While I can provide you with a list of machine learning projects related to quantum computing and quantum machine learning, it may be challenging to find specifically 30 projects that deal with predicting outcomes of quantum experiments, as the intersection of quantum experiments and machine learning prediction may not be as extensive. However, I can provide you with a list of machine learning projects in the broader field of quantum computing that involve various applications, including quantum state prediction, error correction, and quantum system characterization. Here are 10 such projects:
Quantum State Tomography using Machine Learning: Using machine learning techniques to reconstruct the quantum state of a system based on measurement outcomes.
Quantum Error Correction with Neural Networks: Applying neural networks to improve the performance of quantum error correction codes.
Quantum Circuit Optimization using Reinforcement Learning: Using reinforcement learning algorithms to optimize quantum circuits for specific tasks or reduce the number of gates.
Quantum Control Landscape Exploration: Using machine learning methods to explore the landscape of quantum control parameters for improving quantum system manipulation.
Quantum System Identification with Neural Networks: Utilizing neural networks to identify the underlying dynamics and parameters of a quantum system from experimental data.
Quantum Machine Learning for Molecular Property Prediction: Applying machine learning techniques to predict molecular properties using quantum data, such as quantum chemistry simulations.
Quantum Data Generation with Generative Adversarial Networks (GANs): Using GANs to generate synthetic quantum data for training quantum machine learning models.
Quantum Generative Models for Quantum State Generation: Developing generative models, such as variational autoencoders, to generate quantum states with desired properties.
Quantum Noise Characterization with Machine Learning: Employing machine learning techniques to characterize and mitigate noise in quantum systems, such as identifying noise sources and estimating their effects.
Quantum Generative Adversarial Networks for Quantum Image Classification: Applying generative adversarial networks to classify quantum images, such as images representing quantum circuits or quantum states.
While this list may not contain 30 projects specifically focused on predicting outcomes of quantum experiments, it provides a glimpse of various machine learning applications in the field of quantum computing.