The phase diagram of QCD at finite densities remains numerically inaccessible by classical computations. Quantum computers, with their potential for exponential speedup, could overcome this challenge. However, their current physical implementations are affected by quantum noise. In this contribution, I will introduce a novel quantum error mitigation technique based on a BBGKY-like hierarchy,...
Every High-Energy Physics (HEP) experiment exhibits a unique signature in terms of detector efficiency, geometric acceptance, and software reconstruction. The resulting effects alter the original observable distribution (given by nature or simulated at parton level) by adding smearing and biasing stochastic terms. Unfolding is the statistical technique devoted to the retrieval of the original...
Flow equations, introduced in the 1990s, have proven to be a powerful tool for Hamiltonian diagonalization in various fields, including high-energy physics. Inspired by this classical method, we present double-bracket quantum algorithms (DBQAs) as a quantum counterpart for diagonalizing Hamiltonians.
We explore the compilation of DBQAs into quantum circuits by proposing a quantum circuit...
Understanding the capabilities of classical simulation methods is key to identifying where quantum computers are advantageous. Not only does this ensure that quantum computers are used only where necessary, but also one can potentially identify subroutines that can be offloaded onto a classical device.
Motivated by these considerations, in our recent works [1,2], we present a classical...
The 3-pages abstract is submitted in the attachments. (VQC_symmetry_abstract.pdf)
We wrote the full introduction and summarized the main results with data and plots. We fear that the content section here is not enough to house all those material. Please take a look at the 3-pages abstract prepared according to the guidance found in submission instructions.
Thanks in advance.
Machine learning with quantum computing has garnered worldwide interest for its transformation potential in science and technology. At the core of quantum computing lies the concept of quantum information, with quantum entanglement serving as a cornerstone of this paradigm. Entanglement detection and quantification of quantum states are a vital challenge in the current noisy intermediate-scale...
Quantum Chromodynamics allows the discovery of new states and phases of matter, among which is the Quark-Gluon Plasma (QGP). Jets are one of the most common QGP probes that are not only produced inside the QGP medium but also their behavior in the vacuum is well‑understood, despite their complexity. Moreover, jets’ properties are modified due to the interaction with the medium, which is...
Despite the considerable potential of the field of Quantum Machine Learning (QML) as a promising avenue within the broader field of ML, it is currently facing significant challenges. These include various sources of Barren Plateaus [1] that restrict scalability and the lack of a clear advantage over classical ML. Furthermore, the inherent limitations of real devices, such as noise, further...
Lattice gauge theories (LGTs) are essential tools for studying fundamental interactions in particle physics and have broad applications in condensed matter physics and quantum information. While many aspects of Abelian and non-Abelian gauge theories can be simulated efficiently with classical numerical methods, their intrinsic quantum nature makes other relevant phenomena hard to reproduce....
Diffusion models (DMs)[1]have emerged as top contenders for generating data and images in AI,
offering superior quality and training stability compared to generative adversarial networks (GANs).
Latent DMs [2], which operate in latent rather than pixel space, are particularly advantageous for
processing large-scale images efficiently, saving computation time and energy.
In the realm of...
See pdf
The poster titled "Optimisation of Multi-Qubit Chip Topology" focuses on designing scalable and efficient architectures for multi-qubit quantum processors. The research highlights superconducting qubits, known for their controllability and role as fundamental units of quantum information in quantum computing. This study emphasises the importance of parameters such as entanglement, quantum...
Permutation and Lorentz Invariance in Quantum Kernels
Authors:
Michele Grossi (CERN)
Santeri Laurila (CERN & Helsinki Institute of Physics HIP)
Massimiliano Incudini (Intel)
Väinö Mehtola (HIP & VTT Technical Research Centre)
Date: December 2024
Introduction
Symmetries are central to the standard model and many other frameworks in physics. When...
We demonstrate a method to study the phase diagram of a quantum system on quantum devices via adiabatic preparation of states. The method is a direct application of the adiabatic theorem due to M. Born and V. Fock, Z. Phys. 51, 165 (1928). The key idea of the method is to individually evolve the ground state and the first-excited state using a Hamiltonian whose parameters are time-dependent....
Understanding the substructure of jets is a fundamental challenge in high-energy physics due to its inherent complexity and multi-scale dynamics. While classical methods such as Monte Carlo simulation serve as powerful tools for reproducing the phenomenological properties of jets, such methods struggle to accurately capture the intricate correlations and stochastic processes governing jet...
Various computationally challenging tasks in high energy physics can be formulated as quadratic unconstrained binary optimization (QUBO) or Ising problems. The problem is designed so that the ground state of the QUBO/Ising model provides the correct answer. It is a Nondeterministic Polynomial-time (NP) complete problem, and the solution candidates diverge exponentially with the problem size....
The QUANTEP project focuses on developing and implementing a comprehensive silicon photonics integrated circuit for quantum computation using linear quantum optics and single photons. A key prototype under investigation is the universal two-qubit Controlled-NOT (C-NOT) gate, which employs a linear, coincidence basis gate requiring only single photons as inputs. This prototype serves as a...
We employ a variational quantum algorithm to study the chiral condensate of 1+1 dimensional SU(2) non-Abelian gauge theory at different temperatures and chemical potentials. Our algorithm is tested both by classical simulations and on real quantum computers. We observed the breaking and restoration of chiral symmetry. Our simulation results are in good agreement with theoretical calculations...
Subspace preserving quantum circuits are a class of quantum algorithms that, relying on some symmetries in the computation, can offer theoretical guarantees for their training. Those algorithms have gained extensive interest as they can offer polynomial speed-up and can be used to mimic classical machine learning algorithms. In this work, we propose a novel convolutional neural network...
As the high-luminosity LHC era approaches, high-energy physics requires increasingly efficient computational methods for event reconstruction. We present a novel approach to charged particle track reconstruction using the LHCb Vertex Locator as a case study. The method relies on minimizing an Ising-like Hamiltonian via matrix inversion, where classical solutions achieve state-of-the-art...
