arXiv subjects

Francesco Scala

Publications and source records attributed to Francesco Scala.

11 recordsLinked to original sources

arXiv2025

Spectral Gap Estimation via Adiabatic Preparation

Estimating energy gaps, i.e. the energy difference between two different states, in quantum systems is crucial for understanding their properties. Conventionally, spectral gap estimation relies on independently computing the ground-state and first-excited-state energies and then taking their difference. This work introduces an alternative procedure for estimating spectral gaps on digital quantum devices using the Adiabatic Preparation technique to create a specific superposition state. The expectation values of observables measured on such a state exhibit time-dependent fluctuations which, through a fitting process, can be used to estimate the energy gap. We successfully test our method on the 1D and 2D Ising models, and H2 and He2 molecules, implementing relatively shallow circuits both on noiseless and noisy simulators. The robustness of the approach is corroborated by additional experiments on the real IonQ Aria device for the 1D Ising model up to 20 qubits, demonstrating the applicability of the proposed method for currently available digital quantum devices and paving the way for more complex energy gap calculation requiring deeper circuits in the fault-tolerant era to come.

quant-ph↗

arXiv2025

Noise-Induced Equalization in quantum learning models

Quantum noise is known to strongly affect quantum computation, thus potentially limiting the performance of currently available quantum processing units. Even learning models based on variational quantum algorithms, which were designed to cope with the limitations of state-of-the art noisy hardware capabilities, are affected by noise-induced barren plateaus, arising when the noise level becomes too strong. However, the generalization performances of such quantum machine learning algorithms can also be positively influenced by a proper level of noise, despite its generally detrimental effects. Here, we propose a pre-training procedure to determine the quantum noise level leading to desirable optimisation landscape properties. We show that an optimized level of quantum noise induces an ``equalization'' of the directions in the Riemannian manifold, flattening(/enhancing) the initially steep(/shallow) ones by redistributing sensitivity across its principal eigen-directions. We analyse this noise-induced equalization through the lens of the Quantum Fisher Information Matrix, thus providing a recipe that allows to estimate the noise level inducing the strongest equalization. We finally benchmark these conclusions with extensive numerical simulations providing evidence of the beneficial noise effects in the neighborhood of the best equalization, often leading to improved generalization.

quant-ph↗

arXiv2025

Gradient Scalability and Taylor Surrogation of Quantum Cost Landscapes

Variational Quantum Algorithms are promising candidates for near-term quantum computing, yet they face scalability challenges due to barren plateaus, where gradients vanish exponentially relative to system size. Recent conjectures suggest that avoiding these plateaus might inherently lead to classical simulability, thereby limiting the opportunities for quantum advantage. In this work, we advance the theoretical understanding of the relationship between gradient scalability at initialization and the computational complexity of variational quantum algorithms. We first present the Taylor surrogate, a classical simulation technique that matches Pauli path runtime guarantees on near-Clifford regions while offering runtime advantages in specific regimes. Leveraging this surrogate, we prove that beyond previously established classically simulable regions, the computational complexity is at least super-polynomial. Next, we introduce the Linear Clifford Encoder, a classically efficient ansatz modifier that ensures constant-scaling gradients within landscape regions close to Clifford circuits. Finally, numerical experiments on these modified landscapes provide preliminary empirical evidence of a transition zone where constant-scaling gradients may decay polynomially in super-polynomially complex regions rather than exponentially. These findings suggest speculative instances where non-vanishing gradients and super-polynomial complexity could potentially coexist, vindicating the need for future formal proofs.

quant-ph↗

arXiv2025

Combining Abstract Argumentation and Machine Learning for Efficiently Analyzing Low-Level Process Event Streams

Monitoring and analyzing process traces is a critical task for modern companies and organizations. In scenarios where there is a gap between trace events and reference business activities, this entails an interpretation problem, amounting to translating each event of any ongoing trace into the corresponding step of the activity instance. Building on a recent approach that frames the interpretation problem as an acceptance problem within an Abstract Argumentation Framework (AAF), one can elegantly analyze plausible event interpretations (possibly in an aggregated form), as well as offer explanations for those that conflict with prior process knowledge. Since, in settings where event-to-activity mapping is highly uncertain (or simply under-specified) this reasoning-based approach may yield lowly-informative results and heavy computation, one can think of discovering a sequence-tagging model, trained to suggest highly-probable candidate event interpretations in a context-aware way. However, training such a model optimally may require using a large amount of manually-annotated example traces. We then propose a data-efficient neuro-symbolic approach to the problem, where the candidate interpretations returned by the example-driven sequence tagger is refined by the AAF-based reasoner. This allows us to also leverage prior knowledge to compensate for the scarcity of example data, as confirmed by experimenftal results.

cs.AI↗

arXiv2025

Towards Practical Quantum Neural Network Diagnostics with Neural Tangent Kernels

Knowing whether a Quantum Machine Learning model would perform well on a given dataset before training it can help to save critical resources. However, gathering a priori information about model performance (e.g., training speed, critical hyperparameters, or inference capabilities on unseen data) is a highly non-trivial task, in general. Recently, the Quantum Neural Tangent Kernel (QNTK) has been proposed as a powerful mathematical tool to describe the behavior of Quantum Neural Network (QNN) models. In this work, we propose a practical framework allowing to employ the QNTK for QNN performance diagnostics. More specifically, we show how a critical learning rate and a characteristic decay time for the average training error can be estimated from the spectrum of the QNTK evaluated at the initialization stage. We then show how a QNTK-based kernel formula can be used to analyze, up to a first-order approximation, the expected inference capabilities of the quantum model under study. We validate our proposed approach with extensive numerical simulations, using different QNN architectures and datasets. Our results demonstrate that QNTK diagnostics yields accurate approximations of QNN behavior for sufficiently deep circuits, can provide insights for shallow QNNs, and enables detecting - hence also addressing - potential shortcomings in model design.

quant-ph↗

arXiv2024

Resource-efficient quantum algorithm for linear systems of equations

Finding the solution to linear systems is at the heart of many applications in science and technology. Over the years a number of algorithms have been proposed to solve this problem on a digital quantum device, yet most of these are too demanding to be applied to the current noisy hardware. In this work, an original algorithmic procedure to solve the Quantum Linear System Problem (QLSP) is presented, which combines ideas from Variational Quantum Algorithms (VQA) and the framework of classical shadows. The result is the Shadow Quantum Linear Solver (SQLS), a quantum algorithm solving the QLSP avoiding the need for large controlled unitaries, requiring a number of qubits that is logarithmic in the system size. In particular, our heuristics show an exponential advantage of the SQLS in circuit execution per cost function evaluation when compared to other notorious variational approaches to solving linear systems of equations. We test the convergence of the SQLS on a number of linear systems, and results highlight how the theoretical bounds on the number of resources used by the SQLS are conservative. Finally, we apply this algorithm to a physical problem of practical relevance, by leveraging decomposition theorems from linear algebra to solve the discretized Laplace Equation in a 2D grid for the first time using a hybrid quantum algorithm.

quant-ph↗

arXiv2024

Spectral Gap Superposition States

This work introduces a novel NISQ-friendly procedure for estimating spectral gaps in quantum systems. By leveraging Adiabatic Thermalization, we are able to create the Spectral Gap Superposition state, a newly defined quantum state exhibiting observable fluctuations in time that allow for the accurate estimation of any energy gap. Our method is tested by estimating the energy gap between the ground and the first excited state for the 1D and 2D Ising model, the Hydrogen molecule H2 and Helium molecule He2. Despite limiting our circuit design to have at most 40 Trotter steps, our numerical experiments of both noiseless and noisy devices for the presented systems give relative errors in the order of $10^{-2}$ and $10^{-1}$. Further experiments on the IonQ Aria device lead to spectral gap estimations with a relative error of $10^{-2}$ for a 4-site Ising chain, demonstrating the validity of the procedure for NISQ devices and charting a path towards a new way of calculating energy gaps.

quant-ph↗

arXiv2023

A General Approach to Dropout in Quantum Neural Networks

In classical Machine Learning, "overfitting" is the phenomenon occurring when a given model learns the training data excessively well, and it thus performs poorly on unseen data. A commonly employed technique in Machine Learning is the so called "dropout", which prevents computational units from becoming too specialized, hence reducing the risk of overfitting. With the advent of Quantum Neural Networks as learning models, overfitting might soon become an issue, owing to the increasing depth of quantum circuits as well as multiple embedding of classical features, which are employed to give the computational nonlinearity. Here we present a generalized approach to apply the dropout technique in Quantum Neural Network models, defining and analysing different quantum dropout strategies to avoid overfitting and achieve a high level of generalization. Our study allows to envision the power of quantum dropout in enabling generalization, providing useful guidelines on determining the maximal dropout probability for a given model, based on overparametrization theory. It also highlights how quantum dropout does not impact the features of the Quantum Neural Networks model, such as expressibility and entanglement. All these conclusions are supported by extensive numerical simulations, and may pave the way to efficiently employing deep Quantum Machine Learning models based on state-of-the-art Quantum Neural Networks.

quant-ph↗

arXiv2023

Deterministic entangling gates with nonlinear quantum photonic interferometers

The quantum computing paradigm in photonics currently relies on the multi-port interference in linear optical devices, which is intrinsically based on probabilistic measurements outcome and thus non-deterministic. Devising a fully deterministic, universal, and practically achievable quantum computing platform based on integrated photonic circuits is still an open challenge. Here we propose to exploit weakly nonlinear photonic devices to implement deterministic entangling quantum gates, following the definition of dual rail photonic qubits. It is shown that a universal set of single- and two-qubit gates can be designed by a suitable concatenation of few optical interferometric elements, with optimal fidelities arbitrarily close to 100% theoretically demonstrated through a bound constrained optimization algorithm. The actual realization would require the concatenation of a few tens of elementary operations, as well as on-chip optical nonlinearities that are compatible with some of the existing quantum photonic platforms, as it is finally discussed.

quant-ph↗

arXiv2023

A Quantum Algorithmic Approach to Multiconfigurational Valence Bond Theory: Insights from Interpretable Circuit Design

Efficient ways to prepare fermionic ground states on quantum computers are in high demand and different techniques have been developed over the last years. Despite having a vast set of methods, it is still unclear which method performs well for which system. In this work, we combine interpretable circuit designs with an effective basis approach in order to optimize a multiconfigurational valence bond wavefunction. Based on selected model systems, we show how this leads to explainable performance. We demonstrate that the developed methodology outperforms related methods in terms of the size of the effective basis as well as individual quantum resources for the involved circuits.

quant-ph↗

arXiv2022

Quantum variational learning for entanglement witnessing

Several proposals have been recently introduced to implement Quantum Machine Learning (QML) algorithms for the analysis of classical data sets employing variational learning means. There has been, however, a limited amount of work on the characterization and analysis of quantum data by means of these techniques, so far. This work focuses on one such ambitious goal, namely the potential implementation of quantum algorithms allowing to properly classify quantum states defined over a single register of $n$ qubits, based on their degree of entanglement. This is a notoriously hard task to be performed on classical hardware, due to the exponential scaling of the corresponding Hilbert space as $2^n$. We exploit the notion of "entanglement witness", i.e., an operator whose expectation values allow to identify certain specific states as entangled. More in detail, we made use of Quantum Neural Networks (QNNs) in order to successfully learn how to reproduce the action of an entanglement witness. This work may pave the way to an efficient combination of QML algorithms and quantum information protocols, possibly outperforming classical approaches to analyse quantum data. All these topics are discussed and properly demonstrated through a simulation of the related quantum circuit model.

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