arXiv subjects

Thomas Luu

Publications and source records attributed to Thomas Luu.

At least 37 records · Page 2Linked to original sources

arXiv2024

Hubbard interaction at finite $T$ on a hexagonal lattice

The temporal finite volume induces significant effects in Monte Carlo simulations of systems in low dimensions, such as graphene, a 2-D hexagonal system known for its unique electronic properties and numerous potential applications. In this work, we explore the behavior of fermions on a hexagonal sheet with a Hubbard-type interaction characterized by coupling $U$. This system exhibits zero or near zero-energy excitations that are highly sensitive to finite temperature effects. We compute corrections to the self-energy and the effective mass of low-energy excitations, arriving at a quantization condition that includes the temporal finite volume. These analyses are then conducted for both zero and finite temperatures. Our findings reveal that the first-order $\mathcal{O}(U)$ contributions are absent, leading to non-trivial corrections starting at $\mathcal{O}(U^2)$. We validate our calculations against exact and numerical results obtained from Hybrid Monte Carlo simulations on small lattices.

cond-mat.str-el↗

arXiv2024

Overcoming Ergodicity Problems of the Hybrid Monte Carlo Method using Radial Updates

Despite its many advantages, the sensible application of the Hybrid Monte Carlo (HMC) method is often hindered by the presence of large - or even infinite - potential barriers. These potential barriers partition the configuration space into distinct sectors, which leads to ergodicity violations and biased measurements of observables. In this work, we address this problem by augmenting the HMC method with a multiplicative Metropolis-Hastings update in a so-called "radial direction" of the fields, which enables jumps over the aforementioned potential barriers at comparably low computational cost. The effectiveness of this approach is demonstrated for the Hubbard model, formulated in a non-compact space by means of a continuous Hubbard-Stratonovich transformation. Our numerical results show that the radial updates successfully resolve the ergodicity violation, while simultaneously reducing autocorrelations.

cond-mat.str-el↗

arXiv2024

Reducing the Sign Problem with simple Contour Deformation

We apply constant imaginary offsets to the path integral for a reduction of the sign problem in the Hubbard model. These simple transformations enhance the quality of results from HMC calculations without compromising the speed of the algorithm. This method enables us to efficiently calculate systems that are otherwise inaccessible due to a severe sign problem. To support this claim, we present observables of the C20 and C60 fullerenes. Furthermore, we demonstrate that at a certain offset, the sign problem is completely lifted in the limit of large chemical potential.

cond-mat.str-el↗

arXiv2024

Single Particle Spectrum of Doped $\mathrm{C}_{20}\mathrm{H}_{12}$-Perylene

We present a Hamiltonian Monte Carlo study of doped perylene $\mathrm{C}_{20}\mathrm{H}_{12}$ described with the Hubbard model. Doped perylene can be used for organic light-emitting diodes (OLEDs) or as acceptor material in organic solar cells. Therefore, central to this study is a scan over charge chemical potential. A variational basis of operators allows for the extraction of the single-particle spectrum through a mostly automatic fitting procedure. Finite chemical potential simulations suffer from a sign problem which we ameliorate through contour deformation. The on-site interaction is kept at $U/κ= 2$. Discretization effects are handled through a continuum limit extrapolation. Our first-principles calculation shows significant deviation from non-interacting results especially at large chemical potentials.

cond-mat.str-el↗

arXiv2024

ILP-based Resource Optimization Realized by Quantum Annealing for Optical Wide-area Communication Networks -- A Framework for Solving Combinatorial Problems of a Real-world Application by Quantum Annealing

Resource allocation of wide-area internet networks is inherently a combinatorial optimization problem that if solved quickly, could provide near real-time adaptive control of internet-protocol traffic ensuring increased network efficacy and robustness, while minimizing energy requirements coming from power-hungry transceivers. In recent works we demonstrated how such a problem could be cast as a quadratic unconstrained binary optimization (QUBO) problem that can be embedded onto the D-Wave AdvantageTM quantum annealer system, demonstrating proof of principle. Our initial studies left open the possibility for improvement of D-Wave solutions via judicious choices of system run parameters. Here we report on our investigations for optimizing these system parameters, and how we incorporate machine learning (ML) techniques to further improve on the quality of solutions. In particular, we use the Hamming distance to investigate correlations between various system-run parameters and solution vectors. We then apply a decision tree neural network (NN) to learn these correlations, with the goal of using the neural network to provide further guesses to solution vectors. We successfully implement this NN in a simple integer linear programming (ILP) example, demonstrating how the NN can fully map out the solution space that was not captured by D-Wave. We find, however, for the 3-node network problem the NN is not able to enhance the quality of space of solutions.

quant-ph↗

arXiv2024

An Effective Theory for Graphene Nanoribbons with Junctions

Graphene nanoribbons are a promising candidate for fault-tolerant quantum electronics. In this scenario, qubits are realised by localised states that can emerge on junctions in hybrid ribbons formed by two armchair nanoribbons of different widths. We derive an effective theory based on a tight-binding ansatz for the description of hybrid nanoribbons and use it to make accurate predictions of the energy gap and nature of the localisation in various hybrid nanoribbon geometries. We use quantum Monte Carlo simulations to demonstrate that the effective theory remains applicable in the presence of Hubbard interactions. We discover, in addition to the well known localisations on junctions, which we call `Fuji', a new type of `Kilimanjaro' localisation smeared out over a segment of the hybrid ribbon. We show that Fuji localisations in hybrids of width $N$ and $N+2$ armchair nanoribbons occur around symmetric junctions if and only if $N\pmod3=1$, while edge-aligned junctions never support strong localisation. This behaviour cannot be explained relying purely on the topological $Z_2$ invariant, which has been believed the origin of the localisations to date.

cond-mat.mes-hall↗

arXiv2023

From Theory to Practice: Applying Neural Networks to Simulate Real Systems with Sign Problems

The numerical sign problem poses a seemingly insurmountable barrier to the simulation of many fascinating systems. We apply neural networks to deform the region of integration, mitigating the sign problem of systems with strongly correlated electrons. In this talk we present our latest architectural developments as applied to contour deformation. We also demonstrate its applicability to real systems, namely perylene.

cond-mat.str-el↗

arXiv2023

Testing importance sampling on a quantum annealer for strong coupling SU(3) gauge theory

$SU(N_c)$ gauge theories in the strong coupling limit can be described by integer variables representing monomers, dimers and baryon loops. We demonstrate how the D-wave quantum annealer can perform importance sampling on $U(N_c)$ gauge theory in the strong coupling formulation of this theory. In addition to causing a sign problem in importance sampling, baryon loops induce a complex QUBO matrix which cannot be optimized by the D-Wave annealer. Instead we show that simulating the sign-problem free quenched action on the D-Wave is sufficient when combined with a sign reweighting method. As the first test on $SU(3)$ gauge theory, we simulate on $2 \times 2$ lattice and compare the results with its analytic solutions.

hep-lat↗

arXiv2023

$U(N)$ gauge theory in the strong coupling limit on a quantum annealer

Lattice QCD in the strong coupling regime can be formulated in dual variables which are integer-valued. It can be efficiently simulated for modest finite temperatures and finite densities via the worm algorithm, circumventing the finite density sign problem in this regime. However, the low temperature regime is more expensive to address. As the partition function is solely expressed in terms of integers, it can be cast as a combinatorial optimization problem that can be solved on a quantum annealer. We will first explain the setup of the system we want to study, and then present its reformulation suitable for a quantum annealer, and in particular the D-Wave. As a proof of concept, we present first results obtained on D-Wave for gauge group $U(1)$ and $U(3)$, and outline the next steps towards gauge groups $SU(3)$. We find that in addition, histogram reweighting greatly improves the accuracy of our observables when compared to analytic results.

hep-lat↗

arXiv2023

Fermionic Sign Problem Minimization by Constant Path Integral Contour Shifts

The path integral formulation of quantum mechanical problems including fermions is often affected by a severe numerical sign problem. We show how such a sign problem can be alleviated by a judiciously chosen constant imaginary offset to the path integral. Such integration contour deformations introduce no additional computational cost to the Hybrid Monte Carlo algorithm, while its effective sample size is greatly increased. This makes otherwise unviable simulations efficient for a wide range of parameters. Applying our method to the Hubbard model, we find that the sign problem is significantly reduced. Furthermore, we prove that it vanishes completely for large chemical potentials, a regime where the sign problem is expected to be particularly severe without imaginary offsets. In addition to a numerical analysis of such optimized contour shifts, we analytically compute the shifts corresponding to the leading and next-to-leading order corrections to the action. We find that such simple approximations, free of significant computational cost, suffice in many cases.

cond-mat.str-el↗

arXiv2023

Tactile Network Resource Allocation enabled by Quantum Annealing based on ILP Modeling

Agile networks with fast adaptation and reconfiguration capabilities are required for on-demand provisioning of various network services. We propose a new methodical framework for short-time network optimization based on quantum computing (QC) and integer linear program (ILP) models, which has the potential of realizing a real-time network automation. We define methods to map a nearly real-world ILP model for resource provisioning to a quadratic unconstrained binary optimization (QUBO) problem, which is solvable on quantum annealer (QA). We concentrate on the three-node network to evaluate our approach and its obtainable quality of solution using the state-of-the-art quantum annealer D-Wave Advantage 5.2/5.3. By studying the annealing process, we find annealing configuration parameters that obtain feasible solutions close to the reference solution generated by the classical ILP-solver CPLEX. Further, we studied the scaling of the network problem and provide estimations on quantum annealer's hardware requirements to enable a proper QUBO problem embedding of larger networks. We achieved the QUBO embedding of networks with up to 6 nodes on the D-Wave Advantage. According to our estimates a real-sized network with 12 to 16 nodes require a QA hardware with at least 50000 qubits or more.

quant-ph↗

arXiv2022

Mitigating the Hubbard Sign Problem with Complex-Valued Neural Networks

Monte Carlo simulations away from half-filling suffer from a sign problem that can be reduced by deforming the contour of integration. Such a transformation, which induces a Jacobian determinant in the Boltzmann weight, can be implemented using neural networks. This additional determinant cost for a generic neural network scales cubically with the volume, preventing large-scale simulations. We implement a new architecture, based on complex-valued affine coupling layers, which reduces this to linear scaling. We demonstrate the efficacy of this method by successfully applying it to systems of different size, the largest of which is intractable by other Monte Carlo methods due to its severe sign problem.

physics.comp-ph↗

arXiv2022

Localization of Electronic States in Hybrid Nano-Ribbons in the Non-Perturbative Regime

We investigate the localization of low-energy single quasi-particle states in the 7/9-hybrid nanoribbon system in the presence of strong interactions and within a finite volume. We consider two scenarios, the first being the Hubbard model at half-filling and perform quantum Monte Carlo simulations for a range $U$ that includes the strongly correlated regime. In the second case we add a nearest-neighbor superconducting pairing $Δ$ and take the symmetric line limit, where $Δ$ is equal in magnitude to the hopping parameter $t$. In this limit the quasi-particle spectrum and wavefunctions can be directly solved for general onsite interaction $U$. In both cases we extract the site-dependent quasi-particle wavefunction densities and demonstrate that localization persists in these non-perturbative regimes under particular scenarios.

cond-mat.str-el↗

arXiv2022

Two particles interacting via a contact interaction on $S^2$

We consider two particles interacting via a contact interaction that are constrained to a sphere, or $S^2$. We determine their spectrum to arbitrary precision and for arbitrary angular momentum. We show how the non-inertial frame leads to non-trivial solutions for different angular momenta. Our results represent an extension of the finite-volume Lüscher formulas but now to a non-trivial geometry. We apply our results to predict the spectrum of select two-nucleon halo nuclei and compare with experimental results.

math-ph↗

arXiv2022

Self-consistent formulations for stochastic nonlinear neuronal dynamics

Neural dynamics is often investigated with tools from bifurcation theory. However, many neuron models are stochastic, mimicking fluctuations in the input from unknown parts of the brain or the spiking nature of signals. Noise changes the dynamics with respect to the deterministic model; in particular bifurcation theory cannot be applied. We formulate stochastic neuronal dynamics in the Martin-Siggia-Rose de Dominicis-Janssen (MSRDJ) formalism and present the fluctuation expansion of the effective action and the functional renormalization group (fRG) as two systematic ways to incorporate corrections to the mean dynamics and time-dependent statistics due to fluctuations in the presence of nonlinear neuronal gain. To formulate self-consistency equations, we derive a fundamental link between the effective action in the Onsager-Machlup(OM) formalism, which allows the study of phase transitions, and the MSRDJ effective action, which is computationally advantageous. These results in particular allow the derivation of an OM effective action for systems with non-Gaussian noise. This approach naturally leads to effective deterministic equations for the first moment of the stochastic system; they explain how nonlinearities and noise cooperate to produce memory effects. Moreover, the MSRDJ formulation yields an effective linear system that has identical power spectra and linear response. Starting from the better known loopwise approximation, we then discuss the use of the fRG as a method to obtain self-consistency beyond the mean. We present a new efficient truncation scheme for the hierarchy of flow equations for the vertex functions by adapting the Blaizot, Méndez and Wschebor approximation from the derivative expansion to the vertex expansion. The methods are presented by means of the simplest possible example of a stochastic differential equation that has generic features of neuronal dynamics.

cond-mat.stat-mech↗

arXiv2021

The Hubbard model with fermionic tensor networks

Many electromagnetic properties of graphene can be described by the Hubbard model on a honeycomb lattice. However, this system suffers strongly from the sign problem if a chemical potential is included. Tensor network methods are not affected by this problem. We use the imaginary time evolution of a fermionic projected entangled pair state, which allows to simulate both parity sectors independently. Incorporating the fermionic nature on the level of the tensor network allows to fix the particle number to be either even or odd. This way we can access the states at half filling and with one additional electron. We calculate the energy and other observables of both states, which was not possible before with Monte Carlo methods.

physics.comp-ph↗

arXiv2021

The Antiferromagnetic Character of the Quantum Phase Transition in the Hubbard Model on the Honeycomb Lattice

We provide a unified, comprehensive treatment of all operators that contribute to the anti-ferromagnetic, ferromagnetic, and charge-density-wave structure factors and order parameters of the hexagonal Hubbard Model. We use the Hybrid Monte Carlo algorithm to perform a systematic, carefully controlled analysis in the temporal Trotter error and of the thermodynamic limit. We expect our findings to improve the consistency of Monte Carlo determinations of critical exponents. We perform a data collapse analysis and determine the critical exponent $β=0.898(37)$ for the semimetal-Mott insulator transition in the hexagonal Hubbard Model. Our methods are applicable to a wide range of lattice theories of strongly correlated electrons.

cond-mat.str-el↗

arXiv2021

The Semimetal-Antiferromagnetic Mott Insulator Quantum Phase Transition of the Hubbard Model on the Honeycomb Lattice

The Hubbard model on the honeycomb lattice undergoes a quantum phase transition from a semimetallic to a Mott insulating phase and from a disordered to an anti-ferromagnetically phase. We show that these transitions occur simultaneously and we calculate the critical coupling $U_c=3.835(14)$ as well as the critical exponents $ν=1.181(43)$ and $β=0.898(37)$ which are expected to fall into the $SU(2)$ Gross-Neveu universality class. For this we employ Hybrid Monte Carlo simulations, extrapolate the single particle gap and the spin structure factors to the thermodynamic and continuous time limits, and perform a data collapse fit. We also determine the zero temperature values of single particle gap and staggered magnetisation on both sides of the phase transition.

cond-mat.str-el↗