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Diving into the fascinating realm of quantum systems, the seminar will offer a panoramic view of long-range interactions. Starting with an encompassing tour of critical phenomena in systems featuring power-law interactions 1/𝑟𝛼 at 𝛼 < 𝑑, we’ll unveil the intricate equilibrium scaling dependence on the power-law exponent α. We will then take a deep dive into the dynamic world of “strong” long-range systems with 𝛼 < 𝑑, where we’ll unravel the counterintuitive features of out-of-equilibrium scaling dynamics during sudden and gradual quenches.

Zoom link: https://zoom.us/j/95634523890?pwd=N1QrSEpScG9lU3NrU1JCU3FVbDREZz09

Testing the limits of validity of the superposition principle is of crucial importance in the foundations of quantum mechanics and the development of quantum technologies. A way to quantify possible breakdowns of the superposition principle is given by collapse models. These models modify the Schrödinger equation, by adding non-linear and stochastic terms which describe spontaneous collapse in space of the wavefunction. The effects of the non-linear terms are negligible for microscopic systems however, because of an amplification mechanism built in the models, they become dominant for macroscopic objects, providing in this way a natural solution to the measurement problem. Because in collapse models the Schrödinger equation is modified, they make different predictions compared to Quantum Mechanics, hence they can be tested in experiments. We will introduce the most relevant collapse models, the Continuous Spontaneous Localization (CSL) model, and the Diósi-Penrose (DP) model. Then, we will give a summary of the current bounds set by different experiments on their phenomenological parameters. In particular, we will focus on experiments based on the study of radiation emission from matter.

The physical properties of many complex Quantum Materials (QM), like transition metal oxides, are the results of a complex interplay among electrons, phonons, and magnons. This complexity makes the properties of QM highly susceptible to external factors such as pressure, doping, magnetic fields, or temperature. This leads to the intricate phase diagrams found in many families of compounds and, in turn, provides the means to switch between completely different macroscopic functionalities by a fine tuneing of “control parameters”, such as temperature or pressure. The same susceptibility also renders QM an ideal platform for designing experiments where tailored electromagnetic fields interacting with matter can lead to the emergence on ultrafast timescales of novel, occasionally exotic, physical properties. This aspect has been explored in time domain studies [1] and has led to different proofs that ultrashort mid-IR light pulses can “force” the formation of quantum coherent states in matter. Those findings disclose a new regime of physics where photo- excitation can be used to dynamically “sustain” quantum coherence, thermodynamic-limits may be bridged and quantum effects can, in principle, appear at ambient temperatures, where thermal fluctuations normally inhibits them at equilibrium. In this presentation, I will review our recent results in archetypal strongly correlated cuprate superconductors and demonstrate the feasibility of a light-based control of quantum phases in real materials [2,3,4]. I will then introduce our new approaches to time domain spectroscopy going beyond mean photon number observables [5-10] and show that the statistical features of light can provide information on superconducting fluctuations beyond standard linear and non-linear optical spectroscopies [11]. Finally, I will elaborate on our current research effort to use cavity electrodynamics to control the onset of quantum coherent states in complex materials [12].

[1] Advances in physics 65, 58-238 (2016)

[2] Science 331, 189-191 (2011)

[3] Phys. Rev. Lett. 122, 067002 (2019)

[4] Nature Physics 17, 368–373 (2021)

[5] Phys. Rev. Lett. 119, 187403 (2017)

[6] New J. Phys. 16 043004 (2014)

[7] Nat. Comm. 6, 10249 (2015)

[8] PNAS March 19, 116 (12) 5383-5386 (2019)

[9] J. of Physics B 53, 145502 (2019)

[10] Optics Letters 45, 3498 (2020)

[11] Light: Science & Applications 11, 44 (2022)

[12] Nature 622, 487–492 (2023)

https://zoom.us/j/99542736244?pwd=U1YwVVZTVWtOOXE3QjFJbWlrQ1AyQT09

In addition to the usual projective measurements, quantum mechanics allows for alternative ways to extract information from a quantum system. Some of these lead to a quasi-probability distribution for the observable measured which are not positively defined. In perfect analogy with the Wigner quasiprobability distribution, the presence of negative regions in the distribution can be used to spot pure quantum behaviors of the system or the dynamics. I will present a particular scheme, called quantum non-demolition, where quasi-probability distribution arises naturally. It exploits an additional quantum detector coupled to the system to be measured which allows us to gain important information about the wave-function of the system. I will discuss what are the advantages and disadvantages of this approach with some practical examples: the measure of the work done on a quantum system driven by an external field and the calculation of the derivative of a quantum operator. In the last part of the talk, I will discuss the connection with the violation of the Leggett-Garg inequalities, and how this approach identifies pure quantum effects and quantum-to-classical transition due to the interaction with an environment.

https://zoom.us/j/91331596512?pwd=RFlwdTA5OTNYRmFaNGxjYUphclpFUT09

As the 21st century unfolds, quantum physics and information theory continue to increase their impact on science and modern technology. Today, a frontier of our current knowledge is made by Complex Quantum Systems: many-body, out-of-equilibrium, open quantum systems interacting with highly structured environments. From gauge theories to complex molecules and quantum devices, understanding and controlling their information processing capabilities is a formidable task that lies at the interface of both technological progress and fundamental science. Therein lie several challenges, old and new, that will benefit from a new perspective and a brand-new set of tools. In this Colloquium I will introduce Conditional Ensembles, a new way to study open quantum systems that includes microscopic information about their environment. After introducing and discussing the significance of this new approach, I will give a bird’s-eye-view of the recent results achieved using this framework, and outline some future research work, aimed at synthesizing a new understanding of how quantum information is structured and processed in complex quantum systems.

Zoom link: https://zoom.us/j/97893050441?pwd=UDdFZkNuaHhYd3A3MWplcDVOajdvdz09

Recent experiments with quantum simulators and noisy intermediate-scale quantum devices have demonstrated unparalleled capabilities of probing many-body wave functions, via directly probing them at the single quantum level via projective measurements. However, very little is known about how to interpret and analyze such huge datasets. This represents a fundamental challenge for theory to understand experimental data, that is also relevant to other fields where similarly large data sets are routinely explored – from classical simulations of gauge theories, to observatory studies of many-body ensembles.

In this talk, I will show how it is possible to provide such characterisation of quantum hardware via direct and assumption-free data mining. The core idea of this programme is the fact that snapshots of many body systems can be construed as a very high-dimensional manifold. Such a manifold can be characterized via basic topological concepts, in particular, by their intrinsic dimension, and by advanced theoretical tools from network theory and non-parametric, unsupervised learning.

This new approach to the many-body problem opens up a cornucopia of methods to connect physical properties to a stochastic sampling of the system wave function. I will focus here on two specific applications. Firstly, I will discuss theoretical results for both classical and quantum many-body spin systems that illustrate how data structures undergo structural transitions whenever the underlying physical system does, and display universal (critical) behavior in both classical and quantum mechanical cases. These results pave the way for a systematic understanding of field theory aspects in data space, a topic of current interest in particle and statistical physics. Secondly, I will discuss how our methods allow to track Kolmogorov complexity in quantum simulators and quantum computers, providing novel insights into the working of such systems, in terms of both practical and fundamental aspects – including cross-certification of quantum devices, a grand challenge in the field.

Zoom link: https://zoom.us/j/93164444063?pwd=S25EbkVBUFdicjUwemxCL01taTgvZz09

The so-called second quantum technological revolution is evolving at a rapid pace and promises significant impacts not only on science but also within the industrial sector. Progress in this field critically relies on efficient methods for controlling the quantum properties of systems and their dynamics. In this talk we are going to give two illustrative examples of how, using a key algorithm in modern machine learning, automatic differentiation, we can control the properties of interest of a quantum system. Our initial case study focuses on the control of the tunneling probability of particles in a two-mode system. We show that when the quantum system is coupled to an ancilla, one can learn the optimal ancillary component and the optimal coupling, such that the tunneling probability/time can be controlled. The subsequent example addresses the mitigation of decoherence within a quantum system with noise. Employing a similar methodology, we show how we can learn an ancillary system and its corresponding noise parameters to counteract and diminish the impact of system noise.

Artificial atoms in solids are leading candidates for quantum networks, scalable quantum computing, and sensing, as they combine long-lived spins with mobile and robust photonic qubits. A central goal is to realize photonic platforms that can scale and individually address and control single atoms. Recently, silicon has emerged as a promising host material where artificial atoms with long spin coherence times and emission into the telecommunications band can be controllably created and addressed. This field leverages the maturity of silicon photonics to embed quantum emitters into the world’s most advanced microelectronics and photonics platform. However, a current bottleneck is the naturally weak emission rate of artificial atoms. An open challenge is to enhance this interaction via coupling to an optical cavity. In my talk, I will discuss the integration of silicon color centers in optical cavities and show the enhancement of their brightness when successful cavity-atom coupling is achieved. I will further discuss the prospects for their applications in the context of quantum information processing.

CERN has started its Quantum Technology Initiative in order to investigate the use of quantum technologies in High Energy Physics (HEP). A three-year roadmap and research programme has been defined in collaboration with the HEP and quantum-technology research communities. In this context, initial pilot projects have been set up at CERN in collaboration with other HEP institutes worldwide on Quantum Computing and Quantum Machine Learning in particular. This talk will provide an overview of recent results obtained by the different studies, focusing on current usage of quantum machine learning techniques for HEP use cases and beyond.