arXiv subjects

Felipe F. Fanchini

Publications and source records attributed to Felipe F. Fanchini.

At least 19 recordsLinked to original sources

arXiv2026

Preparing a Thermofield Double State with Feedback Quantum Algorithms

The efficient preparation of correlated thermal states, such as the Thermofield Double (TFD) state, is a fundamental prerequisite for simulating quantum gravity models and many-body thermodynamics on quantum processors. In this work, we investigate the ground state preparation of the Two Coupled Sachdev-Ye-Kitaev model, known as the Maldacena-Qi model, which is dual to a traversable wormhole in $AdS_2$, utilizing feedback-based quantum algorithms. We demonstrate that the standard feedback-based quantum algorithm (FALQON) and its time-rescaled variant (TR-FALQON) face severe kinetic limitations in this system, failing to converge to the highly entangled ground state when initialized in trivial product states. To overcome these barriers, we propose the hybrid ITE-TR-FALQON protocol, which integrates the imaginary-time evolution present in imaginary-time-enhanced FALQON (ITE-FALQON) with the time-rescaling mechanism. Our numerical results indicate that the introduction of non-unitary dynamics is strictly necessary to break symmetry traps and filter out excited states, while time-rescaling drastically accelerates algorithm convergence. The proposed method achieves fidelities close to unity and reproduces the von Neumann and Rényi entropy spectra of the exact TFD state with high precision.

hep-th↗

arXiv2026

PUBO Formulation for MST and Application to Optimum-Path Forest

The Optimum-Path Forest is a graph-based framework for designing classifiers that exploit inter-sample connectivity. A particular variant constructs decision boundaries based on prototypes computed by a Minimum Spanning Tree (MST) over the training data, which might become prohibitive for large-scale datasets. In this context, Quantum Machine Learning has emerged as a promising approach to overcome the high computational burden of combinatorial problems. We propose a quantum-inspired approach for prototype selection in OPF classifiers by reformulating the MST problem as a Polynomial Unconstrained Binary Optimization (PUBO) task and further employing the Feedback-Based Quantum Optimization (FALQON) algorithm for Hamiltonian minimization. The PUBO formulation reduces the need for qubits and eliminates the need for auxiliary variables, thereby addressing scalability constraints in current quantum hardware. Experiments on real-world datasets demonstrate that the FALQON-optimized MST achieves accuracies comparable to those of the classical Prim's algorithm while maintaining prototype quality. While FALQON occasionally reached local minima, it did not significantly impact the accuracy of the prototype selection process.

quant-ph↗

arXiv2026

Quantum feedback algorithms for DNA assembly using FALQON variants

Reconstructing DNA sequences without a reference, known as de novo assembly, is a complex computational task involving the alignment of overlapping fragments. To address this problem, a usual strategy is to map the assembly to a Quadratic Unconstrained Binary Optimization (QUBO) formulation, which can be solved by different quantum algorithms. In this work, we focus on three versions of the Feedback-based Algorithm, a protocol that eliminates classical optimization loops via measurement feedback. We analyze long-read DNA fragments from SARS-CoV-2 and human mitochondrial DNA using standard FALQON, second-order FALQON (SO-FALQON), and time-rescaled FALQON (TR-FALQON). Numerical results show that both variants improve convergence to the ground state and increase success probabilities at reduced circuit depths. These findings indicate that enhanced feedback-driven dynamics are effective for solving combinatorial problems on near-term quantum hardware.

quant-ph↗

arXiv2026

Measurement-Guided State Refinement for Shallow Feedback-Based Quantum Optimization Algorithm

Limited circuit depth remains a central constraint for quantum optimization in the noisy intermediate-scale quantum (NISQ) regime, where shallow unitary dynamics may fail to sufficiently concentrate probability on low-energy configurations. We introduce Measurement-Guided Initialization (MGI), an iterative strategy that uses measurement outcomes from previous executions to update the initialization of subsequent runs. The method extracts single-qubit marginal probabilities from dominant measurement outcomes and prepares a biased product-state initialization, allowing information obtained during optimization to be reused without introducing classical parameter optimization. We implement this approach in the context of the Feedback-Based Algorithm for Quantum Optimization (FALQON) and evaluate its performance on weighted MaxCut instances. Numerical results show that measurement-guided initialization improves the performance of shallow-depth circuits and enables iterative refinement toward high-quality solutions while preserving the non-variational structure of the algorithm. These results indicate that measurement statistics can be exploited to improve shallow quantum optimization protocols compatible with NISQ devices.

quant-ph↗

arXiv2025

Regression of Functions by Quantum Neural Networks Circuits

The performance of quantum neural network models depends strongly on architectural decisions, including circuit depth, placement of parametrized operations, and data-encoding strategies. Selecting an effective architecture is challenging and closely related to the classical difficulty of choosing suitable neural-network topologies, which is computationally hard. This work investigates automated quantum-circuit construction for regression tasks and introduces a genetic-algorithm framework that discovers Reduced Regressor QNN architectures. The approach explores depth, parametrized gate configurations, and flexible data re-uploading patterns, formulating the construction of quantum regressors as an optimization process. The discovered circuits are evaluated against seventeen classical regression models on twenty-two nonlinear benchmark functions and four analytical functions. Although classical methods often achieve comparable results, they typically require far more parameters, whereas the evolved quantum models remain compact while providing competitive performance. We further analyze dataset complexity using twelve structural descriptors and show, across five increasingly challenging meta-learning scenarios, that these measures can reliably predict which quantum architecture will perform best. The results demonstrate perfect or near-perfect predictive accuracy in several scenarios, indicating that complexity metrics offer powerful and compact representations of dataset structure and can effectively guide automated model selection. Overall, this study provides a principled basis for meta-learning-driven quantum architecture design and advances the understanding of how quantum models behave in regression settings--a topic that has received limited exploration in prior work. These findings pave the way for more systematic and theoretically grounded approaches to quantum regression.

quant-ph↗

arXiv2025

Warm-Starting PCE for Traveling Salesman Problem

Variational quantum algorithms are promising for combinatorial optimization, but their scalability is often limited by qubit-intensive encoding schemes. To overcome this bottleneck, Pauli Correlation Encoding (PCE) has emerged as one of the most promising algorithms in this scenario. The method offers not only a polynomial reduction in qubit count and a suppression of barren plateaus but also demonstrates competitive performance with state-of-the-art methods on Maxcut. In this work, we propose a warm-start PCE, an extension that incorporates a classical bias from the Goemans-Williamson (GW) randomized rounding algorithm into the loss function to guide the optimization toward improved approximation ratios. We evaluated this method on the Traveling Salesman Problem (TSP) using a QUBO-to-MaxCut transformation for up to $5$ layers. Our results show that Warm-PCE consistently outperforms standard PCE, achieving the optimum solution in $28\text{--}64\%$ of instances, versus $4\text{--}26\%$ for PCE, and attaining higher mean approximation ratios that improve with circuit depth. These findings highlight the practical value of this warm-start strategy for enhancing PCE-based solvers on near-term hardware.

quant-ph↗

arXiv2025

Warm-Starting QAOA with XY Mixers: A Novel Approach for Quantum-Enhanced Vehicle Routing Optimization

Quantum optimization algorithms, such as the Quantum Approximate Optimization Algorithm, are emerging as promising heuristics for solving complex combinatorial problems. To improve performance, several extensions to the standard QAOA framework have been proposed in recent years. Two notable directions include: warm-starting techniques, which incorporate classical approximate solutions to guide the quantum evolution, and custom mixer Hamiltonians, such as XY mixers, which constrain the search to feasible subspaces aligned with the structure of the problem. In this work, we propose an approach that integrates these two strategies: a warm-start initialization with an XY mixer ansatz, enabling constraint-preserving quantum evolution biased toward high-quality classical solutions. The method begins by reformulating the combinatorial problem as a MaxCut instance, solved approximately using the Goemans-Williamson algorithm. The resulting binary solution is relaxed and used to construct a biased superposition over valid one-hot quantum states, maintaining compatibility with the XY mixer's constraints. We evaluate the approach on 5-city instances of the Traveling Salesperson Problem, a canonical optimization problem frequently encountered as a subroutine in real-world Vehicle Routing Problems. Our method is benchmarked against both the standard XY-mixer QAOA and a warm-start-only variant based on MaxCut relaxation. Results show that the proposed combination consistently outperforms both baselines in terms of the percentage and rank of optimal solutions, demonstrating the effectiveness of combining structured initializations with constraint-aware quantum evolution for optimization problems.

quant-ph↗

arXiv2025

Quantum Phases Classification Using Quantum Machine Learning with SHAP-Driven Feature Selection

In this study, we present an innovative methodology to classify quantum phases within the ANNNI (Axial Next-Nearest Neighbor Ising) model by combining Quantum Machine Learning (QML) techniques with the Shapley Additive Explanations (SHAP) algorithm for feature selection and interpretability. Our investigation focuses on two prominent QML algorithms: Quantum Support Vector Machines (QSVM) and Variational Quantum Classifiers (VQC). By leveraging SHAP, we systematically identify the most relevant features within the dataset, ensuring that only the most informative variables are utilized for training and testing. The results reveal that both QSVM and VQC exhibit exceptional predictive accuracy when limited to 5 or 6 key features, thereby enhancing performance and reducing computational overhead. This approach not only demonstrates the effectiveness of feature selection in improving classification outcomes but also offers insights into the interpretability of quantum classification tasks. The proposed framework exemplifies the potential of interdisciplinary solutions for addressing challenges in the classification of quantum systems, contributing to advancements in both machine learning and quantum physics.

quant-ph↗

arXiv2023

Physics informed neural networks learning a two-qubit Hamiltonian

Machine learning techniques are employed to perform the full characterization of a quantum system. The particular artificial intelligence technique used to learn the Hamiltonian is called physics informed neural network (PINN). The idea behind PINN is the universal approximation theorem, which claims that any function can be approximate by a neural network if it contains enough complexity. Consequently, a neural network can be a solution of a physical model. Moreover, by means of extra data provided by the user, intrinsic physical parameters can be extracted from the approach called inverse-PINN. Here, we apply inverse-PINN with the goal of extracting all the physical parameters that constitutes a two qubit Hamiltonian. We find that this approach is very efficient. To probe the robustness of the inverse-PINN to learn the Hamiltonian of a two-qubit system, we use the IBM quantum computers as experimental platforms to obtain the data that is plugged in the PINN. We found that our method is able to predict the two-qubit parameters with 5% of accuracy on average.

quant-ph↗

arXiv2023

Effectiveness of the Krotov method in controlling open quantum systems

We apply the Krotov method for open and closed quantum systems with the objective of finding optimized controls to manipulate qubit/qutrit systems in the presence of the external environment. In the case of unitary optimization, the Krotov method is first applied to a quantum system neglecting its interaction with the environment. The resulting controls from the unitary optimization are then used to drive the system along with the environmental noise. In the case of non-unitary optimization, the Krotov method already takes into account the noise during the optimization process. We consider two distinct computational task: target-state preparation and quantum gate implementation. These tasks are carried out in simple qubit/qutrit systems and also in systems presenting leakage states. For the state-preparation cases, the controls from the non-unitary optimization outperform the controls from the unitary optimization. However, as we show here, this is not always true for the implementation of quantum gates. There are some situations where the unitary optimization performs equally well compared to the non-unitary optimization. We verify that these situations corresponds to either the absence of leakage states or to the effects of dissipation being spread uniformly over the system, including non-computational levels. For such cases, the quantum gate implementation must cover the entire Hilbert space and there is no way to dodge dissipation. On the other hand, if the subspace containing the computational levels and its complement are differently affected by dissipation, the non-unitary optimization becomes effective.

quant-ph↗

arXiv2022

Estimating the degree of non-Markovianity using variational quantum circuits

Several applications of quantum machine learning (QML) rely on a quantum measurement followed by training algorithms using the measurement outcomes. However, recently developed QML models, such as variational quantum circuits (VQCs), can be implemented directly on the state of the quantum system (quantum data). Here, we propose to use a qubit as a probe to estimate the degree of non-Markovianity of the environment. Using VQCs, we find an optimal sequence of qubit-environment interactions that yield accurate estimations of the degree of non-Markovianity for the amplitude damping, phase damping, and the combination of both models. We introduce a problem-based ansatz that optimizes upon the probe qubit and the interaction time with the environment. This work contributes to practical quantum applications of VQCs and delivers a feasible experimental procedure to estimate the degree of non-Markovianity.

quant-ph↗

arXiv2022

Kernel-based quantum regressor models learn non-Markovianity

Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlap between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum dataset. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlap between quantum states. We show that our models deliver accurate predictions that are comparable with the fully classical models.

quant-ph↗

arXiv2021

Machine classification for probe based quantum thermometry

We consider probe-based quantum thermometry and show that machine classification can provide model-independent estimation with quantifiable error assessment. Our approach is based on the k-nearest-neighbor algorithm. The machine is trained using data from either computer simulations or a calibration experiment. This yields a predictor which can be used to estimate the temperature from new observations. The algorithm is highly flexible and works with any kind of probe observable. It also allows to incorporate experimental errors, as well as uncertainties about experimental parameters. We illustrate our method with an impurity thermometer in a Bose-gas, as well as in the estimation of the thermal phonon number in the Rabi model.

quant-ph↗

arXiv2021

Model-Independent Quantum Phases Classifier

Machine learning has revolutionized many fields of science and technology. Through the $k$-Nearest Neighbors algorithm, we develop a model-independent classifier, where the algorithm can classify phases of a model to which it has never had access. For this, we study three distinct spin-$1$ models with some common phases: the XXZ chains with uniaxial single-ion-type anisotropy, the bound alternating XXZ chains, and the bilinear biquadratic chain. We show that, with high probability, algorithms trained with two of these models can determine common phases with the third. It is the first step toward a universal classifier, where an algorithm is able to detect any phase with no knowledge about the Hamiltonian, only knowing partial information about the quantum state.

quant-ph↗

arXiv2021

Estimating the degree of non-Markovianity using machine learning

In the last years, the application of machine learning methods has become increasingly relevant in different fields of physics. One of the most significant subjects in the theory of open quantum systems is the study of the characterization of non-Markovian memory effects that emerge dynamically throughout the time evolution of open systems as they interact with their surrounding environment. Here we consider two well-established quantifiers of the degree of memory effects, namely, the trace distance and the entanglement-based measures of non-Markovianity. We demonstrate that using machine learning techniques, in particular, support vector machine algorithms, it is possible to estimate the degree of non-Markovianity in two paradigmatic open system models with high precision. Our approach can be experimentally feasible to estimate the degree of non-Markovianity, since it requires a single or at most two rounds of state tomography.

quant-ph↗

arXiv2017

Non-Markovianity Quantifier of an Arbitrary Quantum Process

Calculating the degree of non-Markovianity of a dissipative process is a difficult task, even for the dynamics of a single qubit, given the complex maximization problem. In this work, focusing on the entanglement-based quantifier of non-Markovianity, we present an analytical solution for such an optimization problem. We then propose a computable non-Markovianity measure based on generalized robustness of entanglement, an entanglement measure that can be readily calculated by a semidefinite programming method. We show that the non-Markovianity, in a given interval of time, can be witnessed by calculating the expectation value of an observable, making it attractive for experimental investigations.

quant-ph↗

arXiv2016

Time-invariant entanglement and sudden death of non-locality

We investigate both theoretically and experimentally the dynamics of entanglement and non-locality for two qubits immersed in a global pure dephasing environment. We demonstrate the existence of a class of states for which entanglement is forever frozen during the dynamics, even if the state of the system does evolve. At the same time non-local correlations, quantified by the violation of the Clauser-Horne-Shimony-Holt (CHSH) inequality, either undergo sudden death or are trapped during the dynamics.

quant-ph↗

arXiv2015

Quantum Discord for $d\otimes2$ Systems

We present an analytical solution for classical correlation, defined in terms of linear entropy, in an arbitrary $d\otimes 2$ system when the second subsystem is measured. We show that the optimal measurements used in the maximization of the classical correlation in terms of linear entropy, when used to calculate the quantum discord in terms of von Neumann entropy, result in a tight upper bound for arbitrary $d\otimes 2$ systems. This bound agrees with all known analytical results about quantum discord in terms of von Neumann entropy and, when comparing it with the numerical results for $10^6$ two-qubit random density matrices, we obtain an average deviation of order $10^{-4}$. Furthermore, our results give a way to calculate the quantum discord for arbitrary $n$-qubit GHZ and W states evolving under the action of the amplitude damping noisy channel.

quant-ph↗