OAR@UM Community:

The JWST hubble sequence : the rest-frame optical evolution of galaxy structure at 1.5
2023-01-01T00:00:00Z
Title: The JWST hubble sequence : the rest-frame optical evolution of galaxy structure at 1.5 109 M⊙ at z > 3 are not dominated by irregular and peculiar structures, either visually or quantitatively, as previously thought. We find a strong dominance of morphologically selected disk galaxies up to z = 6 in this mass range. We also find that the stellar mass and star formation rate densities are dominated by disk galaxies up to z ∼ 6, demonstrating that most stars in the Universe were likely formed in a disk galaxy. We compare our results to theory to show that the fraction of types we find is predicted by cosmological simulations, and that the Hubble Sequence was already in place as early as one billion years after the Big Bang. Additionally, we make our visual classifications public for the community.

Variational Gibbs state preparation on NISQ devices

2023-01-01T00:00:00Z

Title: Variational Gibbs state preparation on NISQ devices Abstract: The preparation of an equilibrium thermal state of a quantum many-body system on noisy intermediate-scale quantum (NISQ) devices is an important task in order to extend the range of applications of quantum computation. Faithful Gibbs state preparation would pave the way to investigate protocols such as thermalization and out-of-equilibrium thermodynamics, as well as providing useful resources for quantum algorithms, where sampling from Gibbs states constitutes a key subroutine. We propose a variational quantum algorithm (VQA) to prepare Gibbs states of a quantum many-body system. The novelty of our VQA consists in implementing a parameterized quantum circuit acting on two distinct, yet connected (via CNOT gates), quantum registers. The VQA evaluates the Helmholtz free energy, where the von Neumann entropy is obtained via post-processing of computational basis measurements on one register, while the Gibbs state is prepared on the other register, via a unitary rotation in the energy basis. Finally, we benchmark our VQA by preparing Gibbs states of the transverse field Ising and Heisenberg XXZ models and achieve remarkably high fidelities across a broad range of temperatures in statevector simulations. We also assess the performance of the VQA on IBM quantum computers, showcasing its feasibility on current NISQ devices.

EPOCHS VI : the size and shape evolution of galaxies since z ∼8 with JWST observations

2024-01-01T00:00:00Z

Title: EPOCHS VI : the size and shape evolution of galaxies since z ∼8 with JWST observations Authors: Ormerod, Katherine; Conselice, Christopher J.; Adams, Nathan J.; Harvey, Thomas A.; Austin, Duncan; Trussler, James A.A.; Ferreira, Leonardo De Albernaz; Caruana, Joseph; Lucatelli, Geferson; Li, Qiong; Roper, William J. Abstract: We present the results of a size and structural analysis of 1395 galaxies at 0.5 ≤ z ≲ 8 with stellar masses log (M*/M⊙)> 9.5 within the James Webb Space Telescope Public CEERS field that overlaps with the Hubble Space Telescope Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey EGS observations. We use GALFIT to fit single Sérsic models to the rest-frame optical profile of our galaxies, which is a mass-selected sample complete to our redshift and mass limit. Our primary result is that at fixed rest-frame wavelength and stellar mass, galaxies get progressively smaller, evolving as ∼(1 + z)−0.71 ± 0.19 up to z ∼ 8. We discover that the vast majority of massive galaxies at high redshifts have low Sérsic indices, thus do not contain steep, concentrated light profiles. Additionally, we explore the evolution of the size–stellar mass relationship, finding a correlation such that more massive systems are larger up to z ∼ 3. This relationship breaks down at z > 3, where we find that galaxies are of similar sizes, regardless of their star formation rates and Sérsic index, varying little with mass. We show that galaxies are more compact at redder wavelengths, independent of sSFR or stellar mass up to z ∼ 3. We demonstrate the size evolution of galaxies continues up to z ∼ 8, showing that the process or causes for this evolution is active at early times. We discuss these results in terms of ideas behind galaxy formation and evolution at early epochs, such as their importance in tracing processes driving size evolution, including minor mergers and active galactic nuclei activity.

Variational quantum algorithms for combinatorial optimisation : navigating the NISQ era

2025-01-01T00:00:00Z

Title: Variational quantum algorithms for combinatorial optimisation : navigating the NISQ era Abstract: Optimisation algorithms aim to solve a wide range of problems, including those related to physical systems and everyday challenges. Examples include optimising the shape or material distribution of structures to maximise strength and minimise weight, designing optimal investment portfolios, balancing risks and returns, planning efficient routes, optimising container placement on ships, and improving the manufacturing plan in factories. Optimising manufacturing plans in factories is the primary focus of my research. The goal of each optimisation problem typically involves reducing the costs by adjusting the algorithm’s configuration. For instance, in route optimisation, costs typically involve time and fuel, but constraints such as time windows or availability at destinations must also be considered. Incorporating more parameters can lead to solutions that better reflect real-world scenarios. However, increasing the number of parameters adds complexity to the problem, and more resources are required to achieve an optimal solution. In many practical applications, finding the exact optimal solution is often unnecessary. In many cases, finding a solution optimising the current status within an acceptable time frame is sufficient. The challenge of optimising the manufacturing plan in factories, often referred to as the Job-Shop Scheduling Problem (JSSP), involves balancing several parameters. These include sale orders and their requested delivery dates, the expected arrival dates for raw materials derived from the bill of materials of the products listed in sale orders, and resource availability, both machines and human resources required in the production process. Additionally, the process is subject to several constraints, such as sequential dependencies—for example, producing one component before another—and process coordination, such as synchronising different production stages, where manufacturing must be completed before packaging can begin. Operators balance these parameters and constraints to minimise costs, such as reducing changes in the configuration of machines or reducing clean-up tasks between the manufacturing of different components and maximising resource utilisation while keeping up with promised delivery dates to customers. The JSSP is a combinatorial optimisation problem that is NP-hard, meaning it becomes computationally difficult to compute as the number of parameters increases using classical computing methods. However, there are still various classical approaches to the problem, which result primarily in heuristic solutions. These include Integer Linear Programming (ILP), Dispatching Rules, Genetic Algorithms, or Simulated Annealing. In this dissertation, I investigate the quantum computing algorithm Quantum Approximate Optimization Algorithm (QAOA) to address the JSSP problem. Quantum computing is still in its infancy. Its foundation lies in quantum mechanics, which involves mathematical concepts such as linear algebra, complex numbers, and probability amplitudes. Key properties of quantum mechanics like superposition, entanglement and quantum interference allow quantum computers to explore the solution space of a problem more effectively than classical computers. In this dissertation, I first examine the properties of quantum mechanics, which can help me investigate QAOA and study tools that I can use to program a quantum computer. Then, I explore the mathematical models used to represent the problem, in this case, the Job-Shop Scheduling Problem (JSSP). JSSP involves several interdependent parameters. I expressed these parameters in mathematical models containing these interactions, some containing three or more parameters interacting with each other. The result was mathematical formulations involving problems with multivariate objective functions, which were then solved with QAOA. Another objective of my dissertation was to explore the possibility of reducing resource requirements while still achieving sufficiently good results based on relevant figures of merit. In this dissertation, I investigate various configurations of QAOA, evaluating their success while factoring in the computational resources required for execution to achieve an optimal result. I propose a configuration of QAOA, which I call k-interaction Angle QAOA (ka-QAOA). I show that ka-QAOA performs comparably to other proposed QAOA configurations while reducing the computational resources required to achieve similarly good approximations. While the results are promising, as parameters in problems increase, testing the different configurations of QAOA becomes a daunting task. More robust, error-free quantum computers are needed to model and solve real-world optimisation problems like JSSP. In the interim, we can still experiment with problems having few parameters to find optimal quantum algorithms. Description: M.Sc.(Melit.)