arXiv subjects

Luca Dellantonio

Publications and source records attributed to Luca Dellantonio.

At least 19 recordsLinked to original sources

arXiv2026

A Quantum Dynamics Tutorial: Visualising Dynamical Decoupling Sequences on the Bloch Sphere

Dynamical decoupling protocols provide a versatile toolbox for robust control of quantum systems, suppressing noise that would otherwise limit their performance. This tutorial aims to provide an intuitive interpretation of these protocols. Written from an experimentalist's perspective, we use the Bloch sphere picture to visualise quantum dynamics for a range of sequences. Here each dynamical decoupling operation can be viewed as an "effective field vector", whilst the phase and population of the two-level system is encoded into a "Bloch vector". The two-level dynamics are calculated by the cross product between these two vectors, such that the quantum state represented by the Bloch vector rotates around the effective field vector. Several prominent control schemes are simulated using this picture, starting with a Rabi oscillation, followed by the Ramsey sequence and a selection of pulsed and continuous dynamical decoupling techniques. These sequences are engineered to preserve control of the quantum system for as long as possible, with their efficacy often quantified by a coherence time. We clarify the definition of the longitudinal $T_1$, transverse $T_2$ and inhomogeneous $T_2^*$ coherence times, which are used across the literature. Each simulation has an accompanying animation to illustrate the protocol's two-level dynamics on the Bloch sphere. The custom simulation and Bloch sphere plotting scripts are also provided in an open access repository to allow the reader to reproduce, modify, and explore these results. Dynamical decoupling sequences have established themselves as an invaluable tool across the entire quantum technologies remit; by providing an open framework alongside this tutorial, we aim to make these protocols more accessible to new researchers discovering this thriving field.

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arXiv2026

Graph automorphisms to obtain Clifford symmetries in open and closed qudit models

In the recent article [arXiv:2605.18966], we demonstrated that finding Clifford symmetries can be mapped to a Graph Automorphism (GA) problem. Here, we provide an algorithm to obtain such symmetries on general qudit systems, that works on the principle of encoding Clifford invariants of a Hamiltonian onto properties of a graph. Labelling Hamiltonian terms as vertices, a permutation of such vertices that respects the Clifford invariants (a GA) is both a valid Clifford, and a symmetry up to phase correction checks. We test this on multiple physical models and discuss the scaling with respect to the number of qudits and Pauli strings, as well as various strategies for optimisation in different regimes. We further show that the graph automorphism representation of Clifford symmetries can be expanded to open quantum systems.

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arXiv2026

Attention-based optimizer for symmetry finding

Finding symmetries is crucial for understanding physical models. In this work, we present an optimization framework that searches Pauli symmetries of Hamiltonians, merging the fields of machine learning with automated symmetry finding. Built on a Set-Transformer architecture, our framework uses self-attention to encode the pairwise and higher-order correlations among the Pauli-Strings. The relations are then decoded as a candidate, which is further optimized with a custom commutation-based objective, and mapped to a symmetry of the input Hamiltonian. We apply our method to random Pauli Hamiltonians, periodic one and two dimensional transverse-field Ising model and the Toric code. We show that for physical Hamiltonians (Ising and Toric), our framework succeeds with near-deterministic probability while providing substantial advantage compared to state-of-the-art strategies. For random Pauli Hamiltonians, we estimate the required computational resources, specifically the number of parallel starts and the number of GPUs, to find a symmetry with high success probability under fixed design specifications.

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arXiv2026

Clifford symmetries in quantum many-body systems

Obtaining the symmetries of a model is a critical step towards developing an understanding and ultimately analytically or numerically solving the model. However, finding symmetries is generally extremely complicated, often being the result of insightful thinking. In this work, we complement human ingenuity with an algorithm. We leverage the classically efficient Clifford group to find symmetries for arbitrary many-body Hamiltonians via a graph representation. We demonstrate our method on random and physical Hamiltonians, with instances of up to one thousand qubits and demonstrate how our approach can provide deeper understanding of the model.

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arXiv2026

An Error-aware and Adaptive Method for the Estimation of Quantum Observables on Qudit-Based Quantum Computers

The accurate estimation of observables is a crucial task in quantum computing. Recent advances have highlighted the need for (a) specialized protocols for qudit-based devices, that include (b) error-aware strategies. Here, we present AQUIRE, the first protocol that can (a) accurately estimate both the mean and the error of an observable on qudit-based quantum computers. AQUIRE achieves this by constructing a Bayesian model to accommodate generalized Pauli operators. It is designed to continuously monitor the estimated average and the associated error of the observable, adjusting the subsequent measurements in real-time. Additionally, AQUIRE is (b) device- and experiment-specific error-aware, and accounts for hardware imperfections and experimental noise during the estimation process. We demonstrate AQUIRE's advantage via numerical simulations and showcase its ability to quantify the noise affecting the estimation by implementing it on a trapped-ion qudit quantum processor. By exploiting general commutation relations and overlap grouping measurements, our protocol is state-of-the-art when restricted to qubit-based quantum computers and extends this advantage to the qudit case.

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arXiv2025

Self-Configuring Quantum Networks with Superposition of Trajectories

Quantum networks are a backbone of future quantum technologies thanks to their role in communication and scalable quantum computing. However, their performance is challenged by noise and decoherence. We propose a self-configuring approach that integrates superposed quantum paths with variational quantum optimization techniques. This allows networks to dynamically optimize the superposition of noisy paths across multiple nodes to establish high-fidelity connections between different parties. Our framework is in principle capable of adapting to unknown noise without requiring detailed characterization or benchmarking of the corresponding quantum channels. We also discuss the role of vacuum coherence, a quantum effect central to path superposition that impacts protocol performance. Additionally, we demonstrate that our approach remains beneficial even in the presence of imperfections in the generation of path superposition.

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arXiv2025

Enhanced measurements on quantum computers via the simultaneous probing of non-commuting Pauli operators

Measuring the state of quantum computers is a highly non-trivial task, with implications for virtually all quantum algorithms. We propose a novel scheme where identical copies of a quantum state are measured jointly so that all Pauli operators within the considered observable can be simultaneously assessed. We use Bayesian statistics to accurately estimate the average and error, and develop an adaptive shot-allocation algorithm that preferentially samples the most uncertain Pauli terms. In regimes with many non-commuting Pauli operators, our ``double'' scheme can outperform the state-of-the-art measurement protocol in minimizing total shots for a given precision.

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arXiv2025

Securities Transaction Settlement Optimization on superconducting quantum devices

We describe a quantum variational algorithm for securities transactions settlement optimization, based on a novel mathematical formalization of the problem that includes the most relevant constraints considered in the pan-European securities settlement platform TARGET2-Securities. The proposed algorithm is designed for Noisy Intermediate-Scale Quantum devices, specifically targeting IBM's superconducting qubit machines. We adopt non-linear activation functions to encode inequality constraints in the objective function of the problem, and design customized noise mitigation techniques to alleviate the effect of readout errors. We consider batches of up to 40 trades obtained from real transactional data to benchmark our algorithm on quantum hardware against classical and quantum-inspired solvers.

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arXiv2024

Generating graph states with a single quantum emitter and the minimum number of fusions

Graph states are the key resources for measurement- and fusion-based quantum computing with photons, yet their creation is experimentally challenging. We optimize a hybrid graph-state generation scheme using a single quantum emitter and linear optics Bell-state measurements called fusions. We first generate a restricted class of states from a single quantum emitter and then apply fusions to create a target graph state, where we use a dynamic programming approach to find the construction that requires the lowest possible number of fusions. Our analysis yields a lookup table for constructing $\sim 2.8\times 10^7$ non-isomorphic graph states with the minimum number of fusions. The lookup table covers all graph states with up to eight qubits and several other ones with up to 14 qubits. We present construction protocols of selected graph states and provide the lookup table. For large graph states that are not in the lookup table, we derive bounds for the required number of fusions using graph-theoretic properties. Finally, we use the lookup table to search for the best graph codes for loss-tolerant encodings, given a fixed number of fusions for their construction.

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arXiv2024

qLUE: A Quantum Clustering Algorithm for Multi- Dimensional Datasets

Clustering algorithms are at the basis of several technological applications, and are fueling the development of rapidly evolving fields such as machine learning. In the recent past, however, it has become apparent that they face challenges stemming from datasets that span more spatial dimensions. In fact, the best-performing clustering algorithms scale linearly in the number of points, but quadratically with respect to the local density of points. In this work, we introduce qLUE, a quantum clustering algorithm that scales linearly in both the number of points and their density. qLUE is inspired by CLUE, an algorithm developed to address the challenging time and memory budgets of Event Reconstruction (ER) in future High-Energy Physics experiments. As such, qLUE marries decades of development with the quadratic speedup provided by quantum computers. We numerically test qLUE in several scenarios, demonstrating its effectiveness and proving it to be a promising route to handle complex data analysis tasks -- especially in high-dimensional datasets with high densities of points.

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arXiv2024

Fast storage of photons in cavity-assisted quantum memories

Ideal photonic quantum memories can store arbitrary pulses of light with unit efficiency. This requires operating in the adiabatic regime, where pulses have a duration much longer than the bandwidth of the memory. In the non-adiabatic regime of short pulses, memories are therefore imperfect, and information is always lost. We theoretically investigate the bandwidth limitations for setups based on individual atoms, or ensembles thereof, confined inside optical cavities. We identify an effective strategy for optimizing the efficiencies of the storage and retrieval process regardless of the duration of the pulses. Our protocol is derived almost completely analytically and attains efficiencies better than or comparable to those obtained by numerical optimization. Furthermore, our results provide an improved understanding of the performance of quantum memories in several regimes. When considering pulses defined on an infinite time interval, the shapes can be divided into two categories, depending on their asymptotic behaviours. If the intensity of the pulse increases with time slower than or as an exponential function, then the storage efficiency is only limited by the pulse width. For pulses defined on a finite interval, on the other hand, the efficiency is determined by the shape at the beginning of the storage or, correspondingly, at the end of the retrieval process.

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arXiv2023

A square-root speedup for finding the smallest eigenvalue

We describe a quantum algorithm for finding the smallest eigenvalue of a Hermitian matrix. This algorithm combines Quantum Phase Estimation and Quantum Amplitude Estimation to achieve a quadratic speedup with respect to the best classical algorithm in terms of matrix dimensionality, i.e., $\widetilde{\mathcal{O}}(\sqrt{N}/ε)$ black-box queries to an oracle encoding the matrix, where $N$ is the matrix dimension and $ε$ is the desired precision. In contrast, the best classical algorithm for the same task requires $Ω(N)\text{polylog}(1/ε)$ queries. In addition, this algorithm allows the user to select any constant success probability. We also provide a similar algorithm with the same runtime that allows us to prepare a quantum state lying mostly in the matrix's low-energy subspace. We implement simulations of both algorithms and demonstrate their application to problems in quantum chemistry and materials science.

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arXiv2023

Simulating 2D lattice gauge theories on a qudit quantum computer

Particle physics underpins our understanding of the world at a fundamental level by describing the interplay of matter and forces through gauge theories. Yet, despite their unmatched success, the intrinsic quantum mechanical nature of gauge theories makes important problem classes notoriously difficult to address with classical computational techniques. A promising way to overcome these roadblocks is offered by quantum computers, which are based on the same laws that make the classical computations so difficult. Here, we present a quantum computation of the properties of the basic building block of two-dimensional lattice quantum electrodynamics, involving both gauge fields and matter. This computation is made possible by the use of a trapped-ion qudit quantum processor, where quantum information is encoded in $d$ different states per ion, rather than in two states as in qubits. Qudits are ideally suited for describing gauge fields, which are naturally high-dimensional, leading to a dramatic reduction in the quantum register size and circuit complexity. Using a variational quantum eigensolver, we find the ground state of the model and observe the interplay between virtual pair creation and quantized magnetic field effects. The qudit approach further allows us to seamlessly observe the effect of different gauge field truncations by controlling the qudit dimension. Our results open the door for hardware-efficient quantum simulations with qudits in near-term quantum devices.

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arXiv2023

Minimal qubit representations of Hamiltonians via conserved charges

In the last years, we have been witnessing a tremendous push to demonstrate that quantum computers can solve classically intractable problems. This effort, initially focused on the hardware, progressively included the simplification of the models to be simulated. We consider Hamiltonians written in terms of Pauli operators and systematically cut all qubits that are not essential to simulate the system. Our approach is universally applicable and lowers the complexity by first ensuring that the largest possible portion of the Hilbert space becomes irrelevant, and then by finding and exploiting all conserved charges of the system, i.e., symmetries that can be expressed as Pauli operators. Remarkably, both processes are classically efficient and optimal. To showcase our algorithm, we simplify chemical molecules, lattice gauge theories, the Hubbard and the Kitaev models.

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arXiv2023

Measurement-based infused circuits for variational quantum eigensolvers

Variational quantum eigensolvers (VQEs) are successful algorithms for studying physical systems on quantum computers. Recently, they were extended to the measurement-based model of quantum computing, bringing resource graph states and their advantages into the realm of quantum simulation. In this work, we incorporate such ideas into traditional VQE circuits. This enables novel problem-informed designs and versatile implementations of many-body Hamiltonians. We showcase our approach on real superconducting quantum computers by performing VQE simulations of testbed systems including the perturbed planar code, Z2 lattice gauge theory, 1D quantum chromodynamics, and the LiH molecule.

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arXiv2023

Enhancing Quantum Computation via Superposition of Quantum Gates

Overcoming the influence of noise and imperfections in quantum devices is one of the main challenges for viable quantum applications. In this article, we present different protocols, which we denote as "superposed quantum error mitigation", that enhance the fidelity of single gates or entire computations by performing them in coherent superposition. Our results demonstrate that via our methods, significant noise suppression can be achieved for most kinds of decoherence and standard experimental parameter regimes. Our protocols can be either deterministic, such that the outcome is never post-selected, or probabilistic, in which case the resulting state must be discarded unless a well-specified condition is met. By using sufficiently many resources and working under broad assumptions, our methods can yield the desired output state with unit fidelity. Finally, we analyze our approach for gate-based, measurement-based and interferometric-based models, demonstrating the applicability in all cases and investigating the fundamental mechanisms they rely upon.

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arXiv2023

Superposed Quantum Error Mitigation

Overcoming the influence of noise and imperfections is a major challenge in quantum computing. Here, we present an approach based on applying a desired unitary computation in superposition between the system of interest and some auxiliary states. We demonstrate, numerically and on the IBM Quantum Platform, that parallel applications of the same operation lead to significant noise mitigation when arbitrary noise processes are considered. We first design probabilistic implementations of our scheme that are plug and play, independent of the noise characteristic and require no postprocessing. We then enhance the success probability (up to deterministic) using adaptive corrections. We provide an analysis of our protocol performance and demonstrate that unit fidelity can be achieved asymptotically. Our approaches are suitable to both standard gate-based and measurement-based computational models.

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arXiv2023

Adaptive estimation of quantum observables

The accurate estimation of quantum observables is a critical task in science. With progress on the hardware, measuring a quantum system will become increasingly demanding, particularly for variational protocols that require extensive sampling. Here, we introduce a measurement scheme that adaptively modifies the estimator based on previously obtained data. Our algorithm, which we call AEQuO, continuously monitors both the estimated average and the associated error of the considered observable, and determines the next measurement step based on this information. We allow both for overlap and non-bitwise commutation relations in the subsets of Pauli operators that are simultaneously probed, thereby maximizing the amount of gathered information. AEQuO comes in two variants: a greedy bucket-filling algorithm with good performance for small problem instances, and a machine learning-based algorithm with more favorable scaling for larger instances. The measurement configuration determined by these subroutines is further post-processed in order to lower the error on the estimator. We test our protocol on chemistry Hamiltonians, for which AEQuO provides error estimates that improve on all state-of-the-art methods based on various grouping techniques or randomized measurements, thus greatly lowering the toll of measurements in current and future quantum applications.

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