2-QNSCD optimization algorithm for learning from quantum data
Reproducibility package for “Exploiting More Than Symmetry in Variational Quantum Machine Learning” (QCE26).
A training-free, channel-level audit that tests whether a noisy quantum neural network still respects an intended symmetry once the ideal circuit is composed with hardware noise.
Reproducible quantum optimization toolkit for QAOA and VQE with deterministic artifacts, benchmark corpus, and backend validation.
A Quantum Method for Constrained Vehicle Dynamics and Green-Wave Optimization
Reproducible quantum simulation of lattice QED and the Schwinger model with Qiskit, VQE, exact diagonalization, Trotterized dynamics, vacuum structure, topological theta dependence, entanglement, particle production, physical observables and error mitigation.
QVoice: Quantum Variational Optimization with In-Constraint Energy
Simulation study of how depolarising gate noise degrades the best-of-K cut of QAOA (p=1,2) on 3-regular MaxCut versus random sampling and an equal-budget hill climber. Exact density matrices, n=6-10, 3-5 graphs.
Preregistered simulator and IBM hardware pilot for Lambda-IC gradient-scale diagnostics in qMERA trainability.
A backend-agnostic quantum machine learning library for SpinQit, Qiskit, Cirq and PyTorch. Exact gradients, composable ansatze, quantum kernels. Cross-validated against PennyLane.
Benchmarking budget-aware classical optimizers vs COBYLA/SPSA for QAOA parameter tuning on IBM quantum hardware — negative result: shallow QAOA landscapes are too smooth to benefit from constrained optimization techniques.
Reproducible theory and experiments on representation alignment, trainability, and exact q-sample preparation in commuting quantum Boltzmann machines.
End-to-End Python implementation of Mancilla et al.'s (2026) methodology for solving the direct indexing portfolio selection problem as quantum combinatorial optimization. Enforces cardinality constraints via subspace confinement. Benchmarks PennyLane quantum circuits against D-Wave simulated annealing & HRP baselines with walk-forward backtesting.
Simulations and analysis showing that gradient loss in noisy U(1)-equivariant quantum neural networks is governed by readout-visible sector coherence. Density-matrix simulations, regression analysis, and reproducibility code for a study of noise-induced gradient degradation in equivariant brickwork QNNs.
Complete paper and reproducible code for the Hopf ansatz: arbitrary pure-state preparation, analytic geometry, and gradient-based optimization.
A personal repository where I place from time to time some ideas, that I am putting into practice in Quantum Computing to get feedback.
Hardware-Aware Variational Quantum Algorithms under Noise (VQE / QAOA Benchmarking Across IBM & IonQ)
Complete paper and reproducible code for Hopf quantum backpropagation: shared gradient records, inverse frames, and checkpoint methods.
Benchmarking flat-space, projection-based, and Riemannian optimizers across 8 variational quantum tasks. Simple projected updates often matched or beat more complex geometry-aware methods.
This repository is a personal collection of projects in Quantum Computing, combining theoretical study and practical implementation
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