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Eduardo I. Duzzioni

Publications and source records attributed to Eduardo I. Duzzioni.

At least 19 recordsLinked to original sources

Global Entanglement Quantification via Classical Shadows

Detecting and characterizing quantum correlations are tasks of great relevance in quantum information. More specifically, quantifying the amount of multipartite entanglement is a known difficult task, even for pure states. To this end, several entanglement measures have been proposed, although there is currently no universal manner to do so. In this work, we propose the classical shadows technique to measure the generalized global entanglement introduced in Phys. Rev. A 74, 022314. This particular multipartite entanglement quantifier $E_G^{(n)}$ relies on the linear entropies of all $n$-qubit partitions of a state. As the linear entropy can be written in terms of a complete set of observables of the subsystem, we employ classical shadows to estimate many observables with fewer measurements. We simulate the quantification of $E_G^{(n)}$ for well-known entangled states and for random states, comparing shadow estimations and grouping techniques. Our results show a clear advantage of classical shadows over direct estimation approaches, indicating that it can be a useful tool to quantify entanglement without the need to reconstruct the density operator of the whole system.

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Time rescaling for second-order feedback-based quantum optimization

Feedback-based quantum algorithms recently gained attention by demonstrating how quantum computers can be used to solve optimization problems without resorting to hybrid quantum-classical architectures. Recent progress made with Feedback-based Algorithm for Quantum Optimization (FALQON) variants significantly reduced the depth of the required circuits. In this work, we merge the time-rescaled and second-order FALQON variants into a unified framework. The results show an important improvement over past modifications, reducing circuit depth and allowing a more flexible choice of time-steps, while maintaining stable solutions. This FALQON variant makes it suitable for the NISQ era.

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Continuous-time quantum walks on a defective lattice: Boosting the spreading of delocalized states through Parrondo's strategy

We investigate the quantum transport of delocalized states in continuous-time quantum walks (CTQWs) on a one-dimensional lattice containing a single defect. The defect is modeled by assigning complex-valued hopping amplitudes to the edges that connect the site corresponding to the mean position of the delocalized initial state to its nearest neighbors. We find that this single defective site is sufficient to enhance the ballistic spreading of an initially Gaussian wave packet. Extending these results, we implement a time-dependent alternation protocol between two distinct defect configurations, each individually yielding poor propagation of the state. The combination of these two unfavorable configurations improves the transport efficiency of the quantum walker, revealing a manifestation of Parrondo's paradox in CTQWs with delocalized initial states. This study provides insights into the role of complex-phase defects and time-dependent protocols in CTQWs, demonstrating that the interplay between quantum interference and graph engineering can effectively enhance quantum transport in discrete lattices.

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Evaluating Parameter Transfer in FALQON Across Graph Families

We evaluate FALQON parameter transfer for Max-Cut, transferring sequences from small donors ($n \in \{8,10,12\}$) to 14-node recipients. Using 3-regular and Erdős-Rényi families, we show that transfer success is dictated by the recipient graph, not the donor. Transfer excels for dense recipients -- achieving high approximation ratios regardless of the donor -- but remains challenging in sparse cross-family cases. Crucially, performance is highly resilient to donor size, with 8-node donors matching larger instances. Thus, cheap small graphs can provide robust parameters for larger targets, significantly reducing the measurement overhead of the feedback loop.

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A review of quantum machine learning and quantum-inspired applied methods to computational fluid dynamics

Computational Fluid Dynamics (CFD) is central to science and engineering, but faces severe scalability challenges, especially in high-dimensional, multiscale, and turbulent regimes. Traditional numerical methods often become prohibitively expensive under these conditions. Quantum computing and quantum-inspired methods have been investigated as promising alternatives. This review surveys advances at the intersection of quantum computing, quantum algorithms, machine learning, and tensor network techniques for CFD. We discuss the use of Variational Quantum Algorithms as hybrid quantum-classical solvers for PDEs, emphasizing their ability to incorporate nonlinearities through Quantum Nonlinear Processing Units. We further review Quantum Neural Networks and Quantum Physics-Informed Neural Networks, which extend classical machine learning frameworks to quantum hardware and have shown advantages in parameter efficiency and solution accuracy for certain CFD benchmarks. Beyond quantum computing, we examine tensor network methods, originally developed for quantum many-body systems and now adapted to CFD as efficient high-dimensional compression and solver tools. Reported studies include several orders of magnitude reductions in memory and runtime while preserving accuracy. Together, these approaches highlight quantum and quantum-inspired strategies that may enable more efficient CFD solvers. This review closes with perspectives: quantum CFD remains out of reach in the NISQ era, but quantum-inspired tensor networks already show practical benefits, with hybrid approaches offering the most promising near-term strategy.

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Optimizing Quantum Compilation via High-Level Quantum Instructions

Current quantum programming is dominated by low-level, circuit-centric approaches that limit the potential for compiler optimization. This work presents how a high-level programming construct provides compilers with the semantic information needed for advanced optimizations. We introduce a novel optimization that leverages a quantum-specific instruction to automatically substitute quantum gates with more efficient, approximate decompositions, a process that is transparent to the programmer and significantly reduces quantum resource requirements. Furthermore, we show how this instruction guarantees the correct uncomputation of auxiliary qubits, enabling safe, dynamic quantum memory management. We illustrate these concepts by implementing a V-chain decomposition of the multi-controlled NOT gate, showing that our high-level approach not only simplifies the code but also enables the compiler to generate a circuit with up to a 50% reduction in CNOT gates. Our results suggest that high-level abstractions are crucial for unlocking a new class of powerful compiler optimizations, paving the way for more efficient quantum computation.

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Quantum Gate Decomposition: A Study of Compilation Time vs. Execution Time Trade-offs

Similar to classical programming, high-level quantum programming languages generate code that cannot be executed directly by quantum hardware and must be compiled. However, unlike classical code, quantum programs must be compiled before each execution, making the trade-off between compilation time and execution time particularly significant. In this paper, we address the first step of quantum compilation: multi-qubit gate decomposition. We analyze the trade-offs of state-of-the-art decomposition algorithms by implementing them in the Ket quantum programming platform and collecting numerical performance data. This is the first study to both implement and analyze the current state-of-the-art decomposition methods within a single platform. Based on our findings, we propose two compilation profiles: one optimized for minimizing compilation time and another for minimizing quantum execution time. Our results provide valuable insights for both quantum compiler developers and quantum programmers, helping them make informed decisions about gate decomposition strategies and their impact on overall performance.

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Optimizing Gate Decomposition for High-Level Quantum Programming

This paper presents novel methods for optimizing multi-controlled quantum gates, which naturally arise in high-level quantum programming. Our primary approach involves rewriting $U(2)$ gates as $SU(2)$ gates, utilizing one auxiliary qubit for phase correction. This reduces the number of CNOT gates required to decompose any multi-controlled quantum gate from $O(n^2)$ to at most $32n$. Additionally, we can reduce the number of CNOTs for multi-controlled Pauli gates from $16n$ to $12n$ and propose an optimization to reduce the number of controlled gates in high-level quantum programming. We have implemented these optimizations in the Ket quantum programming platform and demonstrated significant reductions in the number of gates. For instance, for a Grover's algorithm layer with 114 qubits, we achieved a reduction in the number of CNOTs from 101,252 to 2,684. This reduction in the number of gates significantly impacts the execution time of quantum algorithms, thereby enhancing the feasibility of executing them on NISQ computers.

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Shadow measurements for feedback-based quantum optimization

Improving the performance of quantum algorithms is a fundamental task to achieve quantum advantage. In many cases, extracting information from quantum systems poses an important challenge for practical implementations in real-world quantum computers, given the high resource cost of performing state tomography. In this scenario, randomized measurements emerged as a promising tool. In particular, the classical shadows protocol allows one to retrieve expected values of low-weight Pauli observables by performing only local measurements. In this paper, we present an implementation of the recently introduced Feedback-based algorithm for quantum optimization (FALQON) with the Ket quantum programming platform, for solving the MaxCut optimization problem. We employ classical shadows for the feedback routine of parameter estimation and compare this approach with the direct estimation of observables. Our results show that depending on the graph geometry for the MaxCut problem, the number of measurements required to estimate expected values of observables with classical shadows can be up to 16 times lower than with direct observable estimation. Furthermore, by analyzing complete graphs, we numerically confirm a logarithmic growth in the required number of measurements relative to the number of observables, reinforcing that classical shadows can be a useful tool for estimating low-locality Pauli observables in quantum algorithms.

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Genuine $k$-partite correlations and entanglement in the ground state of the Dicke model for interacting qubits

Here, we calculate and study correlations of the Dicke model in the presence of qubit-qubit interaction. Whereas the analysis of correlations among its subsystems is essential for the understanding of corresponding critical phenomena and for performing quantum information tasks, the majority of correlation measures are restricted to bipartitions due to the inherent challenges associated with handling multiple partitions. To circunvent this we employ the calculation of Genuine Multipartite Correlations (GMC) based on the invariance of our model under particle permutation. We then quantify the correlations within each subpart of the system, as well as the percentage contribution of each GMC of order $k$, highlighting the many-body behaviors for different regimes of parameters. Additionally, we show that GMC signal both first- and second-order quantum phase transitions present in the model. Furthermore, as GMC encompasses both classical and quantum correlations, we employ Quantum Fisher Information (QFI) to detect genuine multipartite entanglement. Ultimately, we map the Dicke model with interacting qubits to spin in solids interacting with a quantum field of magnons, thus demonstrating a potential experimental realization of this model.

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Automated Auxiliary Qubit Allocation in High-Level Quantum Programming

We present a method for optimizing quantum circuit compilation by automating the allocation of auxiliary qubits for multi-qubit gate decompositions. This approach is implemented and evaluated within the high-level quantum programming platform Ket. Our results indicate that the decomposition of multi-qubit gates is more effectively handled by the compiler, which has access to all circuit parameters, rather than through a quantum programming API. To evaluate the approach, we compared our implementation against Qiskit, a widely used quantum programming platform, by analyzing two quantum algorithms. Using a 16-qubit QPU, we observed a reduction of 87% in the number of CNOT gates in Grover's algorithm for 9 qubits. For a state preparation algorithm with 7 qubits, the number of CNOT gates was reduced from $2.8\times10^7$ to $5.7\times10^3$, leveraging additional Ket optimizations for high-level quantum program constructions. Overall, a quadratic reduction in the number of CNOT gates in the final circuit was observed, with greater improvements achieved when more auxiliary qubits were available. These findings underscore the importance of automatic resource management, such as auxiliary qubit allocation, in optimizing quantum applications and improving their suitability for near-term quantum hardware.

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High-fidelity state transfer via quantum walks from delocalized states

We study the state transfer through quantum walks placed on a bounded one-dimensional path. We first consider continuous-time quantum walks from a Gaussian state. We find such a state when superposing centered on the starting and antipodal positions preserves a high fidelity for a long time and when sent on large circular graphs. Furthermore, it spreads with a null group velocity. We also explore discrete-time quantum walks to evaluate the qubit fidelity throughout the walk. In this case, the initial state is a product of states between a qubit and a Gaussian superposition of position states. Then, we add two $σ_x$ gates to confine this delocalized qubit. We also find that this bounded system dynamically enables periodic recovery of the initial separable state. We outline some applications of our results in dynamic graphs and propose quantum circuits to implement them based on the available literature.

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Alternatives to a nonhomogeneous partial differential equation quantum algorithm

Recently J. M. Arrazola et al. [Phys. Rev. A 100, 032306 (2019)] proposed a quantum algorithm for solving nonhomogeneous linear partial differential equations of the form $Aψ(\textbf{r})=f(\textbf{r})$. Its nonhomogeneous solution is obtained by inverting the operator $A$ along with the preparation and measurement of special ancillary modes. In this work we suggest modifications in its structure to reduce the costs of preparing the initial ancillary states and improve the precision of the algorithm for a specific set of inputs. These achievements enable easier experimental implementation of the quantum algorithm based on nowadays technology.

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Genuine Multipartite Correlations in a Boundary Time Crystal

In this work we study genuine multipartite correlations (GMC's) in a boundary time crystal (BTC). Boundary time crystals are nonequilibrium quantum phases of matter in contact to an environment, for which a macroscopic fraction of the many-body system breaks the time-translation symmetry. We analyze both (i) the structure (orders) of GMC's among the subsystems, as well as (ii) their build-up dynamics for an initially uncorrelated state. We find that, in the thermodynamic limit (and only in such a limit), multipartite correlations of all orders grow indefinitely in time in the BTC phase, further displaying a persistent oscillatory behavior around their mean growth. The orders of the correlations show a power-law decaying hierarchy among its $k$-partitions. Moreover, in the long-time limit the GMC's are shown extensive with the system size, contrasting to the subextensive scaling in the non time-crystal (ferromagnetic) phase of the model. We also discuss the classical and quantum nature of these correlations with basis on multipartite entanglement witnesses, specifically, the analysis of the Quantum Fisher Information (QFI). Both GMC and QFI are able to capture and distinguish the different phases of the model. Our work highlights the genuine many-body properties of these peculiar non-equilibrium phases of matter.

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QBoost for regression problems: solving partial differential equations

A hybrid algorithm based on machine learning and quantum ensemble learning is proposed that is capable of finding a solution to a partial differential equation with good precision and favorable scaling in the required number of qubits. The classical part is composed by training several regressors (weak-learners), capable of solving a partial differential equation using machine learning. The quantum part consists of adapting the QBoost algorithm to solve regression problems. We have successfully applied our framework to solve the 1D Burgers' equation with viscosity, showing that the quantum ensemble method really improves the solutions produced by weak-learners. We also implemented the algorithm on the D-Wave Systems, confirming the best performance of the quantum solution compared to the simulated annealing and exact solver methods, given the memory limitations of our classical computer used in the comparison.

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Detailed Account of Complexity for Implementation of Some Gate-Based Quantum Algorithms

In this work, we are interested in the detailed analysis of complexity aspects of both time and space that arises from the implementation of a quantum algorithm on a quantum based hardware. In particular, some steps of the implementation, as state preparation and readout processes, in most of the cases can surpass the complexity aspects of the algorithm itself. We present the complexity involved in the full implementation of quantum algorithms for solving linear systems of equations and linear system of differential equations, from state preparation to the number of measurements needed to obtain good statistics from the final states of the quantum system, in order to assess the overall complexity of the processes.

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A hybrid classical-quantum approach to solve the heat equation using quantum annealers

The numerical solution of partial differential equations by discretization techniques is ubiquitous in computational physics. In this work we benchmark this approach in the quantum realm by solving the heat equation for a square plate subject to fixed temperatures at the edges and random heat sources and sinks within the domain. The hybrid classical-quantum approach consists in the solution on a quantum computer of the coupled linear system of equations that result from the discretization step. Owing to the limitations in the number of qubits and their connectivity, we use the Gauss-Seidel method to divide the full system of linear equations into subsystems, which are solved iteratively in block fashion. Each of the linear subsystems were solved using 2000Q and Advantage quantum computers developed by D-Wave Systems Inc. By comparing classical numerical and quantum solutions, we observe that the errors and chain break fraction are, on average, greater on the 2000Q system. Unlike the classical Gauss-Seidel method, the errors of the quantum solutions level off after a few iterations of our algorithm. This is partly a result of the span of the real number line available from the mapping of the chosen size of the set of qubit states. We verified this by using techniques to progressively shrink the range mapped by the set of qubit states at each iteration (increasing floating-point accuracy). As a result, no leveling off is observed. However, an increase in qubits does not translate to an overall lower error. This is believed to be indicative of the increasing length of chains required for the mapping to real numbers and the ensuing limitations of hardware.

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Genuine Multipartite Correlations Distribution in the Criticality of Lipkin-Meshkov-Glick Model

Correlations play a key role in critical phenomena. Quantities such as the entanglement entropy, for instance, provide key insights into the mechanisms underlying quantum criticality. Almost all of our present knowledge, however, is restricted to bipartite correlations. Some questions still remain unanswered, such as: What parcel of the total correlations are genuinely k-partite? With the goal of shedding light on this difficult question, in this paper we put forth a detailed study of the behavior of genuine multipartite correlations (GMC) of arbitrary orders in the Lipkin-Meshkov-Glick model. We find that GMC of all orders serve to signal the second order quantum phase transition presented in the model. Applying finite-size scaling methods, we also find the critical exponents for some orders of correlations.

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