Singularity Machine Learning

For many industrial machine learning problems, a strong solution is often constructed using ensemble methods. Singularity Machine Learning - Classification differs from traditional ensemble classification libraries as it allows mixing of different types and proportions of learners, ensemble training methods, and optimization algorithms for maximizing ensemble diversity and generalization.

In classical ensemble learning approach, finding the optimal weight for each learner is an exponentially complex optimization problem as the number of learners increases. Also, processing a significant number of features can be resource-consuming, and it is impossible to exhaustively search the space of solutions at very large sizes. The classical method uses a suboptimal shortcut to select relevant features. This optimization problem is notoriously hard to solve and out of bounds even for the best classical solvers in the market, but in Singularity ML more precise solutions are found thanks to the use of quantum solvers which run on real quantum hardware or through quantum-inspired tensor networks (TN) algorithms.

As a result, our solutions give better, more reliable, and precise predictions since it takes away biases that have a very strong dependence. You can learn more about our approach in this paper: https://arxiv.org/pdf/2212.03223

Unlike other Quantum ML products, Singularity Machine Learning is capable of handling large datasets with millions of examples and features without being limited by the number of qubits in the target QPU. The number of qubits only determines the size of the ensemble that can be trained.

Singularity Machine Learning works seamlessly on classically challenging problems involving high-dimensional, noisy, and unbalanced datasets.

An important feature of Singularity Machine Learning - Classification is its ability to allow users to fine-tune the machine learning model according to the specific requirements of their use case. Depending on the context, the users may need to minimize false positives or false negatives. Our quantum-enhanced classifier essentially comprises one hyperparameter which simplifies the hyperparameter tuning process significantly and makes it straightforward for the users to adjust the model’s behavior. For example, in scenarios where a more conservative approach is required such as cancer detection or defect detection, a user can easily “tweak” the model’s hyperparameters to prioritize more caution in detecting an instance as not defective or benign without needing in-depth coding knowledge in quantum computing.

As the size of the model increases, one of the advantages is that overall performance, meaning accuracy and faster training time, are achieved. In ensemble methods, overall performance is increased as the datasets grow. As IBM’s quantum platform develops and grows, customers will benefit from an increase in efficiency and speed as our function will scale proportionately.

Another significant benefit of Multiverse Computing’s Singularity ML vs. classical ML is the interpretability of the model. This is crucial in highly regulated industries such as healthcare and finance where the interpretability of the result is extremely important. The growing complexity of AI models significantly intensifies the need for interpretability. As organizations rely more on sophisticated algorithms, ensuring these models are understandable, transparent, and accountable is paramount for safe and responsible AI adoption. Singularity ML allows solving complex ML problems by striking the right balance between model complexity and interpretability. In addition, it allows you access to global and local interpretability methods for understanding the overall behavior of the classifier as well as the specific reasons behind a model’s prediction for each case.