Adrián Pérez

New article by the Theory Team!

This is Quantic’s latest article “Quantum observables for continuous control of the Quantum Approximate Optimization Algorithm via Reinforcement Learning”, by A. Garcia-Saez & J. Riu, available on the ArXiv.
The article presents a classical optimization strategy for the Quantum Approximation Optimization Algorithm (QAOA) using Reinforcement Learning (RL). The algorithm is tested for several instances of the MAXCUT problem.

In general, RL approaches consist of discrete-time agent-environment interactions. The agent is provided with partial/total observation of the environment and maximizes the reward by acting into it.

The QAOA is implemented such that, at each step of an episode of arbitrary but fixed length p, a pair of parameter-dependent unitary transformations are applied to a quantum state.
Gate sequence of the algorithm implemented.
The values of the parameters are selected by the Deep RL agent using as inputs to the Neural Network a set of measurements of the quantum state, which include the expected values of X and Z operators for each qubit as well as the clauses of the objective Hamiltonian individually.
At the end of each episode, the agent is rewarded with an amount equal to the expected value of the objective Hamiltonian in the final quantum state of the environment. Results for an instance of a 3-regular graph with 13 vertices are shown in the following graph:
Moreover, an incremental training strategy that allows the agent to reach larger p’>p episode lengths is successfully used for graphs with 21 qubits and p up to 25:

New experimental PhD student in Quantic!

This week, Fabian Zwiehoff from Germany has enrolled in our group as a new PhD student He did his studies at Karlsruhe Institute of Technology (KIT) and Technische Universität (TU) Berlin, and his Master’s project at ICFO on QKD. He will be working in the experimental team building superconducting quantum devices.

Welcome, Fabian!

Quantic at SONAR+D

Last summer, some members of Quantic were at Sonar+D, showing what a quantum computer looks like. It was a great experience, sharing some days with interesting people who exhibited their projects too. You can see here a little bit of the making of our quantum computer-toy.

We hope you enjoy it as much as we did!

People in this video: Pol Forn, Artur García, David López, Adrián Pérez

David López and his outreach

The Quantic member David lópez Núñez took part in the “Nit de Recerca Jove (European Researchers’ Night) 2019” hosted by CSIC, and gave a talk in his former High School in L’Hospitalet de Llobregat.

The title of the talks was: “Bricocuántica, ¿Cómo hacer un ordenador cuántico?” (Quantum DIY: how to make a quantum computer?)

      

 

It is always nice to get closer to new generations! Good job!

Our students this year (II)

The last of Quantic students, Rafael Luque, has presented his work with great results. Below one can find the updated list of grades

Bachelor:

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

Master:

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!

New article by Carlos Bravo, PhD student in Quantic

PhD student Carlos Bravo-Prieto posted a pre-print article as the result of his summer stay at Los Álamos (NM, USA):

“Variational Quantum Linear Solver: A Hybrid Algorithm for Linear Systems”, by C. Bravo-Prieto (), R. LaRose (), M. Cerezo (), Y. Subasi, L. Cincio and P. J. Coles (). preprint:

In this work, they presented a variational quantum algorithm for solving the quantum linear system problem. On the analytical side, they derived efficient quantum circuits to estimate faithful cost functions, while showing that they are difficult to estimate classically.

Schematic of the quantum linear solver algorithm.

On the numerical side, they studied the scaling of the algorithm run time and found it to be efficient with respect to the condition number and the desired precision:

Furthermore, they implemented the variational algorithm in ‘s quantum computer, for particular problems up to a size of 32×32, which is the largest implementation of a linear system on quantum hardware:

We have a new PhD student!

This September a new PhD student will join the Quantic group. Sergi Ramos, who has already worked with us in his TFG (Treball de Fi de Grau) and TFM (Treball de Fi de Màster) will be from now on a member of the theory division of the group.

We know we have signed up a good researcher. Welcome (again) Sergi!

Our students this year

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

Bachelor:

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

Master:

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.

 

  • Simple problem: points inside or outside of a circle. Results with a single-qubit classifier
  • Multi-class problem: three circular regions + intermediate space. Results with a single-qubit classifier
  •  Multi-dimensional problem: points inside or outside of a four-dimensional hypersphere.
    Single-qubit: 90% (4L), 95% (6L), 97% (8L)
    Two-qubit: 92% (2L), 98% (3L with entanglement)
    Four-qubit: 98% (2L with entanglement).
  •  Non-convex problem: points inside or outside of an annulus. The improvement of the QClass as we increase the number of layers is amazing!
  •  We have also checked other problems

  • Finally, we have compared the performance of the single-qubit classifier with two well-known classical classification techniques: NN and Support Vector Machines. The QClass results are comparable (sometimes better) with these methods.