---
title: Quantum Continual Learning for Data
url: https://www.ml-quant.com/papers/repec/eee-phsmap-v-620-y-2023-i-c-s0378437123003345/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: RePEc:eee:phsmap:v:620:y:2023:i:c:s0378437123003345
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0378437123003345%3Bh%3Drepec%3Aeee%3Aphsmap%3Av%3A620%3Ay%3A2023%3Ai%3Ac%3As0378437123003345
featured: 2023-06-14
citations: unknown
topic: ML & AI Methods
---


# Quantum Continual Learning for Data

Continual learning proposed as solution to catastrophic forgetting in quantum machine learning.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0378437123003345%3Bh%3Drepec%3Aeee%3Aphsmap%3Av%3A620%3Ay%3A2023%3Ai%3Ac%3As0378437123003345
- Identifier: RePEc:eee:phsmap:v:620:y:2023:i:c:s0378437123003345
- Released: 2023-06-14
- First featured: Quant Letter No. 4 (2023-06-14): https://www.ml-quant.com/issues/2023-06-14/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: ML & AI Methods

## Related

- [Quantum kernel methods under scrutiny: a benchmarking study](https://www.ml-quant.com/papers/arxiv/2409.04406/): The study conducts a large-scale analysis of quantum kernel methods in quantum machine learning, comparing different types of kernels to understand what makes them effective.
- [Explainable representation learning of small quantum states](https://www.ml-quant.com/papers/arxiv/2306.05694/): The research examines the learned representation of a generative model trained on two-qubit density matrices, showing a direct correlation with the entanglement measure concurrence, providing insights into machine learning of quantum states.
- [Decoding Quantum LDPC Codes Using Graph Neural Networks](https://www.ml-quant.com/papers/arxiv/2408.05170/): A new decoding method for Quantum Low-Density Parity-Check codes based on Graph Neural Networks outperforms conventional and neural-enhanced decoding algorithms in performance and complexity.
- [A Hybrid Quantum-classical Fusion Neural Network to Improve Protein-ligand Binding Affinity Predictions for Drug Discovery](https://www.ml-quant.com/papers/arxiv/2309.03919/): A new quantum-classical deep learning model has been created to predict drug binding affinity, increasing accuracy by 6% and providing more stable performance than traditional models.
- [HQNN-FSP: A hybrid classical-quantum neural network for regression-based financial stock market prediction](https://www.ml-quant.com/papers/arxiv/2503.15403/): The research investigates the use of hybrid quantum-classical methods for predicting financial trends, introducing a Quantum Neural Network (QNN) regressor and two hybrid optimization strategies.
- [On Quantum Bsde Solver for High-Dimensional Parabolic Pdes](https://www.ml-quant.com/papers/arxiv/2506.14612/): The research introduces a quantum machine learning method for approximating solutions to complex partial differential equations, showing that Variational Quantum Circuits offer better accuracy and lower variance, especially in highly nonlinear situations.