This paper introduces a novel algorithm for European option pricing that uses the unary representation of the asset price. That is, each qubit maps into a single price of the underlying asset.

The amplitude distributor that uploads the expected price evolution to the qubit register and the circuit that encodes the estimated payoff into an ancilla are remarkably simple in the unary representation.

The amplitude distributor only requires first neighbor interactions between qubits, therefore a simplified chip architecture is enough to run the algorithm.

This scheme allows for a post-processing procedure that results in error mitigation for Noisy Intermediate-Scale Quantum devices. This algorithm is more robust to noise their usual binary

counterparts.

Option pricing in this unary representation can be beneficial for near-term quantum computers.

]]>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 env. 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.

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: 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.

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.

Welcome, Fabian!

]]>We hope you enjoy it as much as we did!

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

]]>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!

]]>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!

]]>“Variational Quantum Linear Solver: A Hybrid Algorithm for Linear Systems”, by C. Bravo-Prieto (~~@~~**charl_bp**), R. LaRose (~~@~~**ryanmlarose**), M. Cerezo (~~@~~**EntangledPhys**), Y. Subasi, L. Cincio and P. J. Coles (~~@~~**PatrickColes314**). preprint: https://scirate.com/arxiv/1909.05820

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.

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 ~~@~~**rigetti**‘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 know we have signed up a good researcher. Welcome (again) Sergi!

]]>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!

]]>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.

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