# A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead

arXiv: https://arxiv.org/abs/2310.10315 · HTML: https://mlsanity.com/paper/2310.10315/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 166 references that are in the map.
- First posted on arXiv 16 Oct 2023.

## Builds on (most cited first, 28 of 28)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Quantum machine learning: a classical perspective](https://mlsanity.com/paper/1707.08561/) (2017)
- [Robust data encodings for quantum classifiers](https://mlsanity.com/paper/2003.01695/) (2020)
- [Experimental Quantum Generative Adversarial Networks for Image Generation](https://mlsanity.com/paper/2010.06201/) (2020)
- [Recurrent Quantum Neural Networks](https://mlsanity.com/paper/2006.14619/) (2020)
- [TensorFlow Quantum: A Software Framework for Quantum Machine Learning](https://mlsanity.com/paper/2003.02989/) (2020)
- [Machine learning & artificial intelligence in the quantum domain](https://mlsanity.com/paper/1709.02779/) (2017)
- [Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead](https://mlsanity.com/paper/2012.11233/) (2020)
- [Entangled Datasets for Quantum Machine Learning](https://mlsanity.com/paper/2109.03400/) (2021)
- [Quantum Recurrent Neural Networks for Sequential Learning](https://mlsanity.com/paper/2302.03244/) (2023)
- [Quantum Neural Networks: Concepts, Applications, and Challenges](https://mlsanity.com/paper/2108.01468/) (2021)
- [Quantum kernels for real-world predictions based on electronic health records](https://mlsanity.com/paper/2112.06211/) (2021)
- [Concentration of Data Encoding in Parameterized Quantum Circuits](https://mlsanity.com/paper/2206.08273/) (2022)
- [A Practical Quantum Instruction Set Architecture](https://mlsanity.com/paper/1608.03355/) (2016)
- [A Leap among Quantum Computing and Quantum Neural Networks: A Survey](https://mlsanity.com/paper/2107.03313/) (2021)
- [A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms](https://mlsanity.com/paper/2208.13251/) (2022)
- [Optimization of Quantum Circuit Mapping using Gate Transformation and Commutation](https://mlsanity.com/paper/1907.02686/) (2019)
- [Compositional optimization of quantum circuits for quantum kernels of support vector machines](https://mlsanity.com/paper/2203.13848/) (2022)
- [Quantum Accelerator Stack: A Research Roadmap](https://mlsanity.com/paper/2102.02035/) (2021)
- [CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks](https://mlsanity.com/paper/2208.00331/) (2022)
- [Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques](https://mlsanity.com/paper/2307.11124/) (2023)
- [Solving machine learning optimization problems using quantum computers](https://mlsanity.com/paper/1911.08587/) (2019)
- [Efficient Noise Mitigation Technique for Quantum Computing](https://mlsanity.com/paper/2109.05136/) (2021)

## Built on by (most cited first, 29 of 29)

- [FedQNN: Federated Learning using Quantum Neural Networks](https://mlsanity.com/paper/2403.10861/) (2024)
- [QFNN-FFD: Quantum Federated Neural Network for Financial Fraud Detection](https://mlsanity.com/paper/2404.02595/) (2024)
- [Quantum Federated Learning: A Comprehensive Survey](https://mlsanity.com/paper/2508.15998/) (2025)
- [QADQN: Quantum Attention Deep Q-Network for Financial Market Prediction](https://mlsanity.com/paper/2408.03088/) (2024)
- [A PennyLane-Centric Dataset to Enhance LLM-based Quantum Code Generation using RAG](https://mlsanity.com/paper/2503.02497/) (2025)
- [QHackBench: Benchmarking Large Language Models for Quantum Code Generation Using PennyLane Hackathon Challenges](https://mlsanity.com/paper/2506.20008/) (2025)
- [PennyCoder: Efficient Domain-Specific LLMs for PennyLane-Based Quantum Code Generation](https://mlsanity.com/paper/2507.19562/) (2025)
- [FAQNAS: FLOPs-aware Hybrid Quantum Neural Architecture Search using Genetic Algorithm](https://mlsanity.com/paper/2511.10062/) (2025)
- [Robustness and Generalization in Quantum Reinforcement Learning via Lipschitz Regularization](https://mlsanity.com/paper/2410.21117/) (2024)
- [Readout-Side Bypass for Residual Hybrid Quantum-Classical Models](https://mlsanity.com/paper/2511.20922/) (2025)
- [QNAS: A Neural Architecture Search Framework for Accurate and Efficient Quantum Neural Networks](https://mlsanity.com/paper/2604.07013/) (2026)
- [AQ-PINNs: Attention-Enhanced Quantum Physics-Informed Neural Networks for Carbon-Efficient Climate Modeling](https://mlsanity.com/paper/2409.01626/) (2024)
- [Cross-Problem Parameter Transfer in Quantum Approximate Optimization Algorithm: A Machine Learning Approach](https://mlsanity.com/paper/2504.10733/) (2025)
- [ResQuNNs: Towards Enabling Deep Learning in Quantum Convolution Neural Networks](https://mlsanity.com/paper/2402.09146/) (2024)
- [RobQFL: Robust Quantum Federated Learning in Adversarial Environment](https://mlsanity.com/paper/2509.04914/) (2025)
- [Design Space Exploration of Hybrid Quantum Neural Networks for Chronic Kidney Disease](https://mlsanity.com/paper/2604.13608/) (2026)
- [Hybrid Quantum-Classical Neural Architecture Search](https://mlsanity.com/paper/2605.18345/) (2026)
- [Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices](https://mlsanity.com/paper/2605.22097/) (2026)
- [PennySynth: RAG-Driven Data Synthesis for Automated Quantum Code Generation](https://mlsanity.com/paper/2605.25572/) (2026)
- [VQCSim: When Does Compile-Once Statevector Simulation Beat Generic Quantum Frameworks?](https://mlsanity.com/paper/2607.11985/) (2026)
- [Quantum Neural Network applications to Protein Binding Affinity Predictions](https://mlsanity.com/paper/2508.03446/) (2025)
- [Hybrid Quantum--Classical Machine Learning Potential with Variational Quantum Circuits](https://mlsanity.com/paper/2508.04098/) (2025)
- [Domain-Aware Hybrid Quantum Learning via Correlation-Guided Circuit Design for Crime Pattern Analytics](https://mlsanity.com/paper/2604.07389/) (2026)
- [QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits](https://mlsanity.com/paper/2604.10933/) (2026)
- [Towards quantum machine learning for assessing the resilience of post-quantum cryptography](https://mlsanity.com/paper/2607.13722/) (2026)
- [Volcanic Clouds Detection through QCNN and Geostationary Satellite Multispectral Imagery](https://mlsanity.com/paper/2608.00072/) (2026)
- [Learning to Rank Tensor Network Contraction Plans for GPU-Accelerated Quantum Circuit Simulation](https://mlsanity.com/paper/2608.05819/) (2026)
- [Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach](https://mlsanity.com/paper/2609.25082/) (2026)
- [Q-PhotoMarket: A Design Space Exploration Framework for Photonic Hybrid Quantum Neural Networks in Financial Market Prediction](https://mlsanity.com/paper/2610.09641/) (2026)

## Abstract (arXiv)

Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is integrated with Machine Learning (ML), it creates a Quantum Machine Learning (QML) system. This paper aims to provide a thorough understanding of the foundational concepts of QC and its notable advantages over classical computing. Following this, we delve into the key aspects of QML in a detailed and comprehensive manner. In this survey, we investigate a variety of QML algorithms, discussing their applicability across different domains. We examine quantum datasets, highlighting their unique characteristics and advantages. The survey also covers the current state of hardware technologies, providing insights into the latest advancements and their implications for QML. Additionally, we review the software tools and simulators available for QML development, discussing their features and usability. Furthermore, we explore practical applications of QML, illustrating how it can be leveraged to solve real-world problems more efficiently than classical ML methods. This survey aims to consolidate the current landscape of QML and outline key opportunities and challenges for future research.