E. Solano

Analog quantum feature selection with neutral-atom quantum processors

Abstract We present a quantum-native approach to feature selection (QFS) based on analog quantum simulation with neutral-atom arrays. Feature relevance, measured through mutual information with the target variable, is encoded in site-dependent local detunings, while pairwise feat...

Digitized counterdiabatic quantum feature extraction

We introduce a Hamiltonian-based quantum feature extraction method that generates complex features via the dynamics of $k$-local many-body spins Hamiltonians, enhancing machine learning performance. Classical feature vectors are embedded into spin-glass Hamiltonians, where both s...

Quantum approximate optimization algorithm with fixed number of parameters

We introduce a novel quantum optimization paradigm: the Fixed-Parameter-Count Quantum Approximate Optimization Algorithm (FPC-QAOA). It is a scalable variational framework that maintains a constant number of trainable parameters regardless of the number of qubits, Hamiltonian com...