Ignacio Fernández Graña

During summer 2022, I interned in a quantum startup based in Amsterdam called Fermioniq. My internship project revolved around studying efficient (approximate) contraction methods for tensor networks. Tensor networks are a powerful mathematical tool with applications in many fields. A particularly important application in quantum information is to efficiently approximate quantum states. However, to extract information from the network, one has to perform the contraction of the network. Doing so in an exact manner is computationally intractable as it involves summing an exponentially increasing number of terms.

In this project, we studied inference algorithms for probabilistic graphical models and translated them for the task of tensor network contractions.

MSc thesis at the Quantum Matter and Artificial Intelligence (QMAI) group at TU Delft. During this project we studied digital and analog algorithms for quantum chemistry applications.

Full thesis can be found here .

Research project carried out as part of my Honours Programme in my MSc degree. I worked under the supervision of Prof. Jordi Tura in the Applied Quantum Algorithms (AQA) group (University of Leiden) studying Variational Quantum Algorithms to approximate the ground state of fermionic systems. We designed an adaptative VQE able that provides a Gaussian state approximation of a general fermionic system. We numerically benchmarked the VQE with two important fermionic systems: a system of non-interacting fermions or free-fermions, and the Sachdev-Ye-Kitaev (SYK) model.

More details about this project can be found here .

For my bachelor thesis, I studied deep learning techniques to analyze data from particle accelerators. Current particle accelerators generate very large amounts of data that need to be analyzed in real time, requiring powerful data analysis techniques that efficiently analyze the obtained data. The goal of my thesis was to train and benchmark a Convolutional Neural Network (CNN) to reduce the noise and identify particle tracks in images generated by particle collision events in accelerators such as PANDA experiment. CNNs have been shown to perform particularly well in image processing tasks, and we show they can successfully reduce the noise and identify individual particles in Monte-Carlo generated events.

The full thesis can be consulted here .

Simulation of the evolution of a pandemic via percolation theory.

The Quantum Approximate Optimization Algorithm (QAOA) algorithm is a widely studied quantum algorithm for solving combinatorial optimization problems. QAOA is particularly interested as it can be implemented in NISQ (Noisy Intermediate-Scale Quantum) devices, which is the kind of quantum processors that will be available in the next few years. QAOA algorithm is an example of a Variational Quantum Algorithm (VQA). VQAs rely on classical computation to optimize the parametrized gates in the circuit. Within this project, we implemented QAOA from scratch using the quantum computing framework Qiskit, and applied it to solve small instances of the Max-Cut problem.

The repository with the code can be found here .

Implemented and benchmarked different distillation protocols for quantum communication.

The repository with the code can be found here .

Implemented and benchmarked different distillation protocols for quantum communication.

The repository with the code can be found here .

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i = 0;

										while (!deck.isInOrder()) {
											print 'Iteration ' + i;
											deck.shuffle();
											i++;
										}

										print 'It took ' + i + ' iterations to sort the deck.';
									

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