2 citations · 2 across the 7 of their papers we have counts for
8 papers
Practical Considerations for Processing Massive Classical Data via Quantum Oracle Sketching
Stefano Markidis
Classical-to-quantum I/O is a critical bottleneck for data-intensive quantum workloads. Quantum Oracle Sketching (QOS) has been proposed to address this problem. We identify its pr…
Quantum Kernels are Spectral Tensor Networks
Erik M. Åsgrim, Stefano Markidis
Quantum kernels admit Fourier representations whose frequencies are determined by the data-encoding gates of the underlying feature map. We show that entangling tensor kernels are…
Not Your Usual FFT: QFTFFT via Classical Quantum-Circuit Simulation
Stefano Markidis, Gilbert Netzer, Luca Pennati +2
We introduce QFTFFT, a family of HPC FFT libraries that compute the discrete Fourier transform by executing a quantum Fourier transform (QFT) circuit on classical quan…
When Noisy Quantum Order Finding Remains Recoverable for Shor's Algorithm
Qingxin Yang, Stefano Markidis
Order finding is the core subroutine of Shor's algorithm. On NISQ hardware, phase estimation output distributions are often distorted by noise, making correct order recovery diffic…
Quantum Optimization for Electromagnetics: Physics-Informed QAOA for Reconfigurable Intelligent Surfaces
Marco Pasquale, Erik M. Åsgrim, Stefano Markidis +1
Optimizing Reconfigurable Intelligent Surfaces (RIS) is a high-dimensional combinatorial challenge. Current quantum algorithms often simplify this problem by ignoring physical cons…
A Continuous-Variable Quantum Fourier Layer: Applications to Filtering and PDE Solving
Paolo Marcandelli, Stefano Mariani, Martina Siena +1
Fourier representations play a central role in operator learning methods for partial differential equations and are increasingly being explored in quantum machine learning architec…
What is Quantum Parallelism, Anyhow?
Stefano Markidis
Central to the power of quantum computing is the concept of quantum parallelism: quantum systems can explore and process multiple computational paths simultaneously. In this paper,…
Brain-Inspired Physics-Informed Neural Networks: Bare-Minimum Neural Architectures for PDE Solvers
Stefano Markidis
Physics-Informed Neural Networks (PINNs) have emerged as a powerful tool for solving partial differential equations~(PDEs) in various scientific and engineering domains. However, t…