Month: April 2024

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.