- Aaronson, 2022, How Much Structure Is Needed for Huge Quantum Speedups?
- Abhijith J. et al., 2022, Quantum Algorithm Implementations for Beginners
- Acuto et al., 2022, Variational Quantum Soft Actor-Critic for Robotic Arm Control
- Ai et al., 2022, Decompositional Quantum Graph Neural Network
- Akhalwaya et al., 2022, Exponential advantage on noisy quantum computers
- Albrecht et al., 2022, Quantum Feature Maps for Graph Machine Learning on a Neutral Atom Quantum Processor
- Allcock & Yuan & Zhang, 2022, Does qubit connectivity impact quantum circuit complexity?
- Ahmed et al., 2022, Implicit differentiation of variational quantum algorithms
- Anand et al., 2022, Exploring the role of parameters in variational quantum algorithms
- Anschuetz et al., 2022 Interpretable Quantum Advantage in Neural Sequence Learning
- Back & Run & Kim, 2022, Scalable Quantum Convolutional Neural Networks
- Beaudoin et al., 2022, Quantum Machine Learning for Material Synthesis and Hardware Security
- Bermejo & Orus, 2022, Variational Quantum and Quantum-Inspired Clustering
- Bittel & Watty & Kliesch, 2022, Fast gradient estimation for variational quantum algorithms
- Bonomi et al., 2022, Quantum Annealing Learning Search Implementations
- Bödeker & Fiorelli & Müller, 2022, Optimal storage capacity of quantum Hopfield neural networks
- Bravo et al., 2022, Universal Quantum Perceptrons for Quantum Machine Learning
- Buessen & Segal & Khait, 2022, Simulating time evolution on distributed quantum computers
- Callison & Browne, 2022, Improved maximum-likelihood quantum amplitude estimation
- Caro et al., 2022, Generalization in quantum machine learning from few training data
- Ceroni et al., 2022, Generating Approximate Ground States of Molecules Using Quantum Machine Learning
- Chandarana et al., 2022, Digitized-Counterdiabatic Quantum Algorithm for Protein Folding
- Chaudhary et al., 2022, Towards a scalable discrete quantum generative adversarial neural network
- Chen, 2022, Quantum deep recurrent reinforcement learning
- Chen & Fry & Deshmukh, 2022, Reservoir Computing via Quantum Recurrent Neural Networks
- Cherrat et al., 2022, Quantum Vision Transformers
- Consiglio & Appollaro & Wiesniak, 2022, A Variational Approach to the Quantum Separability Problem
- Cimini et al., 2022, Deep reinforcement learning for quantum multiparameter estimation
- Correll et al., 2022, Quantum Neural Networks for a Supply Chain Logistics Application
- Cruz & Monteiro, 2022, Quantum Error Correction via Noise Guessing Decoding
- Cumming & Thomas, 2022, Using a quantum computer to solve a real-world problem – what can be achieved today?
- Dasgupta & Paine, 2022, Loading Probability Distributions in a Quantum circuit
- Dawid et al., 2022, Modern applications of machine learning in quantum sciences
- Di Matteo et al., 2022, Quantum computing with differentiable quantum transforms
- Ding & Spector, 2022, Evolutionary Quantum Architecture Search for Parametrized Quantum Circuits
- Dou et al., 2022, QPanda: high-performance quantum computing framework for multiple application scenarios
- Duffield & Benedetti & Rosenkranz, 2022, Bayesian Learning of Parameterised Quantum Circuits
- Emmanoulopoulos & Dimoska, 2022, Quantum Machine Learning in Finance: Time Series Forecasting
- Ewaniuk et al., 2022, Realistic quantum photonic neural networks
- Fadol et al., 2022, Application of Quantum Machine Learning in a Higgs Physics Study at the CEPC
- Fedorov et al., 2022, Quantum computing at the quantum advantage threshold: a down-to-business review
- Feng & Zhou & Zhang, 2022, Noise-Resilient Quantum Power Flow
- Ferris et al., 2022, Quantum Simulation on Noisy Superconducting Quantum Computers
- Fujii et al., 2022, Deep Variational Quantum Eigensolver: a divide-and-conquer method for solving a larger problem with smaller size quantum computers
- Garcia, Benito, Garcia-Penalvo, 2022. Systematic Literature Review: Quantum Machine Learning and its applications
- Gentinetta et al., 2022, The complexity of quantum support vector machines
- Ghosh & Ghosh, 2022, Classical and quantum machine learning applications in spintronics
- Ghosh et al., 2022, Harmonic (Quantum) Neural Networks
- Gili et al., 2022, Do Quantum Circuit Born Machines Generalize?
- Gomez et al., 2022, Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework
- Gong et al., 2022, Enhancing Quantum Adversarial Robustness by Randomized Encodings
- Grange & Poss & Bourreau, 2022, An introduction to variational quantum algorithms on gate-based quantum computing for combinatorial optimization problems
- Grossi et al., 2022, Mixed Quantum-Classical Method For Fraud Detection with Quantum Feature Selection
- Guan & Fang & Ying, 2022, Verifying Fairness in Quantum Machine Learning
- Guo et al., 2022, Scalable quantum computational chemistry with superconducting qubits
- Gupta et al., 2022, Hamiltonian learning from time dynamics using variational algorithms
- Gyurik & Dunjko, 2022, On establishing learning separations between classical and quantum machine learning with classical data
- Heimann & Schönhoff & Franck Kirchner, 2022, Learning capability of parametrized quantum circuits
- Hoffmann & Brown, 2022, Gradient Estimation with Constant Scaling for Hybrid Quantum Machine Learning
- Hu & Zhu, 2022, Finitely Repeated Adversarial Quantum Hypothesis Testing
- Hu et al., 2022, Quantum Neural Network Compression
- Hu & Zhu, 2022, Finitely Repeated Adversarial Quantum Hypothesis Testing
- Huang et al., 2022, Tensor Network Assisted Variational Quantum Algorithm
- Ibrahim et al., 2022, Pulse-Level Optimization of Parameterized Quantum Circuits for Variational Quantum Algorithms
- Ikeda, 2022, Quantum Extensive Form Games
- Incudini et al., 2022, Computing graph edit distance on quantum devices
- Incudini & Martini & Di Pierro, 2022, Structure Learning of Quantum Embeddings
- Incudini et al., 2022, The Quantum Path Kernel: a Generalized Quantum Neural Tangent Kernel for Deep Quantum Machine Learning
- Innocenti et al., 2022, On the potential and limitations of quantum extreme learning machines
- Jäger & Krems, 2022, Universal expressiveness of variational quantum classifiers and quantum kernels for support vector machines
- Jain & Garcia Garcia, 2022, Quantum neural network for continuous variable prediction
- Jerbi et al., 2022, Quantum policy gradient algorithms
- Kao et al., 2022, Quantum Advantage in Small Molecule Drug Discovery
- Karimi et al., 2022, The Power of One Clean Qubit in Supervised Machine Learning
- Kasture & Kyriienko & Elfving, 2022, Protocols for classically training quantum generative models on probability distributions
- Kerenidis & Prakash, 2022, Quantum machine learning with subspace states
- Kim & Huh & Park, 2022, Classical-to-quantum convolutional neural network transfer learning
- Kiss et al., 2022, Quantum neural networks force fields generation
- Kiss et al., 2022, Conditional Born machine for Monte Carlo event generation
- Koch, 2022, Quantum Machine Learning using the ZXW-Calculus
- Koike-Akino & Wang, 2022, AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications
- Kölle et al., 2022, Improving Convergence for Quantum Variational Classifiers using Weight Re-Mapping
- Koßmann et al., 2022, Deep-Circuit QAOA
- Kottman, 2022, Investigating Quantum Many-Body Systems with Tensor Networks, Machine Learning and Quantum Computers
- Krenn et al., 2022, Artificial Intelligence and Machine Learning for Quantum Technologies
- Krol et al. 2022, Efficient parameterized compilation for hybrid quantum programming
- Kyriienko & Magnusson, 2022, Unsupervised quantum machine learning for fraud detection
- Lai & Kuo & Liao, 2022, Syndrome decoding by quantum approximate optimization
- Landman et al., 2022, Quantum Methods for Neural Networks and Application to Medical Image Classification
- Leclerc et al., 2022, Financial Risk Management on a Neutral Atom Quantum Processor
- Li et al., 2022, Quantum Neural Network Classifiers: A tutorial
- Li-Gomez et al., 2022, Quantum enhanced probing of multilayered-samples
- Liang et al., 2022, PAN: Pulse Ansatz on NISQ Machines
- Liang et al., 2022, Variational Quantum Pulse Learning
- Liao et al., 2022, Decohering Tensor Network Quantum Machine Learning Models
- Liao & Shan, 2022, Expressibility-Enhancing Strategies for Quantum Neural Networks
- Lin & Li & Huang, 2022, Quaternion-based machine learning on topological quantum systems
- Lisnichenko & Protasov, 2022, Case study on quantum convolutional neural network scalability
- Liu et al., 2022, Noise can be helpful for variational quantum algorithms
- Liu et al., 2022, Quantum Power Flows: From Theory to Practice
- Lotshaw et al., 2022, Approximate Boltzmann Distributions in Quantum Approximate Optimization
- Lourens et al., 2022, Architecture representations for quantum convolutional neural networks
- Maheshwari & Garcia-Zapirain & Sierra-Sosa, 2022, Quantum Machine Learning Applications in the Biomedical Domain: A Systematic Review
- Majumder & Lewis & Bose, 2022, Variational Quantum Circuits for Multi-Qubit Gate Automata
- Mancilla & Pere, 2022, A Preprocessing Perspective for Quantum Machine Learning Classification Advantage Using NISQ Algorithms
- Marcianò et al., 2022, Deep Neural Networks as the Semi-classical Limit of Topological Quantum Neural Networks: The problem of generalisation
- Marconi et al., 2022, The role of coherence theory in attractor quantum neural networks
- Markidis, 2022, On the Physics-Informed Neural Networks for Quantum Computers
- Martin & Plekhanov & Lubasch, 2022, Barren plateaus in quantum tensor network optimization
- Meirom & Frankel, 2022, PANSATZ: PULSE-BASED ANSATZ FOR VARIATIONAL QUANTUM ALGORITHMS
- Melo & Earnest-Noble & Tacchino, 2022, Pulse-efficient quantum machine learning
- Mensa et al., 2022, Quantum Machine Learning Framework for Virtual Screening in Drug Discovery: a Prospective Quantum Advantage
- Meyer et al., 2022, A Survey on Quantum Reinforcement Learning
- MindSpore Quantum Developers, 2022, Benchmarking Variational Quantum Eigensolvers for Quantum Chemistry
- Mitsuda et al., 2022, Approximate complex amplitude encoding algorithm and its application to classification problem in financial operations
- Miyahara & Roychowdhury, 2022, Quantum Advantage in Variational Bayes Inference
- Monaco et al., 2022, Quantum phase detection generalisation from marginal quantum neural network models
- Motamedi & Ronagh, 2022, Gibbs Sampling of Periodic Potentials on a Quantum Computer
- Moussa et al., 2022, Resource frugal optimizer for quantum machine learning
- Murakami & Zhao, 2022, AutoQC: Automated Synthesis of Quantum Circuits Using Neural Network
- Mustafa & Morapakula & Jain, 2022, Variational Quantum Algorithms for Chemical Simulation and Drug Discovery
- Nakaji & Tezuka & Yamamoto, 2022, Deterministic and random features for large-scale quantum kernel machine
- Neumann & Wezeman, 2022, Distributed Quantum Machine Learning
- Nguemto & Leyton-Ortega, 2022, Re-QGAN: an optimized adversarial quantum circuit learning framework
- Nguyen et al., 2022, Theory for Equivariant Quantum Neural Networks
- Olivera-Atencio et al., 2022, Quantum reinforcement learning in the presence of thermal dissipation
- Oliviero et al., 2022, Measuring magic on a quantum processor
- Ono et al., 2022, Demonstration of a bosonic quantum classifier with data re-uploading
- Oshiyama & Ohzeki, 2022, Benchmark of quantum‑inspired heuristic solvers for quadratic unconstrained binary optimization
- Otgonbaatar et al., 2022, Quantum Transfer Learning for Real-World, Small, and Large-Scale Datasets
- Pan et al. 2022, Deep quantum neural networks equipped with backpropagation on a superconducting processor
- Patel et al., 2022, Reinforcement Learning Assisted Recursive QAOA
- Park & Huh & Park, 2022, Variational quantum one-class classifier
- Park et al., 2022, Quantum multi-programming for Grover’s search
- Pechal et al., 2022, Direct implementation of a perceptron in superconducting circuit quantum hardware
- Peham & Burgholzer, 2022, Equivalence Checking of Quantum Circuits with the ZX-Calculus
- Peixoto et al., 2022, Fitting a Collider in a Quantum Computer: Tackling the Challenges of Quantum Machine Learning for Big Datasets
- Peters & Schuld, 2022, Generalization despite overfitting in quantum machine learning models
- Phalak & Li & Gosh, 2022, Approximate Quantum Random Access Memory Architectures
- Phan et al., 2022, On quantum factoring using noisy intermediate-scale quantum computers
- Pira & Ferrie, 2022, An Invitation to Distributed Quantum Neural Networks
- Pirnay et al., 2022, A super-polynomial quantum advantage for combinatorial optimization problems
- Poggel et al., 2022, Recommending Solution Paths for Solving Optimization Problems with Quantum Computing
- Polson & Sokolov & Xu, 2022, Quantum Bayes AI
- Pozza et al., 2022, Quantum Reinforcement Learning: The maze problem
- Qi, 2022, Federated Quantum Natural Gradient Descent for Quantum Federated Learning
- Qin et al., 2022, Improving Quantum Classifier Performance in NISQ Computers by Voting Strategy from Ensemble Learning
- Qin, 2022, Review of Ansatz Designing Techniques for Variational Quantum Algorithms
- Radha & Jao, 2022, Generalized quantum Similarity Learning
- Ragone et al., 2022, Representation Theory for Geometric Quantum Machine Learning
- Robertson et al., 2022, Escaping barren plateaus in approximate quantum compiling
- Sagingalieva et al., 2022, Hybrid quantum neural network for drug response prediction
- Sajjan et al., 2022, Quantum machine learning for chemistry and physics
- Schatzki et al., 2022, Theoretical Guarantees for Permutation-Equivariant Quantum Neural Networks
- Schuld & Killoran, 2022, Is quantum advantage the right goal for quantum machine learning?
- Sharma & Kumar, 2022, A Comparative Study of Classical and Quantum Machine Learning Models for Sentimental Analysis
- Shastry et al., 2022, Reliable quantum kernel classification using fewer circuit evaluations
- Schenk et al., 2022, Hybrid actor-critic algorithm for quantum reinforcement learning at CERN beam lines
- Simeone, 2022, An Introduction to Quantum Machine Learning for Engineers
- Simonetti, Perri & Gervasi, 2022, An example of use of Variational Methods in Quantum Machine Learning
- Skolik et al., 2022, Robustness of quantum reinforcement learning under hardware errors
- Smith & Paige & Kim, 2022, Faster variational quantum algorithms with quantum kernel-based surrogate models
- Stella Li et al., 2022, Q-LSTM Language Model Decentralized Quantum Multilingual Pre-Trained Language Model for Privacy Protection
- Stein et al., 2022, QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation
- Steinmüller et al., 2022, eXplainable AI for Quantum Machine Learning
- Srikumar & Hill & Hollenberg, 2022, A kernel-based quantum random forest for improved classification
- Suzuki & Kawaguchi & Yamamoto, 2022, Quantum Fisher kernel for mitigating the vanishing similarity issue
- Szulakowska & Dai, 2022, Bayesian autotuning of Hubbard model quantum simulators
- Takeda et al., 2022, Quantum-inspired algorithm applied to extreme learning
- Tancara et al., 2022, Kernel-based quantum regressor models learn non-Markovianity
- Tang & Yan & Hancock, 2022, From Quantum Graph Computing to Quantum Graph Learning: A Survey
- Tapia et al., 2022, Fraud detection with a single-qubit quantum neural network
- Thanasilp et al., 2022, Exponential concentration and untrainability in quantum kernel methods
- Tibaldi et al., 2022, Bayesian Optimization for QAOA
- Tilly et al., 2022, The VQE: a review of methods and best practices
- Tomesh & Allen & Saleem, 2022, Quantum-classical tradeoffs and multi-controlled quantum gate decompositions in variational algorithms
- Tsang et al., 2022, Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation
- Turati & Dacrema & Cremonesi, 2022, Feature Selection for Classification with QAOA
- Uvarov, 2022, Variational quantum algorithms for local Hamiltonian problems
- Viktorovich, 2022, Variational quantum algorithms for local Hamiltonian problems
- Wang & Jiang, 2022, Data reconstruction based on quantum neural networks
- Wang et al. 2022, Symmetric Pruning in Quantum Neural Networks
- Wazni, Lo, McPheat, Sadrzadeh, 2022, A Quantum Natural Language Processing Approach to Pronoun Resolution
- West et al., 2022, Benchmarking Adversarially Robust Quantum Machine Learning at Scale
- Wilkinson & Hartmann, 2022, Evaluating the performance of sigmoid quantum perceptrons in quantum neural networks
- Willsch et al., 2022, Hybrid Quantum Classical Simulations
- Wu & Tao & Li, 2022, Scalable Quantum Neural Networks for Classification
- Yang, Lu and Li, 2022, Accelerated quantum Monte Carlo with mitigated error on noisy quantum computer
- Yang et al., 2022, An analog quantum variational embedding classifier
- Yu et al., 2022, Quantum federated learning based on gradient descent
- Yun & Park & Kim, 2022, Quantum Multi-Agent Meta Reinforcement Learning
- Yun et al., 2022, Slimmable Quantum Federated Learning
- Yun et al., 2022, Quantum Federated Learning with Entanglement Controlled Circuits and Superposition Coding
- Zaborniak et al., 2022, A QUBO model of the RNA folding problem optimized by variational hybrid quantum annealing
- Zhan 2022, Variational Quantum Search with Exponential Speedup
- Zhang & Wang, 2022, An efficient combination strategy for hybrid quantum ensemble classifier
- Zhang & Zhao, 2022, Evolutionary-based quantum architecture search
- Zhang et al., 2022, QUARK: A Gradient-Free Quantum Learning Framework For Classification Tasks
- Zhao & Hu & Zhang, 2022, Supervised Hamiltonian learning via efficient and robust quantum descent
- Zheng & Wang & Zhang, 2022, A quantum neural network with efficient optimization and interpretability
- Zhou et al., 2022, QuanGCN: Noise-Adaptive Training for Robust Quantum Graph Convolutional Networks