21 research outputs found
Vacancies in graphene: an application of adiabatic quantum optimization
Quantum annealers have grown in complexity to the point that quantum
computations involving few thousands of qubits are now possible. In this paper,
\textcolor{black}{with the intentions to show the feasibility of quantum
annealing to tackle problems of physical relevance, we used a simple model,
compatible with the capability of current quantum annealers, to study} the
relative stability of graphene vacancy defects. By mapping the crucial
interactions that dominate carbon-vacancy interchange onto a quadratic
unconstrained binary optimization problem, our approach exploits
\textcolor{black}{the ground state as well the excited states found by} the
quantum annealer to extract all the possible arrangements of multiple defects
on the graphene sheet together with their relative formation energies. This
approach reproduces known results and provides a stepping stone towards
applications of quantum annealing to problems of physical-chemical interest
Noisy quantum walks of two indistinguishable interacting particles
We investigate the dynamics of continuous-time two-particle quantum walks on
a one-dimensional noisy lattice. Depending on the initial condition, we show
how the interplay between particle indistinguishability and interaction
determines distinct propagation regimes. A realistic model for the environment
is considered by introducing non-Gaussian noise as time-dependent fluctuations
of the tunneling amplitudes between adjacent sites. We observe that the
combined effect of particle interaction and fast noise (weak coupling with the
environment) provides a faster propagation compared to the noiseless case. This
effect can be understood in terms of the band structure of the Hubbard model,
and a detailed analysis as a function of both noise and system parameters is
presented.Comment: 9 pages, 8 figure
Investigating the Chinese Postman Problem on a Quantum Annealer
The recent availability of quantum annealers has fueled a new area of
information technology where such devices are applied to address practically
motivated and computationally difficult problems with hardware that exploits
quantum mechanical phenomena. D-Wave annealers are promising platforms to solve
these problems in the form of quadratic unconstrained binary optimization. Here
we provide a formulation of the Chinese postman problem that can be used as a
tool for probing the local connectivity of graphs and networks. We treat the
problem classically with a tabu algorithm and using a D-Wave device. We
systematically analyze computational parameters associated with the specific
hardware. Our results clarify how the interplay between the embedding due to
limited connectivity of the Chimera graph, the definition of logical qubits,
and the role of spin-reversal controls the probability of reaching the expected
solution
GPU-accelerated algorithms for many-particle continuous-time quantum walks
Many-particle continuous-time quantum walks (CTQWs) represent a resource for several tasks in quantum technology, including quantum search algorithms and universal quantum computation. In order to design and implement CTQWs in a realistic scenario, one needs effective simulation tools for Hamiltonians that take into account static noise and fluctuations in the lattice, i.e. Hamiltonians containing stochastic terms. To this aim, we suggest a parallel algorithm based on the Taylor series expansion of the evolution operator, and compare its performances with those of algorithms based on the exact diagonalization of the Hamiltonian or a 4th order Runge–Kutta integration. We prove that both Taylor-series expansion and Runge–Kutta algorithms are reliable and have a low computational cost, the Taylor-series expansion showing the additional advantage of a memory allocation not depending on the precision of calculation. Both algorithms are also highly parallelizable within the SIMT paradigm, and are thus suitable for GPGPU computing. In turn, we have benchmarked 4 NVIDIA GPUs and 3 quad-core Intel CPUs for a 2-particle system over lattices of increasing dimension, showing that the speedup provided by GPU computing, with respect to the OPENMP parallelization, lies in the range between 8x and (more than) 20x, depending on the frequency of post-processing. GPU-accelerated codes thus allow one to overcome concerns about the execution time, and make it possible simulations with many interacting particles on large lattices, with the only limit of the memory available on the device. Program summary Program Title: cuQuWa Licensing provisions: GNU General Public License, version 3 Program Files doi: http://dx.doi.org/10.17632/vjpnjgycdj.1 Programming language: CUDA C Nature of problem: Evolution of many-particle continuous-time quantum-walks on a multidimensional grid in a noisy environment. The submitted code is specialized for the simulation of 2-particle quantum-walks with periodic boundary conditions. Solution method: Taylor-series expansion of the evolution operator. The density-matrix is calculated by averaging multiple independent realizations of the system. External routines: cuBLAS, cuRAND Unusual features: Simulations are run exclusively on the graphic processing unit within the CUDA environment. An undocumented misbehavior in the random-number generation routine (cuRAND package) can corrupt the simulation of large systems, though no problems are reported for small and medium-size systems. Compiling the code with the -arch=sm_30 flag for compute capability 3.5 and above fixes this issue
GPU-accelerated algorithms for many-particle continuous-time quantum walks
Many-particle continuous-time quantum walks (CTQWs) represent a resource for several tasks in quantum technology, including quantum search algorithms and universal quantum computation. In order to design and implement CTQWs in a realistic scenario, one needs effective simulation tools for Hamiltonians that take into account static noise and fluctuations in the lattice, i.e.\ua0Hamiltonians containing stochastic terms. To this aim, we suggest a parallel algorithm based on the Taylor series expansion of the evolution operator, and compare its performances with those of algorithms based on the exact diagonalization of the Hamiltonian or a 4th order Runge\u2013Kutta integration. We prove that both Taylor-series expansion and Runge\u2013Kutta algorithms are reliable and have a low computational cost, the Taylor-series expansion showing the additional advantage of a memory allocation not depending on the precision of calculation. Both algorithms are also highly parallelizable within the SIMT paradigm, and are thus suitable for GPGPU computing. In turn, we have benchmarked 4 NVIDIA GPUs and 3 quad-core Intel CPUs for a 2-particle system over lattices of increasing dimension, showing that the speedup provided by GPU computing, with respect to the OPENMP parallelization, lies in the range between 8x and (more than) 20x, depending on the frequency of post-processing. GPU-accelerated codes thus allow one to overcome concerns about the execution time, and make it possible simulations with many interacting particles on large lattices, with the only limit of the memory available on the device. Program summary Program Title: cuQuWa Licensing provisions: GNU General Public License, version 3 Program Files doi: http://dx.doi.org/10.17632/vjpnjgycdj.1 Programming language: CUDA C Nature of problem: Evolution of many-particle continuous-time quantum-walks on a multidimensional grid in a noisy environment. The submitted code is specialized for the simulation of 2-particle quantum-walks with periodic boundary conditions. Solution method: Taylor-series expansion of the evolution operator. The density-matrix is calculated by averaging multiple independent realizations of the system. External routines: cuBLAS, cuRAND Unusual features: Simulations are run exclusively on the graphic processing unit within the CUDA environment. An undocumented misbehavior in the random-number generation routine (cuRAND package) can corrupt the simulation of large systems, though no problems are reported for small and medium-size systems. Compiling the code with the -arch=sm_30 flag for compute capability 3.5 and above fixes this issue
Lattice quantum magnetometry
We put forward the idea of lattice quantum magnetometry, i.e., quantum sensing of magnetic fields by a
charged (spinless) particle placed on a finite two-dimensional lattice. In particular, we focus on the detection
of a locally static transverse magnetic field, either homogeneous or inhomogeneous, by performing groundstate
measurements. The system turns out to be of interest as a quantum magnetometer, since it provides
non-negligible quantum Fisher information (QFI) in a large range of configurations. Moreover, the QFI shows
some relevant peaks, determined by the spectral properties of the Hamiltonian, suggesting that certain values of
the magnetic fields may be estimated better than others, depending on the value of other tunable parameters. We
also assess the performance of coarse-grained position measurement, showing that it may be employed to realize
nearly optimal estimation strategies
Quantum metrology at level anticrossing
We address parameter estimation in two-level systems exhibiting level anticrossing and prove that universally optimal strategies for parameter estimation may be designed. In fact, we find a parameter-independent measurement scheme, leading to the ultimate quantum precision, independently of the value of the parameter of interest. Optimal estimationmay be achieved also at high temperature, depending on the structure of the two-level Hamiltonian. Finally, we discuss parameter estimation based on dynamical strategies, and a number of specific applications
Quantum walks of two interacting particles on percolation graphs
We address the dynamics of two indistinguishable interacting particles moving on
a dynamical percolation graph, i.e., a graph where the edges are independent random telegraph
processes whose values jump between 0 and 1, thus mimicking percolation. The interplay
between the particle interaction strength, initial state and the percolation rate determine
different dynamical regimes for the walkers. We show that, whenever the walkers are initially
localised within the interaction range, fast noise enhances the particle spread compared to the
noiseless case
Advanced modeling of materials with PAOFLOW 2.0:New features and software design
Recent research in materials science opens exciting perspectives to design novel quantum materials and devices, but it calls for quantitative predictions of properties which are not accessible in standard first principles packages. PAOFLOW, is a software tool that constructs tight-binding Hamiltonians from self consistent electronic wavefunctions by projecting onto a set of atomic orbitals. The electronic structure provides numerous materials properties that otherwise would have to be calculated via phenomenological models. In this paper, we describe recent re-design of the code as well as the new features and improvements in performance. In particular, we have implemented symmetry operations for unfolding equivalent k-points, which drastically reduces the runtime requirements of first principles calculations, and we have provided internal routines of projections onto atomic orbitals enabling generation of real space atomic orbitals. Moreover, we have included models for non-constant relaxation time in electronic transport calculations, doubling the real space dimensions of the Hamiltonian as well as the construction of Hamiltonians directly from analytical models. Importantly, PAOFLOW has been now converted into a Python package, and is streamlined for use directly within other Python codes. The new object oriented design treats PAOFLOW's computational routines as class methods, providing an API for explicit control of each calculation.</p