Our students this year

As the summer ends, we show here the Bachelor’s and Master’s theses our students have presented this year


Gabriel Fernández: Quantum Autoencoders; 8,6

Elies Gil: Variational Quantum Classifier; 9,7

Josep Lumbreras: Scaling of the energy and entropy errors in quantum circuits; 9,1

Santi Vallés: Design of infrared filters to improve the quality of a superconducting qubit; 9,3


Sergi Ramos: Maximal Entanglement in One-Loop Z Boson Decay; 9,1

Rafael Luque: Coherent control of a superconducting quantum bit; 9


Everyone did a great job at Quantic. Some of them will continue within the team, other people will spread their wings. The best of luck for you all!

Quantic turns 2 years old

We are glad to announce that the Quantic family keeps growing. We are already two years old and have more members than ever. Congrats for these two years, and we look forward new and exciting science and projects.

We would like to welcome all new partners to Quantic, and to wish all the best for all people leaving the group. We will see each other again soon.


New article by the Theory team

We present our last article “Data re-uploading for a universal quantum classifier”, by A. Pérez-Salinas, A. Cervera-Lierta, E. Gil-Fuster and J. I. Latorre. It is available in arXiv:1907.02085 and SciRate


The main result of this work is to show that there is a trade-off between the number of qubits needed to perform classification and multiple data re-uploading.

The quantum classifier we built can be understood as a modification of a Neural Network. In feed-forward neural networks (NN), each data point is entered and processed in each neuron. If NN were affected by the no-cloning theorem, they couldn’t work as they do. To build a quantum classifier (QClass), we need to load classical data several times along the computation.

To upload and process data in the QClass, we use a general unitary gate. Each of these gates (called “layer L”) introduces the data points “x” and the processing parameters “phi” that should be adjusted by using some cost function.

We train a single-qubit QClass by dividing the Bloch Sphere into several regions, one for each class, and fine-tune the processing parameters to distribute each data point to its corresponding region. We choose these regions to be maximally orthogonal.

Then, we can define the cost function as the fidelity of the final state of the QClass and the corresponding “class state”. We propose two ways to do that, which can be found in the article. A single-qubit QClass can’t represent any quantum advantage, although, for its simplicity, could be a part of larger circuits. However, this QClass can be generalized to multi-qubit QClass, where the introduction of entanglement will improve the classification procedure. Once we have defined the QClass and the cost function, we need to use a classical minimization method to find the processing parameters. The QClass belongs to the family of parametrized quantum circuits, as the VQE or Qautoencoder. We have used the L-BFGS-B algorithm from scipy.

Benchmark: we have tested the single- and multi-qubit QClass composed by a different number of layers in several problems with different characteristics.


New Doctor at Quantic!

We are glad to announce that last June 21st the member of Quantic Alba Cervera-Lierta has become the first Quantic doctor. She defended her PhD thesis in front of Dr. Alejandro Perdomo-Ortiz (Zapata Computing Inc.), Dr. Ivano Tavernelli (IBM-Zürich) and Prof. Germán Sierra (IFT-CSIC).

The whole Quantic Group congratulates you. Your amazing perseverance as well as your dedication has at last paid off.

Congrats, Alba! There is a wonderful future ahead of you!



New article preprint for Quantic

Last May 7th the members of Quantic Carlos Bravo Prieto, Diego García Martín and José Ignacio Latorre published an arXiv preprint named Quantum Singular Value Decomposer.

In this article we propose a hybrid classical-quantum algorithm that produces the Singular Value Decomposition of a bipartite pure state. The proposed algorithm (Quantum Singular Value Decomposer or QSVD) forces a diagonal form of the state by demanding exact output coincidence.

By training the unitaries to force that subtle diagonalization (the more layers the more precision), we recover the singular values from the probabilities of obtaining the computational-basis vectors after measurement. It follows that entropies can be estimated

A peculiar spinoff of the QSVD circuit is the possibility of performing a SWAP operation between parties A and B without using any gate that connects both subsystems. We just have to apply the adjoints of the unitaries in both parties by classical communication of the parameters.


We consider a further spinoff. We can use the QVSD and CNOTs as a quantum encoder of information of the original state onto one of its parties. This idea can be reversed and used to create random states with a precise entanglement structure.