic-entanglement-configuration-for","title":"Stochastic Entanglement Configuration for Constructive Entanglement Topologies in Quantum Machine Learning with Application to Cardiac MRI","date":"2025-07-15","arxiv_id":"2507.11401","repositories_listed":0,"syntology":null},{"url":null,"slug":"entangled-threats-a-unified-kill-chain-model","title":"Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security","date":"2025-07-11","arxiv_id":"2507.08623","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-federated-learning-for-multimodal","title":"Quantum Federated Learning for Multimodal Data: A Modality-Agnostic Approach","date":"2025-07-10","arxiv_id":"2507.08217","repositories_listed":0,"syntology":null},{"url":null,"slug":"resq-a-novel-framework-to-implement-residual","title":"ResQ: A Novel Framework to Implement Residual Neural Networks on Analog Rydberg Atom Quantum Computers","date":"2025-06-26","arxiv_id":"2506.21537","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-qubit-efficient-hybrid-quantum-encoding","title":"A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning","date":"2025-06-24","arxiv_id":"2506.19275","repositories_listed":0,"syntology":null},{"url":null,"slug":"iterative-quantum-feature-maps","title":"Iterative Quantum Feature Maps","date":"2025-06-24","arxiv_id":"2506.19461","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-quantum-bsde-solver-for-high-dimensional","title":"On Quantum BSDE Solver for High-Dimensional Parabolic PDEs","date":"2025-06-17","arxiv_id":"2506.14612","repositories_listed":0,"syntology":null},{"url":null,"slug":"component-based-quantum-machine-learning","title":"Component Based Quantum Machine Learning Explainability","date":"2025-06-14","arxiv_id":"2506.12378","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-10275","title":"VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning","date":"2025-06-12","arxiv_id":"2506.10275","repositories_listed":0,"syntology":null},{"url":null,"slug":"2506-08749","title":"Superposed Parameterised Quantum Circuits","date":"2025-06-10","arxiv_id":"2506.08749","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-graph-transformer-for-nlp-sentiment","title":"Quantum Graph Transformer for NLP Sentiment Classification","date":"2025-06-09","arxiv_id":"2506.07937","repositories_listed":0,"syntology":null},{"url":null,"slug":"devanagari-digit-recognition-using-quantum","title":"Devanagari Digit Recognition using Quantum Machine Learning","date":"2025-06-08","arxiv_id":"2506.09069","repositories_listed":0,"syntology":null},{"url":null,"slug":"tqml-simulator-optimized-simulation-of","title":"TQml Simulator: Optimized Simulation of Quantum Machine Learning","date":"2025-06-05","arxiv_id":"2506.04891","repositories_listed":0,"syntology":null},{"url":null,"slug":"investigating-quantum-feature-maps-in-quantum","title":"Investigating Quantum Feature Maps in Quantum Support Vector Machines for Lung Cancer Classification","date":"2025-06-03","arxiv_id":"2506.03272","repositories_listed":0,"syntology":null},{"url":null,"slug":"optimization-strategies-for-variational","title":"Optimization Strategies for Variational Quantum Algorithms in Noisy Landscapes","date":"2025-06-02","arxiv_id":"2506.01715","repositories_listed":0,"syntology":null},{"url":null,"slug":"qgan-based-data-augmentation-for-hybrid","title":"QGAN-based data augmentation for hybrid quantum-classical neural networks","date":"2025-05-30","arxiv_id":"2505.24780","repositories_listed":0,"syntology":null},{"url":null,"slug":"supervised-quantum-machine-learning-a-future","title":"Supervised Quantum Machine Learning: A Future Outlook from Qubits to Enterprise Applications","date":"2025-05-30","arxiv_id":"2505.24765","repositories_listed":0,"syntology":null},{"url":null,"slug":"leveraging-diffusion-models-for-parameterized","title":"Leveraging Diffusion Models for Parameterized Quantum Circuit Generation","date":"2025-05-27","arxiv_id":"2505.20863","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-machine-learning-in-healthcare","title":"Quantum Machine Learning in Healthcare: Evaluating QNN and QSVM Models","date":"2025-05-27","arxiv_id":"2505.20804","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-performance-of-deep-quantum-data","title":"Predictive Performance of Deep Quantum Data Re-uploading Models","date":"2025-05-24","arxiv_id":"2505.20337","repositories_listed":0,"syntology":null},{"url":null,"slug":"experimental-robustness-benchmark-of-quantum","title":"Experimental robustness benchmark of quantum neural network on a superconducting quantum processor","date":"2025-05-22","arxiv_id":"2505.16714","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-data-encoding-methods-in-quantum","title":"Benchmarking data encoding methods in Quantum Machine Learning","date":"2025-05-20","arxiv_id":"2505.14295","repositories_listed":0,"syntology":null},{"url":null,"slug":"learning-to-program-quantum-measurements-for","title":"Learning to Program Quantum Measurements for Machine Learning","date":"2025-05-18","arxiv_id":"2505.13525","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-enhanced-parameter-efficient-learning","title":"Quantum-Enhanced Parameter-Efficient Learning for Typhoon Trajectory Forecasting","date":"2025-05-14","arxiv_id":"2505.09395","repositories_listed":0,"syntology":null},{"url":null,"slug":"addressing-the-current-challenges-of-quantum","title":"Addressing the Current Challenges of Quantum Machine Learning through Multi-Chip Ensembles","date":"2025-05-13","arxiv_id":"2505.08782","repositories_listed":0,"syntology":null},{"url":null,"slug":"differentiable-quantum-architecture-search-in-1","title":"Differentiable Quantum Architecture Search in Quantum-Enhanced Neural Network Parameter Generation","date":"2025-05-13","arxiv_id":"2505.09653","repositories_listed":0,"syntology":null},{"url":null,"slug":"hmae-self-supervised-few-shot-learning-for","title":"HMAE: Self-Supervised Few-Shot Learning for Quantum Spin Systems","date":"2025-05-06","arxiv_id":"2505.03140","repositories_listed":0,"syntology":null},{"url":null,"slug":"toward-practical-quantum-machine-learning-a","title":"Toward Practical Quantum Machine Learning: A Novel Hybrid Quantum LSTM for Fraud Detection","date":"2025-04-30","arxiv_id":"2505.00137","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-autoencoder-for-multivariate-time","title":"Quantum Autoencoder for Multivariate Time Series Anomaly Detection","date":"2025-04-24","arxiv_id":"2504.17548","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-machine-learning-framework-for-1","title":"Quantum machine learning framework for longitudinal biomedical studies","date":"2025-04-24","arxiv_id":"2504.18392","repositories_listed":0,"syntology":null},{"url":null,"slug":"introduction-to-quantum-machine-learning-and","title":"Introduction to Quantum Machine Learning and Quantum Architecture Search","date":"2025-04-21","arxiv_id":"2504.16131","repositories_listed":0,"syntology":null},{"url":null,"slug":"adaptive-non-local-observable-on-quantum","title":"Adaptive Non-local Observable on Quantum Neural Networks","date":"2025-04-18","arxiv_id":"2504.13414","repositories_listed":0,"syntology":null},{"url":null,"slug":"predictive-control-of-blast-furnace","title":"Predictive control of blast furnace temperature in steelmaking with hybrid depth-infused quantum neural networks","date":"2025-04-16","arxiv_id":"2504.12389","repositories_listed":0,"syntology":null},{"url":null,"slug":"deqompile-quantum-circuit-decompilation-using","title":"DeQompile: quantum circuit decompilation using genetic programming for explainable quantum architecture search","date":"2025-04-11","arxiv_id":"2504.08310","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-machine-learning-unveiling-trends","title":"Quantum Machine Learning: Unveiling Trends, Impacts through Bibliometric Analysis","date":"2025-04-10","arxiv_id":"2504.07726","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-geometric-aware-perspective-and-beyond","title":"A Geometric-Aware Perspective and Beyond: Hybrid Quantum-Classical Machine Learning Methods","date":"2025-04-08","arxiv_id":"2504.06328","repositories_listed":0,"syntology":null},{"url":null,"slug":"detecting-underdetermination-in-parameterized","title":"Detecting underdetermination in parameterized quantum circuits","date":"2025-04-04","arxiv_id":"2504.03315","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-deep-sets-and-sequences","title":"Quantum Deep Sets and Sequences","date":"2025-04-03","arxiv_id":"2504.02241","repositories_listed":0,"syntology":null},{"url":null,"slug":"hqcc-a-hybrid-quantum-classical-classifier","title":"HQCC: A Hybrid Quantum-Classical Classifier with Adaptive Structure","date":"2025-04-02","arxiv_id":"2504.02167","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-assisted-machine-learning-models-for","title":"Quantum-Assisted Machine Learning Models for Enhanced Weather Prediction","date":"2025-03-30","arxiv_id":"2503.23408","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-doeblin-coefficients-interpretations","title":"Quantum Doeblin Coefficients: Interpretations and Applications","date":"2025-03-28","arxiv_id":"2503.22823","repositories_listed":0,"syntology":null},{"url":null,"slug":"molecular-quantum-transformer","title":"Molecular Quantum Transformer","date":"2025-03-27","arxiv_id":"2503.21686","repositories_listed":0,"syntology":null},{"url":null,"slug":"multiple-embeddings-for-quantum-machine","title":"Multiple Embeddings for Quantum Machine Learning","date":"2025-03-27","arxiv_id":"2503.22758","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-complex-valued-self-attention-model","title":"Quantum Complex-Valued Self-Attention Model","date":"2025-03-24","arxiv_id":"2503.19002","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-quantum-perceptron-learning-via-quantum","title":"On Quantum Perceptron Learning via Quantum Search","date":"2025-03-21","arxiv_id":"2503.17308","repositories_listed":0,"syntology":null},{"url":null,"slug":"enqode-fast-amplitude-embedding-for-quantum","title":"EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data","date":"2025-03-18","arxiv_id":"2503.14473","repositories_listed":0,"syntology":null},{"url":null,"slug":"single-qudit-quantum-neural-networks-for","title":"Single-Qudit Quantum Neural Networks for Multiclass Classification","date":"2025-03-12","arxiv_id":"2503.09269","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-status-of-current-quantum-machine","title":"On the status of current quantum machine learning software","date":"2025-03-11","arxiv_id":"2503.08962","repositories_listed":0,"syntology":null},{"url":null,"slug":"quiet-sr-quantum-image-enhancement","title":"QUIET-SR: Quantum Image Enhancement Transformer for Single Image Super-Resolution","date":"2025-03-11","arxiv_id":"2503.08759","repositories_listed":0,"syntology":null},{"url":null,"slug":"distilling-knowledge-into-quantum-vision","title":"Distilling Knowledge into Quantum Vision Transformers for Biomedical Image Classification","date":"2025-03-10","arxiv_id":"2503.07294","repositories_listed":0,"syntology":null},{"url":null,"slug":"on-the-similarity-of-bandwidth-tuned-quantum","title":"On the similarity of bandwidth-tuned quantum kernels and classical kernels","date":"2025-03-07","arxiv_id":"2503.05602","repositories_listed":0,"syntology":null},{"url":null,"slug":"seismic-inversion-using-hybrid-quantum-neural","title":"Seismic inversion using hybrid quantum neural networks","date":"2025-03-06","arxiv_id":"2503.05009","repositories_listed":0,"syntology":null},{"url":null,"slug":"phishvqc-optimizing-phishing-url-detection","title":"PhishVQC: Optimizing Phishing URL Detection with Correlation Based Feature Selection and Variational Quantum Classifier","date":"2025-03-03","arxiv_id":"2503.01799","repositories_listed":0,"syntology":null},{"url":null,"slug":"qcs-adme-quantum-circuit-search-for-drug","title":"QCS-ADME: Quantum Circuit Search for Drug Property Prediction with Imbalanced Data and Regression Adaptation","date":"2025-03-02","arxiv_id":"2503.01927","repositories_listed":0,"syntology":null},{"url":null,"slug":"exploring-the-potential-of-qeegnet-for-cross","title":"Exploring the Potential of QEEGNet for Cross-Task and Cross-Dataset Electroencephalography Encoding with Quantum Machine Learning","date":"2025-02-28","arxiv_id":"2503.00080","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-machine-learning-in-precision","title":"Quantum Machine Learning in Precision Medicine and Drug Discovery -- A Game Changer for Tailored Treatments?","date":"2025-02-25","arxiv_id":"2502.18639","repositories_listed":0,"syntology":null},{"url":null,"slug":"expressive-equivalence-of-classical-and","title":"Expressive equivalence of classical and quantum restricted Boltzmann machines","date":"2025-02-24","arxiv_id":"2502.17562","repositories_listed":0,"syntology":null},{"url":null,"slug":"comparative-analysis-of-black-hole-mass","title":"Comparative Analysis of Black Hole Mass Estimation in Type-2 AGNs: Classical vs. Quantum Machine Learning and Deep Learning Approaches","date":"2025-02-21","arxiv_id":"2502.15297","repositories_listed":0,"syntology":null},{"url":null,"slug":"quantum-autoencoders-for-image-classification","title":"Quantum autoencoders for image classification","date":"2025-02-21","arxiv_id":"2502.15254","repositories_listed":0,"syntology":null},{"url":null,"slug":"benchmarking-medmnist-dataset-on-real-quantum","title":"Benchmarking MedMNIST dataset on real quantum hardware","date":"2025-02-18","arxiv_id":"2502.13056","repositories_listed":0,"syntology":null}],"record_sha256":"e08b96ef99eacbe439413a106c3816f2fa5b365ab635c0d142fa7b5178476da8","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}