{"total":76,"rows":[{"id":"2405.00781","title":"Barren Plateaus in Variational Quantum Computing","created":"2024-05-01","cited_by_count":133,"area":null},{"id":"2312.09121","title":"Does provable absence of barren plateaus imply classical simulability?","created":"2023-12-14","cited_by_count":80,"area":null},{"id":"2101.08448","title":"Noisy intermediate-scale quantum (NISQ) algorithms","created":"2021-01-21","cited_by_count":79,"area":null},{"id":"2306.13461","title":"Understanding quantum machine learning also requires rethinking generalization","created":"2023-06-23","cited_by_count":49,"area":null},{"id":"2103.16774","title":"Towards understanding the power of quantum kernels in the NISQ era","created":"2021-03-31","cited_by_count":43,"area":null},{"id":"2101.08354","title":"Enhancing Generative Models via Quantum Correlations","created":"2021-01-20","cited_by_count":41,"area":null},{"id":"2206.03066","title":"Recent Advances for Quantum Neural Networks in Generative Learning","created":"2022-06-07","cited_by_count":39,"area":null},{"id":"2305.02881","title":"Trainability barriers and opportunities in quantum generative modeling","created":"2023-05-04","cited_by_count":36,"area":null},{"id":"2305.20069","title":"A survey on the complexity of learning quantum states","created":"2023-05-31","cited_by_count":32,"area":null},{"id":"2310.05866","title":"Generative quantum machine learning via denoising diffusion probabilistic models","created":"2023-10-09","cited_by_count":30,"area":"diffusion"},{"id":"2401.10095","title":"Learning shallow quantum circuits","created":"2024-01-18","cited_by_count":20,"area":null},{"id":"2110.06933","title":"Style-based quantum generative adversarial networks for Monte Carlo events","created":"2021-10-13","cited_by_count":19,"area":null},{"id":"2103.06720","title":"Variational inference with a quantum computer","created":"2021-03-11","cited_by_count":18,"area":null},{"id":"2303.15626","title":"A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models","created":"2023-03-27","cited_by_count":16,"area":null},{"id":"2101.03037","title":"Learning quantum data with the quantum Earth Mover's distance","created":"2021-01-08","cited_by_count":13,"area":null},{"id":"2211.07056","title":"An Invitation to Distributed Quantum Neural Networks","created":"2022-11-14","cited_by_count":13,"area":null},{"id":"2309.10392","title":"Differentiable Quantum Architecture Search for Quantum Reinforcement Learning","created":"2023-09-19","cited_by_count":13,"area":null},{"id":"2401.11555","title":"VQC-Based Reinforcement Learning with Data Re-uploading: Performance and Trainability","created":"2024-01-21","cited_by_count":12,"area":"deeprl"},{"id":"2511.15969","title":"A Primer on Quantum Machine Learning","created":"2025-11-20","cited_by_count":12,"area":null},{"id":"2405.20237","title":"Training-efficient density quantum machine learning","created":"2024-05-30","cited_by_count":11,"area":null},{"id":"2509.09033","title":"Generative quantum advantage for classical and quantum problems","created":"2025-09-10","cited_by_count":11,"area":null},{"id":"2102.06697","title":"Matching Point Sets with Quantum Circuit Learning","created":"2021-02-12","cited_by_count":10,"area":null},{"id":"2108.03190","title":"Quantum Quantile Mechanics: Solving Stochastic Differential Equations for Generating Time-Series","created":"2021-08-06","cited_by_count":10,"area":null},{"id":"2303.13462","title":"Generalization of Quantum Machine Learning Models Using Quantum Fisher Information Metric","created":"2023-03-23","cited_by_count":10,"area":null},{"id":"2210.14936","title":"A super-polynomial quantum-classical separation for density modelling","created":"2022-10-26","cited_by_count":9,"area":null},{"id":"2305.05765","title":"On the average-case complexity of learning output distributions of quantum circuits","created":"2023-05-09","cited_by_count":9,"area":null},{"id":"2310.11891","title":"A Hyperparameter Study for Quantum Kernel Methods","created":"2023-10-18","cited_by_count":9,"area":null},{"id":"2401.07039","title":"Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble","created":"2024-01-13","cited_by_count":9,"area":"diffusion"},{"id":"2201.08770","title":"Generalization Metrics for Practical Quantum Advantage in Generative Models","created":"2022-01-21","cited_by_count":8,"area":null},{"id":"2109.04298","title":"Quantum Machine Learning for Finance","created":"2021-09-09","cited_by_count":7,"area":null},{"id":"2110.05517","title":"Learnability of the output distributions of local quantum circuits","created":"2021-10-11","cited_by_count":7,"area":null},{"id":"2409.07626","title":"Generalization Error Bound for Quantum Machine Learning in NISQ Era -- A Survey","created":"2024-09-11","cited_by_count":7,"area":null},{"id":"2308.16664","title":"What can we learn from quantum convolutional neural networks?","created":"2023-08-31","cited_by_count":6,"area":null},{"id":"2403.19758","title":"Quantum Natural Language Processing","created":"2024-03-28","cited_by_count":6,"area":null},{"id":"2205.14506","title":"Introducing Non-Linear Activations into Quantum Generative Models","created":"2022-05-28","cited_by_count":5,"area":null},{"id":"2210.13319","title":"MARS: Meta-Learning as Score Matching in the Function Space","created":"2022-10-24","cited_by_count":5,"area":null},{"id":"2304.10867","title":"Application of quantum-inspired generative models to small molecular datasets","created":"2023-04-21","cited_by_count":5,"area":null},{"id":"2402.08606","title":"Arbitrary Polynomial Separations in Trainable Quantum Machine Learning","created":"2024-02-13","cited_by_count":5,"area":null},{"id":"2111.06376","title":"Quantum Model-Discovery","created":"2021-11-11","cited_by_count":4,"area":null},{"id":"2205.07674","title":"Conditional Born machine for Monte Carlo event generation","created":"2022-05-16","cited_by_count":4,"area":null},{"id":"2210.13442","title":"Protocols for classically training quantum generative models on probability distributions","created":"2022-10-24","cited_by_count":4,"area":null},{"id":"2310.02075","title":"Learning Quantum Processes with Quantum Statistical Queries","created":"2023-10-03","cited_by_count":4,"area":null},{"id":"2406.06307","title":"Building Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset","created":"2024-06-10","cited_by_count":4,"area":null},{"id":"2107.01429","title":"QKSA: Quantum Knowledge Seeking Agent","created":"2021-07-03","cited_by_count":3,"area":null},{"id":"2110.04253","title":"F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits","created":"2021-10-08","cited_by_count":3,"area":null},{"id":"2203.17089","title":"Quantum-Aided Meta-Learning for Bayesian Binary Neural Networks via Born Machines","created":"2022-03-31","cited_by_count":3,"area":null},{"id":"2207.02346","title":"Many-body localized hidden generative models","created":"2022-07-05","cited_by_count":3,"area":null},{"id":"2408.10669","title":"Tensor tree learns hidden relational structures in data to construct generative models","created":"2024-08-20","cited_by_count":3,"area":null},{"id":"2509.16186","title":"Quantum Generative Adversarial Autoencoders: Learning latent representations for quantum data generation","created":"2025-09-19","cited_by_count":3,"area":null},{"id":"2510.08462","title":"Wavefunction Flows: Efficient Quantum Simulation of Continuous Flow Models","created":"2025-10-09","cited_by_count":3,"area":"diffusion"},{"id":"2302.00788","title":"A supplemental investigation of non-linearity in quantum generative models with respect to simulatability and optimization","created":"2023-02-01","cited_by_count":1,"area":null},{"id":"2305.02460","title":"Tensorizing flows: a tool for variational inference","created":"2023-05-03","cited_by_count":1,"area":null},{"id":"2306.03741","title":"Pre-training Tensor-Train Networks Facilitates Machine Learning with Variational Quantum Circuits","created":"2023-05-18","cited_by_count":1,"area":null},{"id":"2308.08448","title":"Implementing Quantum Generative Adversarial Network (qGAN) and QCBM in Finance","created":"2023-08-15","cited_by_count":1,"area":null},{"id":"2506.03779","title":"Position: Quantum Kernel Machines Should Move Beyond Scalar-Valued Kernels to Realize Their Potential","created":"2025-06-04","cited_by_count":1,"area":null},{"id":"2506.10275","title":"VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning","created":"2025-06-12","cited_by_count":1,"area":null},{"id":"2512.24801","title":"Limits of quantum generative models with classical sampling hardness","created":"2025-12-31","cited_by_count":1,"area":null},{"id":"2602.00048","title":"Quantum Circuit-Based Learning Models: Bridging Quantum Computing and Machine Learning","created":"2026-01-19","cited_by_count":1,"area":null},{"id":"2603.21749","title":"Model selection in hybrid quantum neural networks with applications to quantum transformer architectures","created":"2026-03-23","cited_by_count":1,"area":null},{"id":"2605.03573","title":"Local-Time Riemannian Score Matching on the Quantum Pure-State Manifold","created":"2026-05-05","cited_by_count":1,"area":null},{"id":"2606.10179","title":"Trainability of IQP Quantum Circuit Born Machines Under Gaussian Initialization","created":"2026-06-08","cited_by_count":1,"area":null},{"id":"2211.10418","title":"Sample-efficient Quantum Born Machine through Coding Rate Reduction","created":"2022-11-14","cited_by_count":0,"area":null},{"id":"2303.16955","title":"Quantum Generative Modeling using Parameterized Quantum Circuits","created":"2023-03-25","cited_by_count":0,"area":null},{"id":"2502.15129","title":"Data Complexity Measures for Quantum Circuits Architecture Recommendation","created":"2025-02-21","cited_by_count":0,"area":null},{"id":"2509.00744","title":"Implementing Pearl's $\\mathcal{DO}$-Calculus on Quantum Circuits: A Simpson-Type Case Study on NISQ Hardware","created":"2025-08-31","cited_by_count":0,"area":null},{"id":"2512.02721","title":"Generative modeling using evolved quantum Boltzmann machines","created":"2025-12-02","cited_by_count":0,"area":null},{"id":"2601.21629","title":"Reinforcement Learning for Adaptive Composition of Quantum Circuit Optimisation Passes","created":"2026-01-29","cited_by_count":0,"area":"deeprl"},{"id":"2602.17281","title":"Quantum Scrambling Born Machine","created":"2026-02-19","cited_by_count":0,"area":null},{"id":"2603.06440","title":"Toward Generative Quantum Utility via Correlation-Complexity Map","created":"2026-03-06","cited_by_count":0,"area":null},{"id":"2605.28162","title":"Learning Logical Operations for Arbitrary Quantum Error Correction Codes","created":"2026-05-27","cited_by_count":0,"area":null},{"id":"2606.21256","title":"Intrinsic Flow Matching on Quantum Pure-State Manifolds with Phase-Aligned Transport","created":"2026-06-19","cited_by_count":0,"area":"diffusion"},{"id":"2608.21700","title":"Scalable quantum simulation of continuous-time generative models via tensor networks","created":"2026-08-22","cited_by_count":0,"area":"diffusion"},{"id":"2609.00372","title":"Towards unsupervised representation learning for quantum data: quantum models with inference and generation","created":"2026-08-31","cited_by_count":0,"area":null},{"id":"2609.10505","title":"Quantum Feature Engineering for Credit Default Prediction: When and Why IQP Circuits Help Linear Classifiers","created":"2026-09-09","cited_by_count":0,"area":null},{"id":"2609.34995","title":"Simulation-Based Quantum System Inference with Neural Posterior Estimation","created":"2026-09-28","cited_by_count":0,"area":null},{"id":"2610.12806","title":"Trainable Quantum-Native Language Models: Dissipative Memory, Capacity Limits, and Hardware Validation","created":"2026-10-08","cited_by_count":0,"area":null}],"next_cursor":null}