Benchmarking the role of particle statistics in Quantum Reservoir Computing

Quantum reservoir computing is a neuro-inspired machine learning approach harnessing the rich dynamics of quantum systems to solve temporal tasks. It has gathered attention for its suitability for NISQ devices, for easy and fast trainability, and for potential quantum advantage. Although several types of systems have been proposed as quantum reservoirs, differences arising from particle statistics have not been established yet. In this work, we assess and compare the ability of bosons, fermions, and qubits to store information from past inputs by measuring linear and nonlinear memory capacity. While, in general, the performance of the system improves with the Hilbert space size, we show that also the information spreading capability is a key factor. For the simplest reservoir Hamiltonian choice, and for each boson limited to at most one excitation, fermions provide the best reservoir due to their intrinsic nonlocal properties. On the other hand, a tailored input injection strategy allows the exploitation of the abundance of degrees of freedom of the Hilbert space for bosonic quantum reservoir computing and enhances the computational power compared to both qubits and fermions.Comment: 11 pages, 12 figure

gllodra12/Benchmarking_QRC: Interactive code (v0.0.3)

In this release, we share the main code from our article: Benchmarking the role of particle statistics in Quantum Reservoir Computing. - Run code interactively with Google Colab. - Data to reproduce Figure 3 of the previous article is also available.You can run this code interactively with [Google Colab](https://colab.research.google.com/github/gllodra12/Benchmarking_QRC).This repository contains the code to analyse the role of particle statistics in Quantum Reservoir Computing.Peer reviewe

Detecting the topological phases of the Kitaev model via complex network analysis

[eng] We characterize the topological quantum phase transition on the 1D Kitaev model via complex network analysis. Weighted networks are created by means of several two-body correlations measures, such as mutual information, concurrence and coherence, which serve to build different adjacency matrices. We also analytically calculate the energy spectrum of the system to study its critical properties and use topological arguments to justify the robustness of the phase transition. Correlations measurements are computed numerically in the ground state using two open-source Python libraries (QuTiP and OpenFermion) to implement the Kitaev model and analyze the results in the two topological phases. Complex network measures such as disparity, density and clustering, signal the quantum phase transition providing a wider approach to understand how the system approaches the critical region

Aprenentatge basat en problemes: Una metodologia per fer-te pensar

[cat] En el present treball es pretén incorporar de manera efectiva la metodologia d'aprenentatge basat en problemes (ABPr). El document estarà organitzat en quatre blocs principals. En el primer apartat, justificació, es troba el perquè ens centrem en la metodologia ABPr. En el segon apartat, estat de la qüestió, s’analitza el paper que té l’ABPr dins el currículum de les Illes Balears i altres currículums, a més es farà una comparativa amb altres metodologies. En el tercer apartat, s’elabora una proposta didàctica centrada en el bloc de geometria de 2n d’ESO. En aquest apartat els docents interessats trobaran tots els recursos i activitats necessàries per tractar aquesta metodologia dins l’aula. Finalment, en el darrer apartat, s’exposen les conclusions finals del treball així com, els avantatges i inconvenients. L’autor és conscient que dins l’aula no tothom té el mateix nivell de matemàtiques, per aquesta raó durant l'elaboració de la proposta didàctica s’ha tingut molt en compte el material manipulatiu, ja que perquè aquest aprenentatge sigui significatiu s’han d’enllaçar conceptes matemàtics amb objectes o experiències que ja coneixen. D’aquesta manera, es pretén engrescar al major percentatge d’alumnes que sigui possible

Aprendizaje automático para la clasificación de arritmias cardíacas

[abstract not available

Detecting the topological phase of the Kitaev Model via network analysis

Trabajo presentado en el IFISC Poster Party (online).-- The IFISC Poster Party is an annual activity where PhD students and postdoctoral researchers of IFISC present their research in a poster format.-- Transport and Information in Quantum Systems.We use a classical network to examine the quantum properties of the finite Kitaev chain. Using network metrics, we show that classical correlation networks are a useful tool to detect the transition between the topological and trivial regime. It is also shown that other unexpected properties can also be detected by this technique.Peer reviewe

Benchmarking the role of particle statistics in Quantum Reservoir Computing

Quantum reservoir computing is a neuro-inspired machine learning approach harnessing the rich dynamics of quantum systems to solve temporal tasks. It has gathered attention for its suitability for NISQ devices, for easy and fast trainability, and for potential quantum advantage. Although several types of systems have been proposed as quantum reservoirs, differences arising from particle statistics have not been established yet. In this work, we assess and compare the ability of bosons, fermions, and qubits to store information from past inputs by measuring linear and nonlinear memory capacity. While, in general, the performance of the system improves with the Hilbert space size, we show that also the information spreading capability is a key factor. For the simplest reservoir Hamiltonian choice, and for each boson limited to at most one excitation, fermions provide the best reservoir due to their intrinsic nonlocal properties. On the other hand, a tailored input injection strategy allows the exploitation of the abundance of degrees of freedom of the Hilbert space for bosonic quantum reservoir computing and enhances the computational power compared to both qubits and fermions.The authors acknowledge useful discussions with M. C. Soriano and F. Plastina. Funding acknowledged from the Spanish State Research Agency, through the QUARESC project (PID2019-109094GB-C21/AEI/ 10.13039/501100011033) and the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (MDM-2017-0711), from CAIB through the QUAREC project (PRD2018/47), and from the CSIC Interdisciplinary Thematic Platform (PTI+) on Quantum Technologies in Spain (QTEP+). G.L.G. is funded by the Spanish MEF/MIU and co-funded by the University of the Balearic Islands through the Beatriz Galindo program (BG20/00085). C.C. was supported by Direcció General de Política Universitària i Recerca from the government of the Balearic Islands through the postdoctoral program Margalida Comas.N

Benchmarking the role of particle statistics in Quantum Reservoir Computing

11 pages, 12 figuresQuantum reservoir computing is a neuro-inspired machine learning approach harnessing the rich dynamics of quantum systems to solve temporal tasks. It has gathered attention for its suitability for NISQ devices, for easy and fast trainability, and for potential quantum advantage. Although several types of systems have been proposed as quantum reservoirs, differences arising from particle statistics have not been established yet. In this work, the ability of bosons, fermions, and qubits are assessed and compared to store information from past inputs by measuring linear and nonlinear memory capacity. While, in general, the performance of the system improves with the Hilbert space size, it is shown that also the information spreading capability is a key factor. For the simplest reservoir Hamiltonian choice, and for each boson limited to at most one excitation, fermions provide the best reservoir due to their intrinsic nonlocal properties. On the other hand, a tailored input injection strategy allows the exploitation of the abundance of degrees of freedom of the Hilbert space for bosonic quantum reservoir computing and enhances the computational power compared to both qubits and fermions.The authors acknowledge useful discussions with M. C. Soriano and F. Plastina. Funding acknowledged from the Spanish State Research Agency, through the QUARESC project (PID2019-109094GB-C21/AEI/ 10.13039/501100011033) and the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in R&D (MDM-2017-0711), from CAIB through the QUAREC project (PRD2018/47), and from the CSIC Interdisciplinary Thematic Platform (PTI+) on Quantum Technologies in Spain (QTEP+). G.L.G. is funded by the Spanish MEF/MIU and co-funded by the University of the Balearic Islands through the Beatriz Galindo program (BG20/00085). C.C. was supported by Direcció General de Política Universitària i Recerca from the government of the Balearic Islands through the postdoctoral program Margalida Comas.Peer reviewe

Benchmarking the performance of quantum reservoir computing platforms of particles of distinct statistics

Trabajo presentado en el IFISC Poster Party (online).-- The IFISC Poster Party is an annual activity where PhD students and postdoctoral researchers of IFISC present their research in a poster format.-- Transport and Information in Quantum Systems.Reservoir computing (RC) is a neuro-inspired machine learning approach to time series processing. As such, it forms an example of a natural unconventional analog computer designed to perform a given computational task. Its power in solving nonlinear and temporal tasks depends on the reservoir possessing a high dimensional state space and the ability to retain memory of information for sufficiently long time. Quantum systems, with their large number of degrees of freedom and their complex real time dynamics satisfy both requirements, and for this reason are good candidates to serve as substrates for RC. In addition, quantum effects such as superposition could lead to improvement in the performance of a RC. An important issue we explore here in order to establish the potential of quantum reservoirs computing (QRC) is the role of the particle statistics of the units composing the complex network reservoir. Considering the simplest interaction, we assess the performance of fermions bosons and the commonly used spins for QRC.Peer reviewe

Optimization of the efficiency of PhD students by employing cavity magnetrons

Trabajo presentado en el IFISC Poster Party (online).-- The IFISC Poster Party is an annual activity where PhD students and postdoctoral researchers of IFISC present their research in a poster format.-- Dynamics and Collective Phenomena in Social and Socio-technical Systems.Peer reviewe