I will discuss some recent progress on obtaining truncated Hamiltonians for QCD. After a brief motivation, I will discuss how an expansion in 1/Nc yields a large reduction in the available Hilbert space as well as a much simpler Hamiltonian. This has allowed a simulation of 2+1D SU(3) YM theory on IBM quantum computers on an 8x8 lattice. Next, I will discuss how subleading orders in 1/Nc as...
Recent advances in quantum hardware and quantum information science are opening new avenues for tackling longstanding challenges in fundamental physics. In this talk, I will discuss how emerging quantum platforms can be used to simulate aspects of QCD. A particular focus will be on trapped ion quantum computers, which offer a unique capability: they can naturally represent both fermionic and...
Ultralight scalar fields have emerged as a compelling alternative to the standard cold dark matter paradigm. Their wave-like nature, governed by the Schrödinger–Poisson system, leads to new predictions for structure formation, including the presence of solitonic cores with flat central density profiles in dark matter halos. In this talk, I present an overview of FDM models both with and...
In this work, we probe string modes of motion with dynamical matter in a digital quantum simulation of a
(2+1) dimensional gauge theory using an IBM superconducting quantum processor with up to 144 qubits. The Z2-Higgs model is realized through a direct mapping of matter and gauge fields onto heavy-hex qubits, with circuit depths reaching nearly 200 entangling layers. By leveraging the...
Measurements in fundamental physics are becoming increasingly difficult. In particular, many high-precision measurements are now dealing with the intrinsic quantum mechanical noise of the detectors themselves. LIGO is an example: it is limited by Heisenberg uncertainty in the laser light. However, this quantum noise can in principle be engineered away by clever use of quantum resources. I will...
The quantum rotor provides a simplified yet nontrivial framework for investigating topological charge in one spatial dimension. In classical lattice field theory, accessing fixed topological sectors typically requires reweighting techniques or constrained sampling. Quantum simulation, by contrast, offers a natural alternative: projective measurements collapse the system into sectors with...
Theoretical predictions at high-energy colliders are based on encoding the quantum fluctuations that occur at very short distances in the collision by Feynman diagrams. These diagrams are made up of interaction vertices and propagators, which in fact represent a quantum superposition of propagation in the two directions between two interaction vertices. So, this allows us to map Feynman...
A flagship application of quantum computing is the simulation of other quantum systems. In this talk, I will show how quantum computers can simulate scattering in the high-energy regime of QCD probed by colliders like the LHC. In particular, I will present techniques to calculate QCD Feynman diagrams and their interferences using a quantum computer. The colour parts of the interactions are...
Quantum sensing offers significant advantages over classical techniques when detecting extremely weak signals, such as those from dark matter, by leveraging entanglement and superposition to achieve greater sensitivity and precision. There are two main approaches in quantum sensing: adapting classical signal processing methods to the quantum domain and developing novel quantum algorithms and...
Non-perturbative processes such as scattering and false-vacuum decay in quantum field theory (QFT) play a central role in cosmology and particle physics, but remain intractable for classical simulation due to their highly entangled dynamics. We introduce a framework for simulating the real-time dynamics of interacting scalar QFTs in the continuous-variable quantum computing (CVQC) paradigm,...
The intersection of quantum computing and machine learning offers novel pathways to address long-standing challenges in fundamental physics. In this talk, I will present recent advances in applying quantum machine learning techniques to high-energy and many-body problems, highlighting how neural quantum states and variational quantum circuits can provide efficient representations of...
Quantum link models (QLMs) have gained attention in recent years, as a framework for discretising gauge theories which is especially suited to quantum computation approaches, and which often exhibit exotic phases of matter, allowing one to address dynamical properties related to quantum many-body scarring and Hilbert-space fragmentation which are otherwise difficult to study. We choose to...
This talk will explore how tools from quantum information science can provide new paradigms for machine learning and data processing in high-energy physics. I will first discuss the theoretical relationship between learning and compression, a cornerstone of information theory which holds that effective learning models inherently extract and compress meaningful patterns from data. Then, I will...
We introduce local information flows as a diagnostic to characterize out-of-equilibrium quantum dynamics in lattice gauge theories. We employ the information lattice framework, a local decomposition of total information into spatial and scale-resolved contributions, to characterize the propagation and buildup of quantum correlations in real-time processes. Focusing on the Schwinger model --- a...
Understanding flux string dynamics can provide insight into quark confinement and hadronization. First-principles quantum and numerical simulations have mostly focused on toy-model Abelian lattice gauge theories (LGTs). With the advent of state-of-the-art quantum simulation experiments, it is important to bridge this gap and study string dynamics in non-Abelian LGTs beyond one spatial...