# Inter-case Predictive Process Monitoring: A candidate for Quantum Machine Learning?

arXiv: https://arxiv.org/abs/2307.00080 · HTML: https://mlsanity.com/paper/2307.00080/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [The effect of data encoding on the expressive power of variational quantum machine learning models](https://mlsanity.com/paper/2008.08605/) (2020)
- [Connecting ansatz expressibility to gradient magnitudes and barren plateaus](https://mlsanity.com/paper/2101.02138/) (2021)
- [Quantum machine learning beyond kernel methods](https://mlsanity.com/paper/2110.13162/) (2021)
- [Deep Learning for Predictive Business Process Monitoring: Review and Benchmark](https://mlsanity.com/paper/2009.13251/) (2020)
- [Predictive Monitoring of Business Processes](https://mlsanity.com/paper/1312.4874/) (2013)
- [Quantum-classical convolutional neural networks in radiological image classification](https://mlsanity.com/paper/2204.12390/) (2022)
- [Quantum Kernel for Image Classification of Real World Manufacturing Defects](https://mlsanity.com/paper/2212.08693/) (2022)

## Abstract (arXiv)

Regardless of the domain, forecasting the future behaviour of a running process instance is a question of interest for decision makers, especially when multiple instances interact. Fostered by the recent advances in machine learning research, several methods have been proposed to predict the next activity, outcome or remaining time of a process automatically. Still, building a model with high predictive power requires both - intrinsic knowledge of how to extract meaningful features from the event log data and a model that captures complex patterns in data. This work builds upon the recent progress in inter-case Predictive Process Monitoring (PPM) and comprehensively benchmarks the impact of inter-case features on prediction accuracy. Moreover, it includes quantum machine learning models, which are expected to provide an advantage over classical models with a scaling amount of feature dimensions. The evaluation on real-world training data from the BPI challenge shows that the inter-case features provide a significant boost by more than four percent in accuracy and quantum algorithms are indeed competitive in a handful of feature configurations. Yet, as quantum hardware is still in its early stages of development, this paper critically discusses these findings in the light of runtime, noise and the risk to overfit on the training data. Finally, the implementation of an open-source plugin demonstrates the technical feasibility to connect a state-of-the-art workflow engine such as Camunda to an IBM quantum computing cloud service.