Quantum Principal Component Analysis and Self-TomographyHigh-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...Feb 7, 2025 · 8 min readamm.zanotp.com
Post-Variational Quantum Neural NetworksVariational 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...Feb 2, 2025 · 17 min readamm.zanotp.com
Learning Parametric Partial Differential Equations using Fourier Neural OperatorA 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...Dec 29, 2024 · 8 min readamm.zanotp.com
Solving linear systems of equations using quantum computersLinear 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...Sep 30, 2024 · 9 min readamm.zanotp.com
Computing Gradients of Quantum Circuits using Parameter Shift RuleVariational 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...Sep 19, 2024 · 8 min readamm.zanotp.com
