Speakers
Speakers
Program
Temporary Schedule, subject to changes
Talks
Variational quantum algorithms: an overview on motivation, limitations and their role to transition between NISQ and fault-tolerant era
Variational quantum algorithms have been extensively explored in the last decade, starting from the seminal papers of the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA). Variational approaches were born from the motivation of reducing quantum resources, mitigating errors and find approximate solutions to problems of interest before the advent of fault-tolerant quantum computing. During these years, variational approaches have democratised quantum computing and made them accessible to researchers from broad audiences, and a community intuition has been developed towards the utility and limitations of variational methods. In these lectures, we will review the basic principles of variational quantum computing, the most relevant algorithms, and fundamental limitations of the models. We will allocate time to the study of trainability and expressivity and their trade-offs, and will study barren plateaus as a tool to understand the connections between variational methods, quantum simulability, and even quantum advantages. By the end of the lecture, we expect to provide an overview of the state-of-the-art, the current challenges and a helpful collection of references.
Scalable quantum circuit compilation with quantum machine learning
Compiling many-body quantum dynamics into shallow, accurate, and hardware-efficient circuits is a central challenge in quantum simulation. In this talk, I will present a scalable approach to quantum compilation that combines tensor-network methods with ideas from quantum machine learning. Rather than relying solely on deterministic constructions such as Trotterization, these methods learn compact circuit representations of target dynamics from training data, enabling substantially lower gate costs while preserving high fidelity. These results show that tensor networks are not merely classical competitors to quantum computers, but powerful tools for training scalable quantum compilers. Across 1D, quasi-1D, and 2D settings, this framework achieves dramatic reductions in circuit depth, simulation error, and even fault-tolerant gate counts, pointing toward more practical quantum simulation on both near-term and future quantum hardware.
Quantum and Tensor-Network Methods for Combinatorial Problems
Tensor-network methods are powerful tools for representing and manipulating high-dimensional objects in a compact way. Originally developed in the context of quantum many-body physics, they are now used in a wide range of areas, including optimization, machine learning, and quantum information. In this lecture, I will introduce how tensornetwork ideas, combined with quantum-algorithmic constructions, can be used to study combinatorial problems. The lecture will discuss three examples. The first concerns integer factorization, where tensor-network methods are applied to instances of the closest vector problem arising from Schnorr’s algorithm. The second focuses on equational reasoning, showing how equivalence classes of symbolic expressions can be represented through the ground states of suitable Hamiltonians. The third example considers maximally compact polymers, where configurations of Hamiltonian cycles on two-dimensional lattices are encoded in the amplitudes of a quantum state, providing a route to estimate thermodynamic properties in simplified models inspired by polymer and protein-folding systems.
More quantum machine learning with less quantum gates
The practical realisation of quantum machine learning faces several major challenges such as expensive encoding of classical data in quantum states, trainability issues, and the requirement of millions of qubits for quantum error correction to overcome hardware noise. As quantum processors become increasingly mature in the next few years, a central question is: can we design algorithmic-level optimisations to reduce hardware requirements? This question is the central theme of this lecture. By developing novel techniques such as approximate state preparation, quantum residual learning networks, and partial error correction, we will propose resource-efficient solutions for trainable, accurate and robust quantum machine learning implementations on noisy hardware platforms.
Quantum Reservoir Computing and Quantum Extreme Learning Machines: Theory, Design, and Experiments
This session introduces quantum reservoir computing and quantum extreme learning machines as practical quantum machine learning frameworks for time-series and structured data. We will cover their core principles, including reservoir design, memory, measurements, and classical readout training, and discuss how complexity, input sensitivity, scalability, and hardware constraints shape their performance. The workshop will also include hands-on experiments on time-series forecasting, where participants will explore how design choices affect accuracy, scalability, and hardware compatibility. In addition, we will discuss broader applications of quantum reservoirs beyond forecasting, highlighting their flexibility for different learning tasks.
Quantum processors with neutral atom arrays: from analog simulators to quantum computers
Neutral-atom systems are among the most promising platforms for quantum technologies, with applications spanning quantum simulation, computation, precision sensing, and quantum networking. In this talk, I will discuss quantum processors based on trapped neutral atoms, highlighting their development from bespoke analog quantum simulators to increasingly programmable quantum computers. I will introduce the essential hardware capabilities of atom array systems—including neutral atom trapping, coherent control, state detection, and entangling operations—while also delving into the atomic-physics principles that underpin their performance, scalability, and versatility.
Foundations and Frontiers of Quantum Key Distribution: From BB84 to Regional Quantum Networks
Quantum Key Distribution (QKD) is one of the most mature applications of quantum information science, offering a method to establish cryptographic keys whose security is grounded in the laws of quantum mechanics rather than computational assumptions. This lecture will introduce the foundations of QKD, focusing in particular on the BB84 protocol, the first and most widely studied QKD scheme. We will review its basic principles, including quantum state preparation, measurement in non-orthogonal bases, and the role of quantum uncertainty in detecting eavesdropping.
The lecture will then discuss the main challenges that arise when moving from idealized protocols to practical implementations. These include photon losses, detector imperfections, finite-key effects, authentication requirements, integration with existing optical-fiber infrastructure, and the trade-offs between distance, key rate, and security assumptions. Finally, the talk will present an overview of ongoing regional projects aimed at deploying QKD technologies, to form a regional infrastructure for ultra-secure communication based on quantum technologies.
Quantum computing simulations in the natural sciences
Quantum computing has emerged as a transformative paradigm with the potential to address problems that remain intractable for classical computers due to unfavorable scaling. Recent progress in quantum technologies suggests that meaningful advances can already be achieved on near‑term noisy devices, provided that algorithms and workflows are carefully tailored to their capabilities and limitations. In this context, noise‑resilient quantum algorithms, error‑mitigation strategies, and hybrid quantum–classical schemes have been developed, each exploiting the complementary strengths of classical and quantum platforms. This interplay is proving essential for making quantum approaches practically viable in the near term.
A wide range of application domains stand to benefit from these developments. These include combinatorial optimization and machine‑learning tasks, applications in health care and life sciences such as molecular modeling and drug discovery, and fundamental physics problems ranging from quantum chemistry and materials science to lattice gauge theories in high‑energy physics. In many of these areas, classical methods face fundamental limitations when dealing with large, strongly correlated, or high‑dimensional systems. Quantum algorithms offer new pathways to explore these regimes, with the potential to transform our understanding of complex molecular processes, many‑body quantum phenomena, and the microscopic mechanisms underlying emergent physical behavior.