We approximate single particle wave functions for some given potential by expanding the solutions from diagonalizing the single particle problem in a harmonic oscillator basis.These single particle wave function approximations allow us to perform variational Monte Carlo (VMC) simulations without having explicit expressions for the single particle wave functions.We use the VMC simulations to analyze the ground states of quantum dots trapped in various confinement potentials, namely single and double harmonic oscillator potentials and single finite square well potentials.A point of interest is to see how many harmonic oscillator basis functions are needed for high quality approximations to the single particle wave functions for the different potentials.This is of interest because the number of basis functions needed is an important constraint of the method when it comes to computational efficiency.To verify the method used, we compare our ground state energy results with other ab initio many-body methods, such as the Similarity Renormalization Group method, the Coupled Cluster method, and the Full Configuration Interaction method, as well as with other VMC and DMC (Diffusion Monte Carlo) results.i Preface I first discovered physics during high school, thanks to my brother Alexander Fleischer.He knew how much I liked mathematics, so he recommended a course in physics to me.I ended up liking it even more than mathematics, and as a result, after high school I applied to the physics, meteorology and astronomy Bachelor program at the University of Oslo.The very first semester I was introduced to a field I liked just as much as physics, namely programming.Unfortunately, my Bachelor did not involve much programming after the first year.During the third year I had the option of taking an introductory course in computational physics, but I had several other physics courses I wanted to take, and I prioritized these over computational physics.When it was time to apply for a Masters program, I was unsure of what to pick.I then remembered how fun all the programming had been during the first year, and decided to give the combination of physics and programming a try, so I applied to the computational physics Masters program.The first semester I took the introductory computational physics course, and knew immediately that I had picked the right Masters program for me.I would like to thank my supervisor Morten Hjorth-Jensen for all the help and motivation he gave me during the past two years.I would also like to thank my brother Alexander for introducing me to physics in the first place, and for the helpful discussions we had about this thesis.Additionally, I would like to thank Håkon V. Treider for the fun times we have had sharing an office for the better part of two years, and for the help he has given me with general programming related issues.Finally, I would like to thank the various other people I shared an office with over the past two years, and the rest of the people at the computational physics research group, for making it a great place to be. Oslo,
We approximate single particle wave functions for some given potential by expanding the solutions from diagonalizing the single particle problem in a harmonic oscillator basis.These single particle wave function approximations allow us to perform variational Monte Carlo (VMC) simulations without having explicit expressions for the single particle wave functions.We use the VMC simulations to analyze the ground states of quantum dots trapped in various confinement potentials, namely single and double harmonic oscillator potentials and single finite square well potentials.A point of interest is to see how many harmonic oscillator basis functions are needed for high quality approximations to the single particle wave functions for the different potentials.This is of interest because the number of basis functions needed is an important constraint of the method when it comes to computational efficiency.To verify the method used, we compare our ground state energy results with other ab initio many-body methods, such as the Similarity Renormalization Group method, the Coupled Cluster method, and the Full Configuration Interaction method, as well as with other VMC and DMC (Diffusion Monte Carlo) results.i Preface I first discovered physics during high school, thanks to my brother Alexander Fleischer.He knew how much I liked mathematics, so he recommended a course in physics to me.I ended up liking it even more than mathematics, and as a result, after high school I applied to the physics, meteorology and astronomy Bachelor program at the University of Oslo.The very first semester I was introduced to a field I liked just as much as physics, namely programming.Unfortunately, my Bachelor did not involve much programming after the first year.During the third year I had the option of taking an introductory course in computational physics, but I had several other physics courses I wanted to take, and I prioritized these over computational physics.When it was time to apply for a Masters program, I was unsure of what to pick.I then remembered how fun all the programming had been during the first year, and decided to give the combination of physics and programming a try, so I applied to the computational physics Masters program.The first semester I took the introductory computational physics course, and knew immediately that I had picked the right Masters program for me.I would like to thank my supervisor Morten Hjorth-Jensen for all the help and motivation he gave me during the past two years.I would also like to thank my brother Alexander for introducing me to physics in the first place, and for the helpful discussions we had about this thesis.Additionally, I would like to thank Håkon V. Treider for the fun times we have had sharing an office for the better part of two years, and for the help he has given me with general programming related issues.Finally, I would like to thank the various other people I shared an office with over the past two years, and the rest of the people at the computational physics research group, for making it a great place to be. Oslo,
This thesis aims to give an understanding of quantum dots trapped in different potentials by computationally finding the ground state energies of the system.The potentials studied are the single and double harmonic oscillator wells and the finite square well.To achieve this, we have implemented a robust Variational Monte Carlo framework that allows for different single-particle wavefunctions.This allowed us to study the use of a linear expansion of the single harmonic oscillator basisfunctions as an approximation to the single-particle wavefunctions of the other potential wells.We investigated how many of these basisfunctions needed to achieve sufficiently good single-particle wavefunctions.The framework has several optimizations that drastically reduce the computational time of the Monte Carlo method.The framework has a rigorous testing regime that ensures the authenticity of the results.To verify the results, we have compared them to the results of other Variatonal Monte Carlo computations, as well as Diffusion Monte Carlo, the Coupled Cluster method, the Similarity Renormalization Group method and the Full Configuration Interaction method.In addition, we have verified the results of the finite square well by comparing with a general series solution.
Introduction In Chapters 1 to 4 we studied methods for solving the Schrödinger equation for many-electron systems. Many of the techniques described there carry over to other quantum many-particle systems, such as liquid helium, and the protons and neutrons in a nucleus. The techniques which we discussed there were, however, all of a mean-field type and therefore correlation effects could not be taken into account without introducing approximations. In this chapter, we consider more accurate techniques, which are similar to those studied in Chapter 10 and are based on using (pseudo-)random numbers – hence the name ‘Monte Carlo’ for these methods. In Chapter 10 we applied Monte Carlo techniques to classical many-particle systems; here we use these techniques for studying quantum problems involving many particles. In the next section we shall see how we can apply Monte Carlo techniques to the problem of calculating the quantum mechanical expectation value of the ground state energy. This is used in order to optimise this expectation value by adjusting a trial wave function in a variational type of approach, hence the name variational Monte Carlo (VMC). In the following section we use the similarity between the Schrödinger equation and the diffusion equation in order to calculate the properties of a collection of interacting quantum mechanical particles by simulating a classical particle diffusion process. The resulting method is called diffusion Monte Carlo (DMC).
With the ability to solve the many-body Schrödinger equation accurately, in principle all physics and chemistry could be derived from first principles. However, exact wave functions of realistic and interesting systems are in general unavailable because they are non-deterministic polynomial-hard to compute (Troyer, 2005). This implies that we need to rely on approximations. The variational Monte Carlo (VMC) method is widely used for ground state studies, but requires a trial wave function ansatz which must trade off between efficiency and accuracy. The method also has many common features with machine learning algorithms, and as neural networks have shown impressive power as function approximators, the idea is to use a neural network as the trial wave function guess. For fermionic systems, like electronic structure systems, the wave function needs to obey Fermi-Dirac statistics. This is typically achieved using a Slater determinant. As a neural network hardly can model this feature, our approach is to replace the single-particle functions in the Slater determinant with restricted Boltzmann machines, getting the RBM ansatz. In addition, we add further correlations via so-called Jastrow factors (Drummond, 2004). The de facto standard trial wave function ansatz for electronic structure calculations is the Slater-Jastrow ansatz, which was implemented as a reference. Our primary focus is on closed-shell circular quantum dots, where we compute the ground state energy and electron density of two-dimensional systems with up to 90 electrons and three-dimensional systems with up to 70 electrons. The energy obtained by the RBM ansatz was reasonably close to experimental results, and it gradually became closer as we added more complex correlation factors. For our most complicated Jastrow factor, the energy was found to be lower than the energy provided by the Slater-Jastrow ansatz for small dots, but for larger dots it was slightly higher. However, the one-body density profile reveals that the RBM ansatz gives more distinctly located electrons compared to the Slater-Jastrow ansatz. This can be explained by the way the RBM ansatz models the correlations. From the two-body density profile, we also observe that the repulsive interactions get more significant as we add a Jastrow factor. Based on the electron densities and the energy distribution between kinetic and potential energy, it is certain that the various methods provide different electron configurations. For low-frequency dots, the electron density becomes more localized with an additional radial peak compared to high-frequency dots. This is reminiscent of what is known as Wigner localization (Ghosal, 2007). The computational time consumption was found to be favorable for the RBM ansatz for small systems and the Slater-Jastrow ansatz for large systems. This can be explained by the exploding number of variational parameters in the RBM ansatz as the system sizes increase. The RBM ansatz with Jastrow factors were notably more computationally intensive than the other ansätze, and evidently, there is no reason to use the simplest Jastrow factor when more complicated Jastrow factors are available.
Quantum Monte Carlo (QMC) methods represent a powerful family of computational techniques for tackling complex quantum many-body problems and performing calculations of stationary state properties. QMC is among the most accurate and powerful approaches to the study of electronic structure, but its application is often hindered by a steep learning curve; hence it is rarely addressed in undergraduate and postgraduate classes. This tutorial is a step toward filling this gap. We offer an introduction to the diffusion Monte Carlo (DMC) method, which aims to solve the imaginary time Schrödinger equation through stochastic sampling of the configuration space. Starting from the theoretical foundations, the discussion leads naturally to the formulation of a step-by-step algorithm. To illustrate how the method works in simplified scenarios, examples such as the harmonic oscillator and the hydrogen atom are provided. The discussion extends to the fixed-node approximation, a crucial approach for addressing the fermionic sign problem in multi-electron systems. In particular, we examine the influence of trial wave function nodal surfaces on the accuracy of DMC energy by evaluating results from a non-interacting two-fermion system. Extending the method to excited states is feasible in principle, but some additional considerations are needed, supported by practical insights. By addressing the fundamental concepts from a hands-on perspective, we hope this tutorial will serve as a valuable guide for researchers and students approaching DMC for the first time.
We study systems of particles with varying numbers of distinguishable species ranging from 4-species to the N-species limit, where N is the number of particles in the system itself. We examine these systems using various powerful simulation techniques that belong to the quantum Monte Carlo (QMC) family. The N-species limit is studied via Path Integral Monte Carlo (PIMC), which is a finite temperature, non-perturbative technique. At this limit, we explore the viability of two different thermal density matrices for systems that interact via hard-sphere, hard-cavity potentials. We then study the impact of finite-size effects on calculations of the energy, pressure and specific heat of the system when reaching the thermodynamic limit under periodic boundary conditions. We then move to studying a 4-species fermionic system using Variational Monte Carlo (VMC) and Diffusion Monte Carlo (DMC), which are ground state techniques. We apply these methods to the clustering problem of 4-particle and 8-particle systems. While the 4-particle, 4-species system is relatively simple to calculate as it is physically identical to a bosonic system of 4-particles, the 8-particle fermi system is more complicated. We detail the process of simulating a bound 8-particle state with respect to decay into two independent 4-particle clusters with DMC for the first time.
The development of quantum electrodynamics (QED), which studies the light-matter interaction in quantum level, has been accompanied by the enhancement of the coupling strength g between an atom (matter) and an electromagnetic (EM) field (light).With the introduction of a cavity, forming a cavity QED system, the coupling strength reached weak coupling (WC) and strong coupling (SC) regime.These regimes are defined by the system dissipations, the atom decay rate γ and the cavity decay rate κ, where g ≪ γ, κ for the WC regime and g ≫ γ, κ for the SC regime.Especially for the SC regime, the energy exchange between the atom and the EM field can happen repeatedly before the system losses the energy.This physical property enables quantum state manipulation in First and foremost, I would like to express my sincere appreciation to my principal supervisor, Takao Aoki, for his advice and support during my PhD studies.He is the first person who welcomed me to the field of cavity quantum electrodynamics at the end my third-year as an undergraduate student.Then, when I proposed to go to NICT and study about circuit QED and USC/DSC regimes, he agreed and quickly worked on the joined research contract, which yield the contents of this thesis.I still remember his words, "the Hamiltonian is the same anyway", which he said after agreeing my proposal.I also would like to thank him to allow me to the Leading Graduate Program in Science and Engineering and Power Energy Professional WISE Program.These programs require extra work for some unique curriculums, such as Lab.Rotations, internship and studying abroad.He has been supportive and flexible all the time and gave me full room to what I needed to do.All these works could not be done without support and guidance.Next, I would like to thank the members of Macroscopic Quantum Physics Project, National Institute of Information and Communication Technologies (NICT).I greatly appreciate Kouichi Semba, the leader of the project, letting me join the team and supported my PhD studies.He recommended me to Prof. William D. Oliver at MIT which made my visiting stay at EQuS at MIT become true.I also want to thank you him for applying for the fund by CREST which greatly helped me financially for a period of working at NICT.His advices and supports were not only very helpful for the works but also enriched my life.Fumiki Yoshihara was my mentor all the time during these works.He told me how to design and analyze the circuits, prepare the samples and operate the systems for experiments.He always answered me when I face problems and help me solve them, even they are easy ones.It has to be pointed out that he gave me the idea coupling the qubit to multiple modes in the resonator.This research topic has been tough due to its complicity, yet also been fun to mess around.I also appreciate he invited me to the soccer games during the lunch breaks.The cryostat which I used to cool the circuits was mostly prepared by Tomoko Fuse.She also told me how to prepare the cryostat and manage the cables inside it.Her advices and comments on my works were always greatly helpful.As a theorist Sahel Ashhab supported my works from the basic ideas.His advices helped me a lot to understand what is happening in the systems.He also helped me analyze the multimode circuit QED system by carefully deriving the expressions.I would like to thank the of the group, Sunmi Kim, with the discussions over chip designs and preparation of the Lab.At the end, I thank Akiko Hoshi for dealing all the paper works and Takashi Nakayama for helping me with the coding and equipment preparation.Thirdly, I would like to thank Shiro Saito and Kosuke Kakuyanagi from Superconducting Quantum Circuits Research Group at NTT Basic Research Laboratories.As the co-authors, they fabricated the samples with maximum care.I also appreciate the discussions about the experimental results where they always gave helpful advices and suggestions.I would like to thank all the members for the discussions during my iii internship at the same group.I especially thank Shiro Saito and Satoru Toida, the mentor during the internship, to let me join the group and conduct experiments.It was a great experience to work with the top-level researchers around the globe.I also would like to thank my two supervisors of the two Lab.Rotations, Kazuya Yuasa and Hiroshi Kawarada, for giving me the opportunity to join them.Although they were for a short period of time, the experiences broaden my interests towards science and engineering and will be surely valuable for me.I am deeply grateful to my supervisor of Engineering Quantum Systems group, William D. Oliver, for making me joining with he and the other Laboratory members and hosted my stay.I also appreciate the discussions and works with Roni Winik, who patiently explained his research and brought me in with his research topic.I spent a great time discussing and chatting with the group members and enjoyed working with them.I also want to thank William D. Oliver who applied a