arXiv subjects

Diego Tancara

Publications and source records attributed to Diego Tancara.

13 recordsLinked to original sources

arXiv2026

Qudit-ADAPT-VQE: an adaptive variational algorithm with counterdiabatic-inspired improvements for qudits

Variational quantum algorithms based on qudits have attracted significant attention in recent years. However, as in their qubit-based counterparts, challenges such as barren plateaus and the design of efficient ansatz remain major obstacles. In this work, we propose to address these issues through a qudit implementation of the ADAPT-VQE algorithm, which constructs the ansatz iteratively. Specifically, we introduce an operator pool inspired by adiabatic evolution enhanced with counterdiabatic driving for ansatz construction and employ it to solve Max 3-Cut. We show that the warm-start strategy inherent to ADAPT-VQE, together with an ansatz construction based on counterdiabatic operators, achieves higher accuracy and lower native gates implementation than approaches on fixed ansatz. Furthermore, we show that, in qudit-based quantum computing, ADAPT-VQE with a counterdiabatic operator pool can navigate rough optimization landscapes with local traps through the burrowing mechanism, suggesting robustness against barren plateau effects and providing a scalable framework for variational quantum algorithms with qudits.

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arXiv2026

Single excitation swap in a modified Jaynes-Cummings-Hubbard lattice

Controlling the transport and nature of quantum excitations in low-dimensional systems is a key requirement for scalable quantum devices, including communication networks and quantum simulators. We propose a one-dimensional hybrid quantum lattice model, in which each lattice unit integrates a single-mode resonator that interacts with a two-level system (TLS), featuring direct coupling between adjacent TLSs. This configuration enables the coherent propagation of excitations with tunable atomic, photonic, or polaritonic character. Beyond conventional single-excitation transport, we demonstrate that appropriate impedance-matching and resonance conditions allow for the controlled swapping of excitation type as the excitation propagates along the lattice. We analyze the resulting dynamics using local observables and pairwise concurrence to track both transport and quantum correlations. Our results establish a minimal platform for controlled single-excitation conversion, with direct relevance to hybrid quantum networks, on-chip quantum interconnects, and engineered quantum simulators.

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arXiv2026

Counterdiabatic ADAPT-VQE for molecular simulation

Among variational quantum algorithms designed for NISQ devices, ADAPT-VQE stands out for its robustness against barren plateaus, particularly in estimating molecular ground states. On the other hand, counterdiabatic algorithms have shown advantages in both performance and circuit depth when compared to standard adiabatic approaches. In this work, we propose a hybrid method that integrates the ADAPT-VQE framework with counterdiabatic driving within an adiabatic evolution scheme. Specifically, we map the molecular Hamiltonian to a qubit representation and construct an adiabatic Hamiltonian, from which an approximate adiabatic gauge potential is computed using nested commutators. The resulting operator terms define the operator pool, and the ADAPT-VQE algorithm is applied to iteratively select the most relevant elements for the ansatz. Our results demonstrate improvements in performance and reductions in circuit depth compared to using either counterdiabatic algorithms or ADAPT-VQE with fermionic excitation operators, thus supporting the effectiveness of combining both paradigms in molecular simulations.

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arXiv2025

Entanglement generation in qubit-ADAPT-VQE through four-qubit algebraic classification

While variational quantum algorithms are among the most promising approaches for the noisy intermediate-scale quantum (NISQ) era, their scalability is often hindered by the barren plateau problem. Among the proposals that have demonstrated robustness against this issue, the ADAPT-VQE algorithm stands out for ground state estimation, primarily due to its iterative ansatz construction. Although ADAPT-VQE has been extensively benchmarked on molecular Hamiltonians, where the ground states typically exhibit low entanglement, its performance for highly entangled ground states remains largely unexplored. In this work, we explore a variant of this algorithm known as qubit-ADAPT-VQE, assessing its ability to achieve ground states with substantial entanglement in spin models. We focus on four-qubit systems and employ an algebraic entanglement classification to identify distinct entanglement classes among ground states, and consider a representative of each class as an initial state to evaluate the performance of the algorithm. Our findings highlight the versatility of qubit-ADAPT-VQE, demonstrating that it accurately reaches the ground state across all entanglement classes and initial energy values.

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arXiv2025

High-dimensional counterdiabatic quantum computing

The digital version of adiabatic quantum computing enhanced by counterdiabatic driving, known as digitized counterdiabatic quantum computing, has emerged as a paradigm that opens the door to fast and low-depth algorithms. In this work, we explore the extension of this paradigm to high-dimensional systems. Specifically, we consider qutrits in the context of quadratic problems, obtaining the qutrit Hamiltonian codifications and the counterdiabatic drivings. Our findings show that qutrits can improve the solution quality up to 90 times compared to the qubit counterpart. We tested our proposal on 1000 random instances of the multiway number partitioning, max 3-cut, and portfolio optimization problems, demonstrating that, in general, without prior knowledge, it is better to use qutrits and, apparently, high-dimensional systems in general instead of qubits. Finally, considering the state-of-the-art quantum platforms, we show the experimental feasibility of our high-dimensional counterdiabatic quantum algorithms at least in a fully digital form. This work paves the way for the efficient codification of optimization problems in high-dimensional spaces and their efficient implementation using counterdiabatic quantum computing.

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arXiv2025

Quantum coherence and counterdiabatic quantum computing

Counterdiabatic driving emerges as a valuable technique for implementing shortcuts to adiabaticity protocols, enhancing quantum technology applications. In this context, counterdiabatic quantum computing represents a new paradigm with the potential to achieve quantum advantage for industrial problems. This work investigates the production of quantum coherence in adiabatic evolution accelerated by counterdiabatic driving within the framework of counterdiabatic quantum computing. Specifically, we analyze different orders in the nested commutator expansion for approximated counterdiabatic drivings for three cases: a weighted max-cut problem, a 4-local Hamiltonian, and a non-stoquastic Hamiltonian. Our findings reveal that the hierarchy introduced by coherence production correlates with the success probability in the impulse regime. This suggests that protocols increasing coherence during evolution enhance performance in adiabatic evolution driven by counterdiabatic techniques. We show that large quantum coherence also means large energy fluctuation during evolution, which is associated with the speed of evolution, paving the way for designing superior algorithms in counterdiabatic quantum computing.

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arXiv2024

Magnetic and phonon-induced effects on the non-Markovian dynamics of a single solid-state defect

The electron-phonon interaction is one of the most fundamental mechanisms in condensed matter physics. Phonons can induce memory effects in solid-state platforms when localized electronic states interact with lattice vibrations in non-unitary dynamical maps. In this work, we demonstrate how single-mode and structured phonon environments can give rise to non-Markovian dynamics of an individual negatively charged silicon-vacancy center in diamond. Using trace distance as a quantifier via numerical simulations and theoretical calculations, we identify the physical conditions for emerging and understanding non-Markovian behavior in diverse scenarios. Most importantly, we investigate the influence of magnetic fields (longitudinal and transverse), phonon couplings, Fock states, and temperature to understand how these factors influence memory effects in this solid-state device.

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arXiv2024

Quantum kernels for classifying dynamical singularities in a multiqubit system

Dynamical quantum phase transition is a critical phenomenon involving out-of-equilibrium states and broken symmetries without classical analogy. However, when finite-sized systems are analyzed, dynamical singularities of the rate function can appear, leading to a challenging physical characterization when parameters are changed. Here, we report a quantum support vector machine (QSVM) algorithm that uses quantum Kernels to classify dynamical singularities of the rate function for a multiqubit system. We illustrate our approach using $N$ long-range interacting qubits subjected to an arbitrary magnetic field, which induces a quench dynamics. Inspired by physical arguments, we introduce two different quantum Kernels, one inspired by the ground state manifold and the other based on a single state tomography. Our accuracy and adaptability results show that this quantum dynamical critical problem can be efficiently solved using physically inspiring quantum Kernels. Moreover, we extend our results for the case of time-dependent fields, quantum master equation, and when we increase the number of qubits.

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arXiv2022

Engineering non-Markovianity from defect-phonon interactions

Understanding defect-phonon interactions in solid-state devices is crucial for improving our current knowledge of quantum platforms. In this work, we develop first-principles calculations for a defect composed of two spin-$1/2$ particles that interact with phonon modes in a one-dimensional lattice. We follow a bottom-up approach that begins with a dipolar magnetic interaction to ultimately derive the spectral density function and time-local master equation that describes the open dynamics of the defect. We provide theoretical and numerical analysis for the non-Markovian features of the defect-phonon dynamics induced by a pure dephasing channel acting on the Bell basis. Finally, we analyze two measures of non-Markovianity based on the canonical rates and Coherence, shedding more light on the role of the spectral density function and temperature; and envisioning experimental realizations.

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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.

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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.

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arXiv2021

Steering Interchange of Polariton Branches via Coherent and Incoherent Dynamics

Controlling light-matter based quantum systems in the strong coupling regime allows for exploring quantum simulation of many-body physics in nowadays architectures. For instance, the atom-field interaction in a cavity QED network provides control and scalability for quantum information processing. Here, we propose the control of single- and two-body Jaynes-Cummings systems in a non-equilibrium scenario, which allows us to establish conditions for the coherent and incoherent interchange of polariton branches. Our findings provide a systematic approach to manipulate polaritons interchange, that we apply to reveal new insights in the transition between Mott Insulator- and Superfluid-like states. Furthermore, we study the asymmetry in the absorption spectrum by triggering the cavity and atomic losses as a function of the atom-cavity detuning and photon's hopping.

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arXiv2020

Coding closed and open quantum systems in MATLAB: applications in quantum optics and condensed matter

We develop a package of numerical simulations implemented in MATLAB to solve complex many-body quantum systems. We focus on widely used examples that include the calculation of the magnetization dynamics for the closed and open Ising model, dynamical quantum phase transition in cavity QED arrays, Markovian dynamics for interacting two-level systems, and the non-Markovian dynamics of the pure-dephasing spin-boson model. These examples will be useful for undergraduate and graduate students with a medium or high background in MATLAB, and also for researches interested in numerical studies applied to quantum optics and condensed matter systems.

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