Quantum Applications Workshop 2026

26 Feb 2026, 08:30 → 27 Feb 2026, 19:20 Europe/Rome

Lecture hall 5

Workshop on Quantum Technology Applications and Artificial Intelligence: Challenges and Perspectives for the Near Future

One hundred years ago, Heisenberg, Born, and Jordan introduced the first mathematically formulated version of quantum mechanics. Shortly thereafter, Schrödinger proposed an alternative description of nature. These two seemingly different formulations were eventually shown to be equivalent.

 

The first quantum revolution profoundly influenced nearly every aspect of culture—including physics, mathematics, philosophy, psychology, and literature. However, its most transformative impact was on technology. Indeed, almost every device we use today relies on microelectronics, whose fundamental laws are rooted in the quantum realm.

 

Nowadays, the rapid experimental progress in quantum control is driving the so-called ‘second quantum revolution’, in which technologies capable of manipulating individual quantum systems are becoming applicable. These advances promise significant impact in many key areas, such as quantum cryptography and computation, quantum teleportation, frequency standard improvement, and quantum phase-estimation–based metrology.

 

Both theoretical and experimental challenges lie in improving our ability to manipulate quantum states (kets) while preserving their distinctive quantum features—such as entanglement—during the interactions required for experimental implementation and in the presence of unavoidable coupling with the environment.

 

In 1936, Alan Turing, with the Turing machine, gave rise to the idea that thinking could be programmed. John McCarthy coined the term 'Artificial Intelligence' during the 1956 Dartmouth Conference (USA). In those years, Allen Newell and Herbert Simon created the Logic Theorist, the first program capable of solving logical problems, while John McCarthy developed the LISP programming language, which was fundamental to AI for decades.

The beautiful dream of the thinking machine might have remained just that if the first quantum revolution hadn't led to the birth of microelectronics. The realization of high-performance computing, based on increasingly faster computers and the development of more and more advanced machine learning and deep learning algorithms, has made that dream one of the most versatile and useful tools.

An unimaginable step forward will be accomplished in the near future, when the second quantum revolution impacts artificial intelligence — through quantum computers on one side and quantum deep learning on the other.

 

The aim of this conference is to illustrate and discuss some of the applications emerging from the second quantum revolution across different fields including artificial intelligence. 

Thursday 26 February

08:30 Registrations

Welcome

1 Keynote speech: Quantum Applications in Financial Institutions

Speaker: Mario Onorato (Promontory IBM Consulting)

Introduction to Quantum Computing

Convener: Emanuele Marsili

11:00 Coffee break

Introduction to Quantum Computing

Convener: Emanuele Marsili

13:00 Lunch break

Frontiers of Physics

2 Quantum Entanglement Beyond Kinematics: A Dynamical Hypothesis in (3,2)-Dimensional Spacetime

Quantum entanglement exhibits nonlocal correlations that lack a known local dynamical explanation. While standard quantum theory predicts these correlations with high accuracy, it does not specify \emph{how} they are mediated between distant systems. Building on our previous work of Ref.~\cite{PRR} proposing an underlying process in an extended spacetime with an additional timelike coordinate, we develop a concrete, constraint-driven construction. Motivated by the fact that finite-velocity ``hidden-influence'' models generically conflict with no-signaling in multipartite settings, we show first that warped geometry with a single extra \emph{spatial} dimension forbids any effective superluminal "shortcut". We then formulate a warped $(3,2)$-dimensional framework and identify conditions for consistency with the higher-dimensional equations of General Relativity and for operational causality on the brane, implemented by restricting to a physically admissible sector and defining the brane response through a $t$-retarded Green function. In this background we introduce a massless information-carrying bulk field ${\mathscr{X}}_a({\bf x}, t, \tau)$ sourced on the $\tau=0$ brane. The warping permits null bulk characteristics whose brane projection can have vanishing brane-time separation in the correlation sector, while the retarded response prescription forbids controllable signaling. We embed the mechanism in a Bohm-Bub-type nonlinear collapse dynamics via brane-evaluated projections of ${\mathscr{X}}_a({\bf x}, t, \tau)$, outline how Born statistics may arise from averaging over contextual microstates, and discuss qualitative experimental signatures, including simultaneous Bell-test configurations sensitive to cross-pair contextual couplings absent in standard quantum mechanics.

Speaker: Prof. Marco Pettini

PETTINI-Siena_slides.pdf

Siena_slides.pptx

3 Quantum Sensing for Particle Physics: the NAMASSTE R&D Project

Abstract

Answering the most puzzling questions in fundamental physis drives a continuous quest for the development of new detection techniques which may allow to go beyond the limits of traditional measurement approaches. On this purpose, an increasing R&D activity investigating new detection strategies based on exploiting the extreme sensitivity of quantum systems is currently ongoing, aiming at introducing innovative sensors with frontier performances.
Among the quantum systems under study, Single Molecule Magnets (SMMs, molecular crystals where each molecule substantially behaves as a tiny, isolated magnet) are considered to have promising potential for development in the context of spin-based devices. After an introduction to this rapidly evolving interdisciplinary field and to these relatively new materials, we present the INFN R&D project NAMASSTE and its results, which give strong evidence for the potential application of quantum sensing based on SMMs to particle detection.

Speaker: Giuseppe Latino (Istituto Nazionale di Fisica Nucleare)

QTechWS_QS4PP_NAMASSTE.pdf

4 Quantum effective action for the Josephson dynamics

We study the beyond-mean-field Josephson dynamics of the relative phase between two coupled macroscopic quantum systems. Using a covariant background field method, we derive the one-loop only-phase quantum effective action and the corresponding equation of motion for the quantum average of the phase. These analytical results are benchmarked against the exact quantum dynamics of the two-site Bose-Hubbard model, demonstrating a relevant improvement over the standard mean-field predictions across a wide range of interaction strengths.

Speaker: Luca Salasnich (University of Padova and INFN)

SALASNICH-talk-siena26.pdf

5 Improving VQE Ground State Accuracy via Soft-Coded Orthogonal Subspace Representations

In this talk, we present a new approach to improve the accuracy of ground state approximations in Variational Quantum Eigensolver (VQE) algorithms. We employ subspace representations where orthogonality is enforced via "soft-coded" constraints within the cost function, rather than "hard-coded" at the circuit level.

Similar to other subspace-based methods like Subspace-Search VQE (SSVQE) and Multistate Contracted VQE (MCVQE), our method trains parameters to overlap with the low-energy sector before diagonalizing the Hamiltonian restricted to the subspace. However, by shifting the orthogonality constraints via penalty terms during the minimization, we find that significantly shallower quantum circuits can be used while maintaining high fidelity.

We validate this approach on two benchmark cases: a 3x3 transverse-field Ising model and random realizations of the Edwards-Anderson spin-glass model on a 4x4 lattice. We show that our soft-coded representation outperforms single-state (standard VQE) and multi-state (SSVQE/MCVQE) approaches, offering a favorable trade-off between circuit depth and accuracy.

Reference:

G. Clemente and M. Intini, arXiv:2602.05980 (2026)

Speaker: Mr Marco Intini (Università di Pisa, INFN Pisa)

Intini_Siena.pdf

16:10 Coffee break

Quantum Computing Quantum Information Communication & Criptography

6 Learning as the Interface Between Quantum Hardware and AI: From Data‑Driven Quantum Design to Quantum‑Native Models

Learning provides a common language to (i) make quantum devices usable in realistic regimes and (ii) turn quantum effects into new computational primitives for AI.
On the “hardware-to-learning” direction, I will present sample‑efficient, task‑oriented inference and optimization frameworks that replace full device characterization with directly trainable objectives, including learning‑theoretic guarantees for continuous‑variable photonic processors and for classical‑to‑quantum processes when classical inputs are not under experimental control.
These ideas translate into data‑driven quantum engineering where optical receiver architectures are learned from measurement data (reinforcement‑learning calibration and supervised discovery of photonic joint‑detection decoders) and where “functional classical shadows” exploit structure across experimental sensing settings to operate in photon‑limited regimes.
On the “learning-to-hardware” direction, I introduce quantum‑native learning primitives—notably a variational quantum self‑attention mechanism that realizes nonlinearity via overlap interference and yields a directly measurable loss—enabling sequence prediction on both classical and many‑body quantum data.
Overall, the message is a tight feedback loop: learning tools make quantum platforms scalable, while quantum platforms motivate and implement new learning operations. [link.aps.org], [quantum-journal.org], [arxiv.org] [quantum-journal.org], [link.aps.org], [link.aps.org] [link.aps.org], [arxiv.org], [arxiv.org] [arxiv.org], [arxiv.org] [quantum-journal.org], [arxiv.org], [arxiv.org]

Speaker: Matteo Rosati (Università Roma Tre)

quantum_learning.pdf

7 Quantum AI: How Quantum Physics Drives the Next Generation of Secure Artificial Intelligence

Quantum physics is redefining the way we design and use Artificial Intelligence. By inspiring new computational models and unlocking richer representations of complex data, it is enabling AI systems that can detect anomalies, reveal subtle patterns, and respond more intelligently to emerging cyber threats.

This talk explores how core quantum concepts — including superposition, entanglement, and interference — are influencing the development of next‑generation AI techniques aimed at strengthening digital security. We will look at how ideas originally developed to understand the fundamental structure of the universe are now driving practical advances in cybersecurity, from smarter detection systems to more resilient digital infrastructures.

Speaker: Antonio Greco

GRECO-Workshop on Quantum Technology Applications and Artificial Intelligence.pdf

8 Quantum computing: a resource of today or tomorrow?

Quantum computing promises transformative computational advantages, yet the practical realization of many celebrated quantum algorithms — such as Quantum Fourier Transform (QFT) and Quantum Phase Estimation (QPE) — demands a large number of reliable logical qubits, placing fault-tolerant execution beyond the reach of current hardware. This requirement has fostered a widespread perception that useful quantum computation remains a future prospect rather than a tool for the present.
In this work, we want to highlight a complementary paradigm: quantum machine learning (QML). Unlike conventional quantum algorithms, QML approaches can actually benefit from the noisy nature of today's quantum devices. Hardware noise, often regarded as a limitation, can serve as an implicit regularizer that improves the optimization landscape and facilitates convergence during variational training—effectively turning a hardware drawback into a computational asset.
We emphasize the critical role of real quantum hardware in this context, arguing that classical simulators, while valuable for prototyping, cannot faithfully reproduce the stochastic environment that contributes to the observed training dynamics. We then present preliminary results from hybrid quantum-classical learning tasks that suggest genuine quantum efficiency in training, with noisy quantum circuits achieving competitive or superior convergence behavior compared with their noise-free simulated counterparts.
Our findings support the view that, within the QML framework, quantum computing is not merely a promise for tomorrow but an exploitable resource available today.

Speaker: Dr Roberto Cappuccio (Istituto Nazionale di Fisica Nucleare)

QC_Today_or_Tomorrow.pdf

9 Mixing reveals spectral and topological structure

In this talk, I present a unified view of quantum spectral probing in which structurelessness, in the form of unbiased mixing that avoids basis-dependent artifacts, is the key resource connecting density-of-states estimation and quantum topological data analysis. I will present DOS-QPE and its single-ancilla variant, which estimate global spectral features of Hamiltonians by probing them with mixed input states such as the maximally mixed state or mixtures over symmetry sectors. I will discuss how using the Acorn trick and its Haar/2-design randomness on a discarded purification register, we can realize these mixed probes in a way that is mathematically invariant yet more robust and verifiable, enabling randomness-based protocols while mitigating coherent errors. The same principle drives QTDA; to reliably extract Betti numbers from eigenspace degeneracies, we prepare structureless initial ensembles using efficient approximate unitary (t)-designs, replacing ideal Haar randomness with polynomial-depth circuits. Across these applications, controlled mixing and unbiasedness act as the common engine that turns spectral primitives into reliable geometric and topological tools.

Speaker: Stefano Scali (Fujitsu Research)

Welcome cocktail

20:00 Social Dinner

Friday 27 February

Introduction to Quantum Computing

Convener: Emanuele Marsili

Introduction to Quantum Computing

Convener: Emanuele Marsili

11:20 Coffee break

Quantum Computing for Business applications

Convener: Emanuele Marsili

Quantum Computing Quantum Information Communication & Criptography

10 Effective counterdiabatic quantum control

We present a scheme for the design of quantum control protocols based on shortcuts to adiabaticity. To fight decoherence, the adiabatic dynamics is accelerated. Introducing high-frequency modulations in the control Hamiltonian we mimic a time-dependent counterdiabatic correction. Applications are presented for the high-fidelity realization of quantum state transfers and quantum gates based on effective counterdiabatic driving, in platforms ranging from superconducting circuits to Rydberg atoms [1]. We sketch as well related ideas to control many-body interactions [2] and to counteract evolution errors by compensating terms in the Hamiltonian.

[1] F. Petiziol, F. Mintert, S. Wimberger, EPL 145, 15001 (2024)
[2] S. Dengis, S. Wimberger, P. Schlagheck, PRA 111, L031301 (2025)

Speaker: Prof. Sandro Wimberger (Università di Parma e Istituto Nazionale di Fisica Nucleare (Milano Bicocca, gruppo Parma))

11 La strada verso il vantaggio quantistico: stato dell'arte di IBM Quantum

Negli ultimi anni si è arrivati ad un ottimo livello di sviluppo dei computer quantistici che ne ha dimostrato l’utilità in alcuni ambiti di ricerca. Si parla infatti di “quantum utility” che può essere raggiunta utilizzando i computer quantistici insieme a supercomputer tradizionali che li supportano nell’esecuzione dei calcoli e nella mitigazione degli errori.

IBM, in base alla sua roadmap di sviluppo tecnologico sta lavorando alla realizzazione di sistemi quantistici con correzione degli errori che saranno resi disponibili dal 2029. Sarà tuttavia possibile dimostrare il vantaggio quantistico, ovvero la possibilità da parte di un computer quantistico di effettuare calcoli non simulabili da sistemi tradizionali, anche prima di quella data per alcuni domini di ricerca.

Lo sviluppo della computazione quantistica arriverà presto a produrre dei calcolatori in grado di portare grandi vantaggi nella ricerca scientifica e nella produzione industriale. Per cogliere questi benefici è fondamentale creare nuove competenze che permettano di sfruttare per primi i vantaggi di questa tecnologia, facendosi allo stesso tempo trovare pronti ad affrontare possibili futuri attacchi cibernetici basati su tecnologie quantistiche.

Speaker: Federico Mattei (IBM)

13:00 Lunch break

Machine Learning & Quantum Machine Learning

12 Quantum machine learning for High Energy Physics

In this talk, an overview of quantum machine learning (QML) activities developed within the LHC experiments will be presented, with particular emphasis on their applications to reconstruction, classification and simulation tasks. Examples include the jet flavour identification as well as quantum generative models aimed at accelerating the detector simulations. It will be shown how quantum algorithms can potentially compete with state-of-the-art classical machine-learning methods. Finally, prospects and challenges for deploying QML tools on near-term quantum hardware and their integration into future data processing strategies for High Energy Physics will be discussed.

Speaker: Lorenzo Sestini (Istituto Nazionale di Fisica Nucleare)

quantum_LHC.pdf

13 Variational quantum circuits: quantum convolutional neural network and quantum GAN

Variational quantum circuits (VQCs) are among the most promising frameworks for near-term quantum computing, offering a flexible and hardware-efficient approach to leveraging current noisy intermediate-scale quantum (NISQ) devices. In this talk, we provide a structured overview of VQC architecture, discussing its key building blocks: the data encoding stage, which embeds classical information in the quantum Hilbert space, and the parameterized ansatz, which constitutes the trainable core of the circuit and is optimized via a classical feedback loop.
Building on this foundation, we present two practical applications of the VQC paradigm. The first is a hybrid quantum-classical convolutional neural network (QCNN), in which quantum convolutional and pooling layers are designed to extract features from structured data while progressively reducing the size of the quantum register. The second is a quantum generative adversarial network (QGAN), in which a quantum generator learns to produce data distributions that a classical or quantum discriminator cannot distinguish from real data.
For both architectures, we discuss simulation results that validate the proposed circuit designs and training strategies. We then present early experimental results obtained on IQM's EMERALD quantum processor, providing an initial assessment of how these models perform on real superconducting hardware. The comparison between simulated and hardware-executed runs provides insight into the impact of device noise on training dynamics and output quality, reinforcing the importance of beyond-simulation benchmarking for variational quantum machine learning.

Speaker: Roberto Cappuccio (Istituto Nazionale di Fisica Nucleare)

vqc_presentation_ver1.1.pdf

15:20 Coffee break

Frontiers of Physics

14 Universal Properties and Phase Diagrams of Quantum Long-Range Systems

Several recent experiments in atomic, molecular and optical systems motivated a huge interest in the study of quantum long-range spin systems, following a long-standing interest in classical interacting long-range systems. The goal of the talk is to present a general description of the critical behavior and phases of long-range systems. In the first part I will first discuss and review results on classical long-range systems. By introducing a convenient ansatz for the effective action, one can determine the phase diagram for the N-component quantum rotor model with long-range interactions, with N=1 corresponding to the Ising model. The phase diagram shows a non-trivial dependence on the power-law exponent of the spatial decay of the couplings.

Speaker: Prof. Andrea Trombettoni (University of Trieste & INFN)

15 Entanglement Properties of Quantum Complex Networks

Graph states play a central role in quantum information science, providing a unifying framework for measurement–based quantum computation, quantum error correction, and the study of many–body entanglement. When a graph structure is used to prescribe a set of pairwise interactions among qubits, the resulting state captures how the connectivity pattern influences the generation and distribution of quantum correlations. Understanding how entanglement responds to structural properties of the underlying graph is therefore of broad interest, especially when the graph is drawn from an ensemble.

In this talk, I will present an explicit closed-form expression for the entanglement as a function of the underlying graph geometry [1,2]. I will first discuss explicit examples of network topologies and illustrate the application of the formula to specific graphs, highlighting its potential implications for the design of entanglement–optimized quantum networks and for graph–structured models in quantum machine learning, where controllable entanglement is a key resource. I will then extend the analysis to ensembles of graphs with statistical structure, focusing in particular on the Erdős–Rényi model. In this setting, the entanglement exhibits a phase–transition–like behavior that is distinct from the classical transition that characterizes the standard Erdős-Rényi model.

[1] De Simone L and Franzosi R 2025 Journal of Physics A: Mathematical and Theoretical 58 415302 URL: https://doi.org/10.1088/1751-8121/ae0bcb
[2] De Simone L and Franzosi R Advanced Quantum Technologies n/a e00514 (Preprint https://advanced.onlinelibrary.wiley.com/doi/pdf/10.1002/qute.202500514)

Speaker: Lucio De Simone (University of Siena - INFN Perugia)

Conference_DeSimone.pdf

16 Multipartite entanglement generation in inflationary scenarios

We explore novel generation of genuine multipartite entanglement in the context of cosmological inflation. In particular, we apply standard perturbative techniques to quantify entanglement production, focusing on the recently proposed Entanglement Distance, which introduces a geometric interpretation of quantum correlations in terms of the Fubini-Study metric. We first discuss how multipartite entanglement emerges from gravitational particle production processes, currently object of intense studies to address dark matter origin and analogue models of gravity. Then, we investigate the entanglement features arising from primordial non-Gaussian quantum fluctuations, which are expected to play a key role in driving the quantum-to-classical transition of cosmological perturbations. We show that a complete understanding of entanglement generation in cosmological settings requires a fully multipartite approach, further discussing potential observational implications for dark matter searches and the Cosmic Microwave Background (CMB) radiation.

Speaker: Alessio Belfiglio (University of Camerino)

17 Entanglement Distance: A Geometric Framework for Efficient Multipartite Entanglement Estimation

Quantum entanglement is a foundational resource in quantum information theory, yet its characterization in multipartite systems remains a significant open challenge. In this talk, we investigate entanglement from a geometric perspective, focusing on the Riemannian structure induced by the Fubini–Study metric on the projective Hilbert space of multi-qubit states. By exploiting the local-unitary invariance of this metric, we introduce Entanglement Distance (ED), a measure that quantifies entanglement as the minimum sum of squared Fubini–Study distances between a state and its locally conjugate counterparts.

We analyze the topological and analytic properties of ED for pure multi-qubit states. Furthermore, we bridge the gap between geometric theory and experimental practice by demonstrating how ED can be efficiently estimated on quantum processors. This framework provides a physically robust and computationally accessible tool for benchmarking entanglement in the next generation of quantum devices.

Speaker: Lorenzo Capra (Università degli Studi di Siena)

18 Decoherence mitigation via repeated joint weak measurements in entangled two-qubit register

In practical quantum processors, residual coupling to uncontrolled environmental degrees of freedom unavoidably induces decoherence, washing out phase relations and progressively destroying entanglement, which are key resources for quantum sensing and computation [1,2]. We report the results of further development of the approach proposed in [3], which suppresses decoherence in an entangled two-qubit register by applying multiple joint weak measurements that are equally spaced in time. The scheme is conceptually related to the quantum Zeno effect [4], but it departs from the usual requirement of extremely frequent projective measurements that aim to freeze the entire evolution. Instead, the protocol operates at moderate measurement cadence and exploits the presence of entanglement: the cumulative back-action of many weak joint measurements steers the state toward a Bell-type subspace while keeping measurement-induced disturbance controllable.
In the proposed algorithm, joint weak measurements are performed on the subspace spanned by the Bell states. Between measurement steps, the density matrix evolves according to Lindblad equation, capturing relaxation, dephasing, and temperature-driven effects [2]. Each weak measurement (parameterized by a strength $q$) provides controlled, nonselective bias toward the targeted entangled subspace without fully collapsing the state. Repeating this operation $N$ times over a fixed time window reshapes the open-system trajectory so that coherence-bearing off-diagonal elements decay more slowly than in free evolution. We quantify coherence by the off-diagonal terms of the density matrix (in the chosen basis) as $\ell_1$-norm [5], and we quantify entanglement using concurrence [6]. A key analytical result is that, for an optimal measurement choice, the short-time entanglement decay becomes dominated primarily by dephasing, while relaxation and temperature-dependent contributions strongly suppressed. Numerical simulations confirm that this slowdown persists over finite times.
To compare regimes, we introduce efficiency ratios based on initial decay slopes, the time to reach a fixed concurrence threshold, and the analogous slope ratio for $\ell_1$-norm. These indicators behave consistently and reveal a useful optimum at a moderate number of measurements for the studied parameters, while significantly degrading when the measurement strength $q$ is reduced below its optimal value. The protocol is especially relevant when the exact state to be preserved is not known, but information about the entanglement structure is available, for instance, in quantum algorithms where a subset of qubits remains idle between gate operations.

References:
1. M. M. Wolf, J. Eisert, T. S. Cubitt, J. I. Cirac (2008) - Assessing Non-Markovian Quantum Dynamics, Phys. Rev. Lett. 101, 150402. DOI: 10.1103/PhysRevLett.101.150402.
2. H.-P. Breuer, E.-M. Laine, J. Piilo, B. Vacchini (2016) - Colloquium: Non-Markovian dynamics in open quantum systems, Rev. Mod. Phys. 88, 021002. DOI: 10.1103/RevModPhys.88.021002.
3. O. M. Konovalenko, Z. A. Maizelis (2025) - Multiple Joint Weak Measurements as a Way to Suppress the Decoherence, Ukrainian Journal of Physics 70(8), 516–523. DOI: 10.15407/ujpe70.8.516.
4. B. Misra, E. C. G. Sudarshan (1977) - The Zeno’s paradox in quantum theory, J. Math. Phys. 18, 756–763. DOI: 10.1063/1.523304.
5. T. Baumgratz, M. Cramer, M. B. Plenio (2014) - Quantifying Coherence, Phys. Rev. Lett. 113, 140401. DOI: 10.1103/PhysRevLett.113.140401.
6. W. K. Wootters (1998) - Entanglement of Formation of an Arbitrary State of Two Qubits, Phys. Rev. Lett. 80, 2245–2248. DOI: 10.1103/PhysRevLett.80.2245.

Speaker: Oleksandr Konovalenko (O.Ya. Usikov Institute for Radiophysics and Electronics)

Conclusions

qaw21026-bis.pdf