arXiv subjects

Synge Todo

Publications and source records attributed to Synge Todo.

At least 37 records · Page 2Linked to original sources

arXiv2024

Systematic construction of multi-controlled Pauli gate decompositions with optimal $T$-count

Multi-controlled Pauli gates are typical high-level qubit operations that appear in the quantum circuits of various quantum algorithms. We find multi-controlled Pauli gate decompositions with smaller CNOT-count or $T$-depth while keeping the currently known minimum $T$-count. For example, for the CCCZ gate, we find decompositions with CNOT-count 7 or $T$-depth 2 while keeping the $T$-count at the currently known minimum of 6. The discovery of these efficient decompositions improves the computational efficiency of many quantum algorithms. What led to this discovery is the systematic procedure for constructing multi-controlled Pauli gate decompositions. This procedure not only deepens our theoretical understanding of quantum gate decomposition but also leads to more efficient decompositions that have yet to be discovered.

quant-ph↗

arXiv2023

Update of $\mathcal{H}Φ$: Newly added functions and methods in versions 2 and 3

$\mathcal{H}Φ$ [$aitch$-$phi$] is an open-source software package of numerically exact and stochastic calculations for a wide range of quantum many-body systems. In this paper, we present the newly added functions and the implemented methods in vers. 2 and 3. In ver. 2, we implement spectrum calculations by the shifted Krylov method, and low-energy excited state calculations by the locally optimal blocking preconditioned conjugate gradient (LOBPCG) method. In ver. 3, we implement the full diagonalization method using ScaLAPACK and GPGPU computing via MAGMA. We also implement a real-time evolution method and the canonical thermal pure quantum (cTPQ) state method for finite-temperature calculations. The Wannier90 format for specifying the Hamiltonians is also implemented. Using the Wannier90 format, it is possible to perform the calculations for the $ab$ $initio$ low-energy effective Hamiltonians of solids obtained by the open-source software RESPACK. We also update Standard mode $\unicode{x2014}$simplified input format in $\mathcal{H}Φ$$\unicode{x2014}$ to use these functions and methods. We explain the basics of the implemented methods and how to use them.

cond-mat.str-el↗

arXiv2022

A Noise-Robust Data Assimilation Method for Crystal Structure Prediction Using Powder Diffraction Intensity

Crystal structure prediction for a given chemical composition has long been a challenge in condensed-matter science. We have recently shown that experimental powder X-ray diffraction (XRD) data are helpful in a crystal structure search using simulated annealing, even when they are insufficient for structure determination by themselves (N. Tsujimoto et al., Phys. Rev. Materials 2, 053801 (2018)). In the method, the XRD data are assimilated into the simulation by adding a penalty function to the physical potential energy, where we used a crystallinity-type penalty function defined by the difference between experimental and simulated diffraction angles. To improve the success rate and noise robustness, we introduce a correlation-coefficient-type penalty function adaptable to XRD data with significant experimental noise. We apply the new penalty function to SiO$_2$ coesite and $ε$-Zn(OH)$_2$ to determine its effectiveness in the data assimilation method.

cond-mat.mtrl-sci↗

arXiv2022

MateriApps LIVE! and MateriApps Installer: Environment for starting and scaling up materials science simulations

In our current era, numerical simulations have become indispensable theoretical and experimental tools for use in daily research activities, particularly in the materials science fields. However, the installation processes for such simulations frequently become problematic because they depend strongly on the device environment, and troubleshooting those processes is a challenging task for beginners. To minimize such difficulties, we created MateriApps LIVE! and MateriApps Installer, which can solve most of the related issues. Specifically, MateriApps LIVE! offers a virtual environment in which users can quickly try out computational materials science simulations on a personal computer while MateriApps Installer provides a comprehensive set of shell scripts for use when installing software on Unix, Linux, macOS, and supercomputer systems. Herein, we provide detailed descriptions of MateriApps LIVE! and MateriApps Installer together with illustrative examples of their use.

physics.comp-ph↗

arXiv2022

Optimized Implementation for Calculation and Fast-Update of Pfaffians Installed to the Open-Source Fermionic Variational Solver mVMC

In this article, we present a high performance, portable and well templated implementation for computing and fast-updating Pfaffian and inverse of an even-ranked skew-symmetric (antisymmetric) matrix. It is achieved with a skew-symmetric, blocked variant of the Parlett-Reid algorithm and a blocked update scheme based on the Woodbury matrix identity. Installation of this framework into the geminal-wavefunction-based many-variable Variational Monte Carlo (mVMC) code boosts sampling performance to up to more than $6$ times without changing Markov chain's behavior. The implementation is based on an extension of the BLAS-like instantiation software (BLIS) framework which has optimized kernel for many state-of-the-art processors including Intel Skylake-X, AMD EPYC Rome and Fujitsu A64FX.

physics.comp-ph↗

arXiv2022

Nested Iterative Shift-invert Diagonalization for Many-body Localization in the Random-field Heisenberg Chain

We study the many-body localization of the random-field Heisenberg chain using the nested shift-invert Lanczos method with an iterative linear solver. We use the minimum residual method (MINRES) inside each Lanczos iteration. The memory consumption of the proposed method is only proportional to the dimension of the Hilbert space. We also introduce a preconditioner that takes into account the effects of disorder and interaction in the random-field Heisenberg chain. As a probe of many-body localization transition, we propose a unitary operator called the twist operator, which has a clear interpretation in the real space. We discuss its behavior for thermal and localized eigenstates. We demonstrate the efficiency of the nested iterative shift-invert diagonalization method with the proposed preconditioner for the many-body localization problem and estimate the transition point of the random-field Heisenberg chain more precisely based on the finite-size analysis of the expectation value of the twist operator.

cond-mat.dis-nn↗

arXiv2021

Quantum-gate decomposer

Efficient decompositions of multi-qubit gates are essential in NISQ applications, where the number of gates or the circuit depth is limited. This paper presents efficient decompositions of CCZ and CCCZ gates, typical multi-qubit gates, under several qubit connectivities. We can construct the CCZ gate with only four CZ-depth when the qubit is square-shaped, including one auxiliary qubit. In T-shaped qubit connectivity, which has no closed loop, we can decompose the CCCZ gate with 17 CZ gates. While previous studies have shown a CCCZ gate decomposition with 14 CZ gates for the fully connected case, we found only four connections are sufficient for 14 CZ gates' implementation. The search for constraint-sufficient decompositions is aided by an optimization method we devised to bring the parameterized quantum circuit closer to the target quantum gate. We can apply this scheme to decompose any quantum gates, not only CCZ and CCCZ. Such decompositions of multi-qubit gates, together with the newly found CCZ and CCCZ decompositions, shorten the execution time of quantum circuits and improve the accuracy of complex quantum algorithms on near future QPUs.

quant-ph↗

arXiv2021

Randomized-gauge test for machine learning of Ising model order parameter

Recently, machine learning has been applied successfully for identifying phases and phase transitions of the Ising models. The continuous phase transition is characterized by spontaneous symmetry breaking, which can not be detected in general from a single spin configuration. To investigate if neural networks can extract correlations among spin snapshots, we propose a new test using the random-gauge Ising model. We show that neural networks can extract the order parameter or the energy of the random-gauge model as in the ferromagnetic case. We also discuss how and where the information of random gauge is encoded in neural networks and attempt to reconstruct the gauge from the neural network parameters. We find that the fully connected network encodes the effect of random gauge to its weights naturally. In contrast, the convolutional network copes with the randomness by assigning different network parts to local gauge patterns. This observation indicates that although the latter demonstrates higher performance than the former for the present randomized-gauge test, the former is more effective and suitable for dealing with models with spatial randomness.

cond-mat.dis-nn↗

arXiv2021

Neural Network Approach to Construction of Classical Integrable Systems

Integrable systems have provided various insights into physical phenomena and mathematics. The way of constructing many-body integrable systems is limited to few ansatzes for the Lax pair, except for highly inventive findings of conserved quantities. Machine learning techniques have recently been applied to broad physics fields and proven powerful for building non-trivial transformations and potential functions. We here propose a machine learning approach to a systematic construction of classical integrable systems. Given the Hamiltonian or samples in latent space, our neural network simultaneously learns the corresponding natural Hamiltonian in real space and the canonical transformation between the latent space and the real space variables. We also propose a loss function for building integrable systems and demonstrate successful unsupervised learning for the Toda lattice. Our approach enables exploring new integrable systems without any prior knowledge about the canonical transformation or any ansatz for the Lax pair.

nlin.SI↗

arXiv2021

Fast-update in self-learning algorithm for continuous-time quantum Monte Carlo

We propose a novel technique for speeding up the self-learning Monte Carlo method applied to the single-site impurity model. For the case where the effective Hamiltonian is expressed by polynomial functions of differences of imaginary-time coordinate between vertices, we can remove the dependence of CPU time on the number of vertices, $n$, by saving and updating some coefficients for each insertion and deletion process. As a result, the total cost for a single-step update is drastically reduced from $O(nm)$ to $O(m^2)$ with $m$ being the order of polynomials in the effective Hamiltonian. Even for the existing algorithms, in which the absolute value is used instead of the difference as the variable of polynomial functions, we can limit the CPU time for a single step of Monte Carlo update to $O(m^2 + m \log n)$ with the help of balanced binary search trees. We demonstrate that our proposed algorithm with only logarithmic $n$-dependence achieves an exponential speedup from the existing methods, which suffer from severe performance issues at low temperatures.

cond-mat.str-el↗

arXiv2020

Bond-weighted Tensor Renormalization Group

We propose an improved tensor renormalization group (TRG) algorithm, the bond-weighted TRG (BTRG). In BTRG, we generalize the conventional TRG by introducing bond weights on the edges of the tensor network. We show that BTRG outperforms the conventional TRG and the higher-order tensor renormalization group with the same bond dimension, while its computation time is almost the same as that of TRG. Furthermore, BTRG can have non-trivial fixed-point tensors at an optimal hyperparameter. We demonstrate that the singular value spectrum obtained by BTRG is invariant under the renormalization procedure in the case of the two-dimensional Ising model at the critical point. This property indicates that BTRG performs the tensor contraction with high accuracy while keeping the scale-invariant structure of tensors.

cond-mat.stat-mech↗

arXiv2020

K$ω$ -- Open-source library for the shifted Krylov subspace method of the form $(zI-H)x=b$

We develop K$ω$, an open-source linear algebra library for the shifted Krylov subspace methods. The methods solve a set of shifted linear equations $(z_k I-H)x^{(k)}=b\, (k=0,1,2,...)$ for a given matrix $H$ and a vector $b$, simultaneously. The leading order of the operational cost is the same as that for a single equation. The shift invariance of the Krylov subspace is the mathematical foundation of the shifted Krylov subspace methods. Applications in materials science are presented to demonstrate the advantages of the algorithm over the standard Krylov subspace methods such as the Lanczos method. We introduce benchmark calculations of (i) an excited (optical) spectrum and (ii) intermediate eigenvalues by the contour integral on the complex plane. In combination with the quantum lattice solver $\mathcal{H} Φ$, K$ω$ can realize parallel computation of excitation spectra and intermediate eigenvalues for various quantum lattice models.

math.NA↗

arXiv2020

Multithreaded event-chain Monte Carlo with local times

We present a multithreaded event-chain Monte Carlo algorithm (ECMC) for hard spheres. Threads synchronize at infrequent breakpoints and otherwise scan for local horizon violations. Using a mapping onto absorbing Markov chains, we rigorously prove the correctness of a sequential-consistency implementation for small test suites. On x86 and ARM processors, a C++ (OpenMP) implementation that uses compare-and-swap primitives for data access achieves considerable speed-up with respect to single-threaded code. The generalized birthday problem suggests that for the number of threads scaling as the square root of the number of spheres, the horizon-violation probability remains small for a fixed simulation time. We provide C++ and Python open-source code that reproduces all our results.

physics.comp-ph↗

arXiv2019

Dynamical phase transition in Floquet optical bistable systems: An approach from finite-size quantum systems

We study a dynamical phase transition in optical bistable systems subject to a time-periodic driving field. The phase transition occurs in the structure of limit cycle as a function of the frequency of the driving field. In the thermodynamic limit, a single limit cycle is divided into two separated limit cycles at the transition point. In finite-size systems, however, there is always a single limit cycle due to the quantum tunneling effect. We use a Floquet dissipative map, which is a time-evolution operator over one period in a dynamics given by a quantum master equation, and discuss the decay rate of relaxation dynamics into the limit cycle based on the dominant eigenvalue of the map. We found that the decay rate exhibits qualitatively different system-size dependence before and after the phase transition, and it shows a finite-size scaling of spinodal phenomena around the transition point. The present work provides a systematic way of studying dynamical phase transition observed in time-periodically driven open systems in terms of the Floquet dissipative map.

cond-mat.stat-mech↗

arXiv2019

Anisotropic Tensor Renormalization Group

We propose a new tensor renormalization group algorithm, Anisotropic Tensor Renormalization Group (ATRG), for lattice models in arbitrary dimensions. The proposed method shares the same versatility with the Higher-Order Tensor Renormalization Group (HOTRG) algorithm, i.e., it preserves the lattice topology after the renormalization. In comparison with HOTRG, both of the computation cost and the memory footprint of our method are drastically reduced, especially in higher dimensions, by renormalizing tensors in an anisotropic way after the singular value decomposition. We demonstrate the ability of ATRG for the square lattice and the simple cubic lattice Ising models. Although the accuracy of the present method degrades when compared with HOTRG of the same bond dimension, the accuracy with fixed computation time is improved greatly due to the drastic reduction of the computation cost.

cond-mat.stat-mech↗

arXiv2019

Sequential minimal optimization for quantum-classical hybrid algorithms

We propose a sequential minimal optimization method for quantum-classical hybrid algorithms, which converges faster, is robust against statistical error, and is hyperparameter-free. Specifically, the optimization problem of the parameterized quantum circuits is divided into solvable subproblems by considering only a subset of the parameters. In fact, if we choose a single parameter, the cost function becomes a simple sine curve with period $2π$, and hence we can exactly minimize with respect to the chosen parameter. Furthermore, even in general cases, the cost function is given by a simple sum of trigonometric functions with certain periods and hence can be minimized by using a classical computer. By repeatedly performing this procedure, we can optimize the parameterized quantum circuits so that the cost function becomes as small as possible. We perform numerical simulations and compare the proposed method with existing gradient-free and gradient-based optimization algorithms. We find that the proposed method substantially outperforms the existing optimization algorithms and converges to a solution almost independent of the initial choice of the parameters. This accelerates almost all quantum-classical hybrid algorithms readily and would be a key tool for harnessing near-term quantum devices.

quant-ph↗

arXiv2018

Localized Mode and Nonergodicity of a Harmonic Oscillator Chain

We present a simple and microscopic physical model that breaks the ergodicity. Our model consists of coupled classical harmonic oscillators, and the motion of the tagged particle obeys the generalized Langevin equation satisfying the second fluctuation dissipation theorem. It is found that although the nonergodicity strength, which is expected to detect the ergodicity breaking, for this model vanishes, the velocity auto correlation function of the tagged particle asymptotically oscillates. We analyze the model by using the molecular dynamics and the exact diagonalization as well as the rigorous mapping to the generalized Langevin equation. Our analysis reveals that the asymptotic oscillation is caused by a localized mode with an isolated frequency from the continuous phonon spectrum.

cond-mat.stat-mech↗

arXiv2018

Fast algorithm for generating random bit strings and multispin coding for directed percolation

We present efficient algorithms to generate a bit string in which each bit is set with arbitrary probability. By adopting a hybrid algorithm, i.e., a finite-bit density approximation with correction techniques, we achieve 3.8 times faster random bit generation than the simple algorithm for the 32-bit case and 6.8 times faster for the 64-bit case. Employing the developed algorithm, we apply the multispin coding technique to one-dimensional bond-directed percolation. The simulations are accelerated by up to a factor of 14 compared with an optimized scalar implementation. The random bit string generation algorithm proposed here is applicable to general Monte Carlo methods.

cond-mat.stat-mech↗