We explore the phase structure of the lattice Schwinger model in the presence of a toplogical -term, a regime in which conventional Monte Carlo simulations suffer from the sign problem, using the variational quantum eigensolver (VQE). Constructing a suitable variational ansatz circuit for the lattice model using symmetry-preserving 2-qubit gates, we perform classical simulations showing that...
This work investigates the capability of digital quantum computers to probe the phase diagram of the Schwinger model, which includes a single fermion flavor and a topological theta-term. The study aims to extend the analysis to higher dimensions where prevailing classical techniques exhibit inefficiency. Employing the variational quantum eigensolver (VQE), a range of ansatz architectures and...
The Schwinger model describes the QED in two-dimensional space-time. In this talk, I will present some results on the excited states of the lattice Schwinger model with the concurrent variational quantum eigensolver (cVQE). First, I will introduce elements of cVQE from the density matrix. Then, I will report results on the spectrum of the lattice Schwinger model, including nonvanishing...
Collider experiments play a central role in understanding the subatomic structure of matter, as well as developing and verifying the fundamental theory of elementary particle interactions. However, comprehending scattering processes at a fundamental level in theory remains a significant challenge. The necessary involved time evolution and the with time rapidly increasing bond dimension in...
We extend the analysis of the study of 2+1D Quantum Electrodynamics with an approach suitable for quantum computers by developing an algorithm that computes the Hamiltonian in both electric and magnetic basis for a lattice with generic size.
The system that we are considering for a matching with MCMC (Markov Chain Monte Carlo) data is 3x3 pure gauge lattice with periodic boundary conditions....
The rapid development of quantum technologies gives hope for unprecedented physical simulations in the upcoming years. One of them is lattice QCD, which however is computationally too expensive at the moment. This motivates the study of simpler theories resembling its features. One is QED in 2+1 dimensions, showing a confining phase and dynamical mass generation. In this work we present a...
In recent years, machine learning has emerged as a prominent subfield of artificial intelligence, finding widespread applications in various scientific domains. Concurrently, the promising potential of quantum computing has inspired the exploration of leveraging its computational advantages to construct quantum machine learning models. Building upon the remarkable success of classical...
The use of quadratic unconstrained binary optimisation (QUBO) for charged particle track reconstruction has been studied alongside a classical tracking algorithm using combinatorial Kalman Filter (CKF). QUBO can be efficiently solved on quantum computers through methods like quantum annealing or variational quantum eigensolver. The QUBO and CKF tracking methods not only differ in terms of the...
Particle track reconstruction plays a crucial role in the exploration of new physical phenomena, particularly when rare signal tracks are obscured by a significant background. The formulation of the reconstruction task as Quadratic Unconstrained Binary Optimisation (QUBO) enables the use of quantum computers, which are believed to offer an advantage over classical computers in such...
Our way towards quantum generative models for experimental calorimeter shower generation and lessons learned are described.
Our best model: the Quantum Angle Generator (QAG) is a new quantum machine learning model designed to produce precise images on current Noise Intermediate Scale (NISQ) Quantum devices. The QAG model uses variational quantum circuits as its core, and multiple circuit...
In this work we propose an algorithm for a QBM using DualQITE for the Gibbs state preparation. The choice of the Hamiltonian, which defines model connectivity, and the ansatz, used for preparation of the Gibbs state, plays a crucial role in performance of QBMs. With an application to classification tasks, we conduct a study on semi-restricted and fully-connected QBMs to explore the influence...
This project focuses on the development of methods for computing real-time dynamics of thermal states for quantum field theories on quantum devices using the 1D Ising model as a testbed. This research is motivated by the challenges faced by conventional classical methods for such systems.
We propose to prepare the thermal state on a quantum state via variational imaginary time evolution...
In the application of the Variational Quantum Eigensolver (VQE) for determining ground state energies of hamiltonian, getting trapped in local minima or saddle points is a recurrent challenge. This talk explores the unconventional idea of utilizing stochastic noise as an ally, rather than an adversary. Drawing parallels with classical optimization, we'll discuss how noise can aid VQE in...
Combinatorial optimization (CO) has many important applications in business and science. Many CO problems are NP-hard and are routinely solved by classical heuristics. It is widely believed that quantum computers cannot solve such problems efficiently, but significant effort is put into designing quantum algorithms that provide fast and reliable approximate solutions. In the era of noisy...
The aim of this talk is to introduce matrix models and discuss how and why to compute them on the quantum computer.
