The digital representation of physical assets and process steps by digital twins is key to address pressuringchallenges like adaptive manufacturing or customised production. Recent breakthroughs in the field ofdigital twins and Edge-based AI already enable digital optimization of individual process steps. However,high-value goods typically include multiple step process chains including a broad range from generative andadditive processes over several steps of material removal up to assembly. Therefore, a digital twin over theholistic process chain is necessary. While even the set-up of representative twins for a single step is alreadychallenging, a concept for monitoring of the interaction and overall quality control of holistic process chainsdoes not exist yet. The paper introduces a machine-learning method based on probabilistic Bayesiannetworks to monitor the »digital twin quality« of coupled digital twins which includes several sub-instancesof digital twins. The approach identifies the contribution of each instance to the overall prediction quality.Furthermore, it is possible to give a range-estimation for the prediction accuracy of the individual subinstances. It is therefore possible to identify the most influential sub-instances of digital twins as well as theirindividual prediction quality. With the help of this information, the quality of the digital twin can beimproved by considering individual sub-instances in a targeted manner. Finally, a preview emphasises thepotential benefits of the quantum computing technology when dealing with parallel computation of largescale inference models.