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Financial institutions deal daily with a spectrum of computationally intensive challenges. These include for example forecasting tasks, such as pricing and risk estimation, detecting anomalous transactions, analysing customer preferences and optimiza...
High-dimensional data presents significant analytical challenges, for example some algorithms suffer the curse of dimensionality (i.e. as the number of dimensions increases, the volume of the data space grows exponentially, making the computation exp...
Variational quantum circuit are among the most promising methods for dealing with optimization problems, combinatorial optimization and quantum machine learning. However, despite their popularity, many of the ansatze upon which such circuit relies su...
A variety of problems in applied science revolve about solving systems of parametrized partial differential equations. Such systems often exhibit complex and non-linear behaviour and mesh-based methods might therefore require an incredibly fine discr...
Linear systems of equations lie at the heart of many scientific and engineering problems, from machine learning to optimization and physics simulations. Classical methods like Gaussian elimination or iterative methods are powerful but can be ineffici...
Variational Quantum Algorithms (VQAs) are regarded as one of the most promising approaches for leveraging near-term quantum devices as they combine the power of quantum circuits with classical optimization to solve problems in chemistry, material sci...
Machine learning is often guided by the balance between bias and variance, i.e. if a model is too simple, it struggles to capture the underlying relationships between inputs and outputs and, on the other hand, a model that's too complex might excel d...
Quantum Boltzmann Machines (QBMs) are at the cutting edge of quantum machine learning, offering a novel extension of classical Boltzmann machines through the lens of quantum mechanics. These models take advantage of quantum principles to push the bou...
Introduction Despite the grandiose name, Physics Informed Neural Networks (PINNs from now on) are simply neural networks trained to solve supervised learning tasks while adhering to any provided law of physics described by general nonlinear partial d...
