arXiv subjects

Herbert Vinck-Posada

Publications and source records attributed to Herbert Vinck-Posada.

At least 19 recordsLinked to original sources

arXiv2026

Arbitrary state preparation in quantum harmonic oscillators using neural networks

Preparing quantum states is a fundamental task in various quantum algorithms. In particular, state preparation in quantum harmonic oscillators (HOs) is crucial for the manipulation of qudits and the implementation of high-dimensional algorithms. In this work, we develop a general methodology for quantum state preparation in an HO coupled to an auxiliary qubit, guaranteeing that any target state is physically preparable. Both the qubit and the HO are driven by two lasers with time-dependent phase modulation. The modulation times and phase values are generated by a neural network whose input is the desired target state. In contrast to conventional quantum control approaches, this framework eliminates the need for per-instance optimization of the control protocol. Instead, the control parameters required to prepare an arbitrary quantum state of the HO are obtained directly from a single forward pass through the neural network. Specifically, we present results for preparing arbitrary qubit, qutrit, and qudit (n=4) states in the HO, achieving average fidelities of 99.99%, 99.5%, and 98.9%, respectively, across random target states.

quant-ph↗

arXiv2026

Variational decision diagrams for quantum-inspired machine learning applications

Decision diagrams (DDs) have emerged as an efficient tool for simulating quantum circuits due to their capacity to exploit data redundancies in quantum states and quantum operations, enabling the efficient computation of probability amplitudes. However, their application in quantum machine learning (QML) has remained unexplored. This paper introduces variational decision diagrams (VDDs), a novel graph structure that combines the structural benefits of DDs with the adaptability of variational methods for efficiently representing quantum states. We investigate the trainability of VDDs by applying them to the ground state estimation problem for transverse-field Ising and Heisenberg Hamiltonians. Analysis of gradient variance suggests that training VDDs is possible, as no signs of vanishing gradients--also known as barren plateaus--are observed. This work provides new insights into the use of decision diagrams in QML as an alternative to design and train variational ansätze.

quant-ph↗

arXiv2025

Dynamical and Emission Properties of Quantum Emitters driven by Ultra-Short Laser Pulses

The development of high-performance quantum technologies relies on the ability to prepare the quantum states of solid-state emitters with high fidelity while cleanly separating the emitted photons from the driving field. Here, we present a comprehensive theoretical comparison of three single-pulse coherent control protocols: resonant Rabi oscillations, adiabatic rapid passage (ARP), and notch-filtered ARP (NARP). To establish an ideal performance baseline, we first map the parameter spaces for each protocol in a closed system, identifying the regions of robust population inversion. We then use a Lindblad master equation to compute the time-resolved emission spectra in the presence of decoherence. Our results show that while all three schemes can generate identical, transform-limited Lorentzian photons, NARP uniquely combines the high-fidelity robustness of adiabatic passage with intrinsic spectral separability. Our findings, which align with the work on NARP by Wilbur et al., establish a clear design framework for engineering the next generation of quantum light sources.

quant-ph↗

arXiv2024

MEMO-QCD: Quantum Density Estimation through Memetic Optimisation for Quantum Circuit Design

This paper presents a strategy for efficient quantum circuit design for density estimation. The strategy is based on a quantum-inspired algorithm for density estimation and a circuit optimisation routine based on memetic algorithms. The model maps a training dataset to a quantum state represented by a density matrix through a quantum feature map. This training state encodes the probability distribution of the dataset in a quantum state, such that the density of a new sample can be estimated by projecting its corresponding quantum state onto the training state. We propose the application of a memetic algorithm to find the architecture and parameters of a variational quantum circuit that implements the quantum feature map, along with a variational learning strategy to prepare the training state. Demonstrations of the proposed strategy show an accurate approximation of the Gaussian kernel density estimation method through shallow quantum circuits illustrating the feasibility of the algorithm for near-term quantum hardware.

quant-ph↗

arXiv2023

Directional spontaneous emission in photonic crystal slabs

Spontaneous emission is one of the most fundamental out-of-equilibrium processes in which an excited quantum emitter relaxes to the ground state due to quantum fluctuations. In this process, a photon is emitted that can interact with other nearby emitters and establish quantum correlations between them, e.g., via super and subradiance effects. One way to modify these photon-mediated interactions is to alter the dipole radiation patterns of the emitter, e.g., by placing photonic crystals near them. One recent example is the generation of strong directional emission patterns-key to enhancing super and subradiance effects-in two dimensions by employing photonic crystals with band structures characterized by linear isofrequency contours and saddle-points. However, these studies have predominantly used oversimplified toy models, overlooking the electromagnetic field's intricacies in actual materials, including aspects like geometrical dependencies, emitter positions, and polarization. Our study delves into the interaction between these directional emission patterns and the aforementioned variables, revealing the untapped potential to fine-tune collective quantum optical phenomena.

cond-mat.mes-hall↗

arXiv2023

An Empirical Study of Quantum Dynamics as a Ground State Problem with Neural Quantum States

We consider the Feynman-Kitaev formalism applied to a spin chain described by the transverse field Ising model. This formalism consists of building a Hamiltonian whose ground state encodes the time evolution of the spin chain at discrete time steps. To find this ground state, variational wave functions parameterised by artificial neural networks -- also known as neural quantum states (NQSs) -- are used. Our work focuses on assessing, in the context of the Feynman-Kitaev formalism, two properties of NQSs: expressivity (the possibility that variational parameters can be set to values such that the NQS is faithful to the true ground state of the system) and trainability (the process of reaching said values). We find that the considered NQSs are capable of accurately approximating the true ground state of the system, i.e., they are expressive enough ansätze. However, extensive hyperparameter tuning experiments show that, empirically, reaching the set of values for the variational parameters that correctly describe the ground state becomes ever more difficult as the number of time steps increase because the true ground state becomes more entangled, and the probability distribution starts to spread across the Hilbert space canonical basis.

quant-ph↗

arXiv2022

Road Network Deterioration Monitoring Using Aerial Images and Computer Vision

Road maintenance is an essential process for guaranteeing the quality of transportation in any city. A crucial step towards effective road maintenance is the ability to update the inventory of the road network. We present a proof of concept of a protocol for maintaining said inventory based on the use of unmanned aerial vehicles to quickly collect images which are processed by a computer vision program that automatically identifies potholes and their severity. Our protocol aims to provide information to local governments to prioritise the road network maintenance budget, and to be able to detect early stages of road deterioration so as to minimise maintenance expenditure.

cs.CV↗

arXiv2022

Optimisation-free Classification and Density Estimation with Quantum Circuits

We demonstrate the implementation of a novel machine learning framework for probability density estimation and classification using quantum circuits. The framework maps a training data set or a single data sample to the quantum state of a physical system through quantum feature maps. The quantum state of the arbitrarily large training data set summarises its probability distribution in a finite-dimensional quantum wave function. By projecting the quantum state of a new data sample onto the quantum state of the training data set, one can derive statistics to classify or estimate the density of the new data sample. Remarkably, the implementation of our framework on a real quantum device does not require any optimisation of quantum circuit parameters. Nonetheless, we discuss a variational quantum circuit approach that could leverage quantum advantage for our framework.

quant-ph↗

arXiv2022

Dark Exciton Giant Rabi Oscillations with no External Magnetic Field

Multi-phonon physics is an emerging field that serves as a test bed for fundamental quantum physics and several applications in metrology, on-chip communication, among others. Quantum acoustic cavities or resonators are devices that are being used to study multi-phonon phenomena both theoretically and experimentally. In particular, we study a system consisting of a semiconductor quantum dot pumped by a driving laser, and coupled to an acoustic cavity. This kind of systems has proven to yield interesting multi-phonon phenomena, but the description of the quantum dot has been limited to a two-level system. This limitation restrains the complexity that a true semiconductor quantum dot can offer. Instead, in this work we consider a model where the quantum dot can have both bright and dark excitons, the latter being particularly useful due to their lower decoherence rates, because they do not present spontaneous photon emission. In this setup, we demonstrate that by fine-tuning the driving laser frequency, one is able to realise giant Rabi oscillations between the vacuum state and a dark exciton state with $N$-phonon bundles. From this, we highlight two outstanding features: first, we are able to create dark states excitations in the quantum dot without the usual external magnetic field needed to do so; and second, in a dissipative scenario where the acoustic cavity and the quantum dot suffer from losses, the system acts as a phonon gun able to emit $N$-phonon bundles.

cond-mat.mes-hall↗

arXiv2021

Many-Qudit representation for the Travelling Salesman Problem Optimisation

We present a map from the travelling salesman problem (TSP), a prototypical NP-complete combinatorial optimisation task, to the ground state associated with a system of many-qudits. Conventionally, the TSP is cast into a quadratic unconstrained binary optimisation (QUBO) problem, that can be solved on an Ising machine. The size of the corresponding physical system's Hilbert space is $2^{N^2}$, where $N$ is the number of cities considered in the TSP. Our proposal provides a many-qudit system with a Hilbert space of dimension $2^{N\log_2N}$, which is considerably smaller than the dimension of the Hilbert space of the system resulting from the usual QUBO map. This reduction can yield a significant speedup in quantum and classical computers. We simulate and validate our proposal using variational Monte Carlo with a neural quantum state, solving the TSP in a linear layout for up to almost 100 cities.

quant-ph↗

arXiv2021

Green's functions in quantum mechanics courses

Green's functions in Physics have proven to be a valuable tool for understanding fundamental concepts in different branches, such as electrodynamics, solid-state and many -body problems. In quantum mechanics advanced courses, Green's functions usually are explained in the context of the scattering problem by a central force. However, their use for more basic problems is not often implemented. The present work introduces Green's Function in quantum mechanics courses with some examples that can be solved with essential tools. For this, the general aspects of the theory are shown, emphasizing the solution of different fundamental issues of quantum mechanics from this approach. In particular, we introduce the time-independent Green's functions and the Dyson equation to solve problems with an external potential. As examples, we show the scattering by a Dirac delta barrier, where the reflection and transmission coefficients are found. In addition, the infinite square potential well energy levels, and the local density of states, are calculated.

physics.ed-ph↗

arXiv2021

Photon-mediated interactions near a Dirac photonic crystal slab

Dirac energy-dispersions are responsible of the extraordinary transport properties of graphene. This motivated the quest for engineering such energy dispersions also in photonics, where they have been predicted to lead to many exciting phenomena. One paradigmatic example is the possibility of obtaining power-law, decoherence-free, photon-mediated interactions between quantum emitters when they interact with such photonic baths. This prediction, however, has been obtained either by using toy-model baths, which neglect polarization effects, or by restricting the emitter position to high-symmetry points of the unit cell in the case of realistic structures. Here, we develop a semi-analytical theory of dipole radiation near photonic Dirac points in realistic structures that allows us to compute the effective photon-mediated interactions along the whole unit cell. Using this theory, we are able to find the positions that maximize the emitter interactions and their range, finding a trade-off between them. Besides, using the polarization degree of freedom, we also find positions where the nature of the collective interactions change from being coherent to dissipative ones. Thus, our results significantly improve the knowledge of Dirac light-matter interfaces, and can serve as a guidance for future experimental designs.

cond-mat.mes-hall↗

arXiv2021

Supervised Learning with Quantum Measurements

This paper reports a novel method for supervised machine learning based on the mathematical formalism that supports quantum mechanics. The method uses projective quantum measurement as a way of building a prediction function. Specifically, the relationship between input and output variables is represented as the state of a bipartite quantum system. The state is estimated from training samples through an averaging process that produces a density matrix. Prediction of the label for a new sample is made by performing a projective measurement on the bipartite system with an operator, prepared from the new input sample, and applying a partial trace to obtain the state of the subsystem representing the output. The method can be seen as a generalization of Bayesian inference classification and as a type of kernel-based learning method. One remarkable characteristic of the method is that it does not require learning any parameters through optimization. We illustrate the method with different 2-D classification benchmark problems and different quantum information encodings.

quant-ph↗

arXiv2020

Phase diagram reconstruction of the Bose-Hubbard model with a Restricted Boltzmann Machine wavefunction

Recently, the use of neural quantum states for describing the ground state of many- and few-body problems has been gaining popularity because of their high expressivity and ability to handle intractably large Hilbert spaces. In particular, methods based on variational Monte Carlo have proven to be successful in describing the physics of bosonic systems such as the Bose-Hubbard (BH) model. However, this technique has not been systematically tested on the parameter space of the BH model, particularly at the boundary between the Mott insulator and superfluid phases. In this work, we evaluate the capabilities of variational Monte Carlo with a trial wavefunction given by a Restricted Boltzmann Machine to reproduce the quantum ground state of the BH model on several points of its parameter space. To benchmark the technique, we compare its results to the ground state found through exact diagonalization for small one-dimensional chains. In general, we find that the learned ground state correctly estimates many observables, reproducing to a high degree the phase diagram for the first Mott lobe and part of the second one. However, we find that the technique is challenged whenever the system transitions between excitation manifolds, as the ground state is not learned correctly at these boundaries. We improve the quality of the results produced by the technique by proposing a method to discard noisy probabilities learned in the ground state.

cond-mat.dis-nn↗

arXiv2019

Event detection in Colombian security Twitter news using fine-grained latent topic analysis

Cultural and social dynamics are important concepts that must be understood in order to grasp what a community cares about. To that end, an excellent source of information on what occurs in a community is the news, especially in recent years, when mass media giants use social networks to communicate and interact with their audience. In this work, we use a method to discover latent topics in tweets from Colombian Twitter news accounts in order to identify the most prominent events in the country. We pay particular attention to security, violence and crime-related tweets because of the violent environment that surrounds Colombian society. The latent topic discovery method that we use builds vector representations of the tweets by using FastText and finds clusters of tweets through the K-means clustering algorithm. The number of clusters is found by measuring the $C_V$ coherence for a range of number of topics of the Latent Dirichlet Allocation (LDA) model. We finally use Uniform Manifold Approximation and Projection (UMAP) for dimensionality reduction to visualise the tweets vectors. Once the clusters related to security, violence and crime are identified, we proceed to apply the same method within each cluster to perform a fine-grained analysis in which specific events mentioned in the news are grouped together. Our method is able to discover event-specific sets of news, which is the baseline to perform an extensive analysis of how people engage in Twitter threads on the different types of news, with an emphasis on security, violence and crime-related tweets.

cs.SI↗

arXiv2019

Using machine learning and information visualisation for discovering latent topics in Twitter news

We propose a method to discover latent topics and visualise large collections of tweets for easy identification and interpretation of topics, and exemplify its use with tweets from a Colombian mass media giant in the period 2014--2019. The latent topic analysis is performed in two ways: with the training of a Latent Dirichlet Allocation model, and with the combination of the FastText unsupervised model to represent tweets as vectors and the implementation of K-means clustering to group tweets into topics. Using a classification task, we found that people respond differently according to the various news topics. The classification tasks consists of the following: given a reply to a news tweet, we train a supervised algorithm to predict the topic of the news tweet solely from the reply. Furthermore, we show how the Colombian peace treaty has had a profound impact on the Colombian society, as it is the topic in which most people engage to show their opinions.

cs.SI↗

arXiv2019

Light emission properties in a double quantum dot molecule immersed in a cavity: phonon-assisted tunneling

Two main mechanisms dictate the tunneling process in a double quantum dot: overlap of excited wave functions, effectively described as a tunneling rate, and phonon-assisted tunneling. In this paper, we study different regimes of tunneling that arise from the competition between these two mechanisms in a double quantum dot molecule immersed in a unimodal optical cavity. We show how such regimes affect the mean number of excitations in each quantum dot and in the cavity, the spectroscopic resolution and emission peaks of the photoluminescence spectrum, and the second-order coherence function which is an indicator of the quantumness of emitted light from the cavity.

quant-ph↗

arXiv2019

Pure dephasing vs. Phonon mediated off-resonant coupling in a quantum-dot-cavity system

Pure dephasing is widely used in the literature to explain experimental observations on quantum dots in cavities. In many cases, its use is not enough and extra terms need to be "fictitiously" added to accomplish with the observed data as it is the case of cavity pumping of an unknown source. Here we controvert the validity of the pure dephasing mechanism as a source of decoherence and present a theoretical study based on the phonon-mediated coupling that can explain the emission spectrum and photon auto- and cross-correlation results in recent experiments without the need of any artificial assumptions. We also demonstrated that the phonon-mediated coupling accounts for unexplained features recently reported in measurements of photon auto- and cross-correlation functions. Our work illuminates many of the debates in this field and opens up new possibilities for experimental verification and theoretical predictions.

cond-mat.mes-hall↗