arXiv subjects

Zhendong Li

Publications and source records attributed to Zhendong Li.

At least 109 records · Page 6Linked to original sources

arXiv2021

Expressibility of comb tensor network states (CTNS) for the P-cluster and the FeMo-cofactor of nitrogenase

Polynuclear transition metal complexes such as the P-cluster and the FeMo-cofactor of nitrogenase with eight transition metal centers represent a great challenge for current electronic structure methods. In this work, we initiated the use of comb tensor network states (CTNS), whose underlying topology has a one-dimensional backbone and several one-dimensional branches, as a many-body wavefunction ansatz to tackle these challenging systems. As an important first step, we explored the expressive power of CTNS with different underlying topologies. To this end, we presented an algorithm to express a configuration interaction (CI) wavefunction into CTNS based on the Schmidt decomposition. The algorithm was illustrated for representing approximate CI wavefunctions obtained from selected CI calculations for the P-cluster and the FeMo-cofactor into CTNS with three chemically meaningful comb structures, which successively group orbitals belonging to the same atom into branches. The conventional matrix product states (MPS) representation was obtained as a special case. We also discussed the insights gained from such decompositions, which shed some light on the future developments of efficient numerical tools for polynuclear transition metal complexes.

physics.chem-ph↗

arXiv2021

Joint Beamforming Design and Power Splitting Optimization in IRS-Assisted SWIPT NOMA Networks

This paper proposes a novel network framework of intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) non-orthogonal multiple access (NOMA) networks, where IRS is used to enhance the NOMA performance and the wireless power transfer (WPT) efficiency of SWIPT. We formulate a problem of minimizing base station (BS) transmit power by jointly optimizing successive interference cancellation (SIC) decoding order, BS transmit beamforming vector, power splitting (PS) ratio and IRS phase shift while taking into account the quality-of-service (QoS) requirement and energy harvested threshold of each user. The formulated problem is non-convex optimization problem, which is difficult to solve it directly. Hence, a two-stage algorithm is proposed to solve the above-mentioned problem by applying semidefinite relaxation (SDR), Gaussian randomization and successive convex approximation (SCA). Specifically, after determining SIC decoding order by designing IRS phase shift in the first stage, we alternately optimize BS transmit beamforming vector, PS ratio, and IRS phase shift to minimize the BS transmit power. Numerical results validate the effectiveness of our proposed optimization algorithm in reducing BS transmit power compared to other baseline algorithms. Meanwhile, compared with non-IRS-assisted network, the IRS-assisted SWIPT NOMA network can decrease BS transmit power by 51.13\%.

cs.IT↗

arXiv2020

Quantum computation of molecular response properties

Accurately predicting response properties of molecules such as the dynamic polarizability and hyperpolarizability using quantum mechanics has been a long-standing challenge with widespread applications in material and drug design. Classical simulation techniques in quantum chemistry are hampered by the exponential growth of the many-electron Hilbert space as the system size increases. In this work, we propose an algorithm for computing linear and nonlinear molecular response properties on quantum computers, by first reformulating the target property into a symmetric expression more suitable for quantum computation via introducing a set of auxiliary quantum states, and then determining these auxiliary states via solving the corresponding linear systems of equations on quantum computers. On one hand, we prove that using the quantum linear system algorithm [Harrow et al., Phys. Rev. Lett. 103, 150502 (2009)] as a subroutine the proposed algorithm scales only polynomially in the system size instead of the dimension of the exponentially large Hilbert space, and hence achieves an exponential speedup over existing classical algorithms. On the other hand, we introduce a variational hybrid quantum-classical variant of the proposed algorithm, which is more practical for near-term quantum devices.

physics.chem-ph↗

arXiv2020

Recent developments in the PySCF program package

PYSCF is a Python-based general-purpose electronic structure platform that both supports first-principles simulations of molecules and solids, as well as accelerates the development of new methodology and complex computational workflows. The present paper explains the design and philosophy behind PYSCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PYSCF as a development environment. We then summarize the capabilities of PYSCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PYSCF across the domains of quantum chemistry, materials science, machine learning and quantum information science.

physics.chem-ph↗

arXiv2019

Stochastic many-body perturbation theory for electron correlation energies

Treating electron correlation more accurately and efficiently is at the heart of the development of electronic structure methods. In the present work, we explore the use of stochastic approaches to evaluate high-order electron correlation energies, whose conventional computational scaling is unpleasantly steep, being $O(N^{n+3})$ with respect to the system size $N$ and the perturbation order $n$ for the Møller-Plesset (MP) series. To this end, starting from Goldstone's time-dependent formulation of \emph{ab initio} many-body perturbation theory (MBPT), we present a reformulation of MBPT, which naturally leads to an Monte Carlo scheme with $O(nN^2+n^2N+f(n))$ scaling at each step, where $f(n)$ is a function of $n$ depending on the specific numerical scheme. Proof-of-concept calculations demonstrate that the proposed quantum Monte Carlo algorithm successfully extends the previous Monte Carlo approaches for MP2 and MP3 to higher orders. Therefore, for the first time the Goldstone's time-dependent formulation is made useful numerically for electron correlation energies, not only being purely as a theoretical tool.

physics.chem-ph↗

arXiv2019

Stimulated X-ray Raman and Absorption Spectroscopy of Iron-Sulfur Dimers

Iron-sulfur complexes play an important role in biological processes such as metabolic electron transport. A detailed understanding of the mechanism of long range electron transfer requires knowledge of the electronic structure of the complexes, which has traditionally been challenging to obtain, either by theory or by experiment, but the situation has begun to change with advances in quantum chemical methods and intense free electron laser light sources. We compute the signals from stimulated X-ray Raman spectroscopy (SXRS) and absorption spectroscopy of homovalent and mixed-valence [2Fe-2S] complexes, using the {\it ab initio} density matrix renormalization group (DMRG) algorithm. The simulated spectra show clear signatures of the theoretically predicted dense low-lying excited states within the d-d manifold. Furthermore, the difference in signal intensity between the absorption-active and Raman-active states provides a potential mechanism to selectively excite states by a proper tuning of the excitation pump, to access the electronic dynamics within this manifold.

physics.chem-ph↗

arXiv2019

On the generalization of the exponential basis for tensor network representations of long-range interactions in two and three dimensions

In one dimension (1D), a general decaying long-range interaction can be fit to a sum of exponential interactions $e^{-λr_{ij}}$ with varying exponents $λ$, each of which can be represented by a simple matrix product operator (MPO) with bond dimension $D=3$. Using this technique, efficient and accurate simulations of 1D quantum systems with long-range interactions can be performed using matrix product states (MPS). However, the extension of this construction to higher dimensions is not obvious. We report how to generalize the exponential basis to 2D and 3D by defining the basis functions as the Green's functions of the discretized Helmholtz equation for different Helmholtz parameters $λ$, a construction which is valid for lattices of any spatial dimension. Compact tensor network representations can then be found for the discretized Green's functions, by expressing them as correlation functions of auxiliary fermionic fields with nearest neighbor interactions via Grassmann Gaussian integration. Interestingly, this analytic construction in 3D yields a $D=4$ tensor network representation of correlation functions which (asymptotically) decay as the inverse distance ($r^{-1}_{ij}$), thus generating the (screened) Coulomb potential on a cubic lattice. These techniques will be useful in tensor network simulations of realistic materials.

cond-mat.str-el↗

arXiv2019

Electronic landscape of the P-cluster of nitrogenase as revealed through many-electron quantum wavefunctions

The electronic structure of the nitrogenase metal cofactors is central to nitrogen fixation. However, the P-cluster and iron molybdenum cofactor, each containing eight irons, have resisted detailed characterization of their electronic properties. Through exhaustive many-electron wavefunction simulations enabled by new theoretical methods, we report on the low-energy electronic states of the P-cluster in three oxidation states. The energy scales of orbital and spin excitations overlap, yielding a dense spectrum with features we trace to the underlying atomic states and recouplings. The clusters exist in superpositions of spin configurations with non-classical spin correlations, complicating interpretation of magnetic spectroscopies, while the charges are mostly localized from reorganization of the cluster and its surroundings. Upon oxidation, the opening of the P-cluster significantly increases the density of states, which is intriguing given its proposed role in electron transfer. These results demonstrate that many-electron simulations stand to provide new insights into the electronic structure of the nitrogenase cofactors.

physics.chem-ph↗

arXiv2018

Classifications and canonical forms of tensor product expressions in the presence of permutation symmetries

Complicated mathematical equations involving products of tensors with permutation symmetries, frequently encountered in fields such as general relativity and quantum chemistry (e.g., equations in high-order coupled cluster theories), require computer-based automatic derivations and manipulations. In these processes, a key step is the collection of tensor product terms that can be found identical by utilizing permutation symmetries of tensors or relabeling dummy indices, which is usually achieved by defining a canonical form for tensor product expressions. However, the problem of finding a canonical form is nontrivial, and can be potentially of exponential cost in the number of indices. In this work, we provided a general solution to this tensor canonicalization problem.

physics.chem-ph↗

arXiv2018

Efficient representation of long-range interactions in tensor network algorithms

We describe a practical and efficient approach to represent physically realistic long-range interactions in two-dimensional tensor network algorithms via projected entangled-pair operators (PEPOs). We express the long-range interaction as a linear combination of correlation functions of an auxiliary system with only nearest-neighbor interactions. To obtain a smooth and radially isotropic interaction across all length scales, we map the physical lattice to an auxiliary lattice of expanded size. Our construction yields a long-range PEPO as a sum of ancillary PEPOs, each of small, constant bond dimension. This representation enables efficient numerical simulations with long-range interactions using projected entangled pair states.

cond-mat.str-el↗

arXiv2018

The electronic complexity of the ground-state of the FeMo cofactor of nitrogenase as relevant to quantum simulations

We report that a recent active space model of the nitrogenase FeMo cofactor, proposed in the context of quantum simulations, is not representative of the electronic structure of the FeMo cofactor ground-state. Although quantum resource estimates, outside of the cost of adiabatic state preparation, will not be much affected, conclusions should not be drawn from the complexity of classical simulations of the electronic structure of this system in this active space. We provide a different model active space for the FeMo cofactor that contains the basic open-shell qualitative character, which may be useful as a benchmark system for making classical and quantum resource estimates.

physics.chem-ph↗

arXiv2018

Low rank representations for quantum simulation of electronic structure

The quantum simulation of quantum chemistry is a promising application of quantum computers. However, for N molecular orbitals, the $\mathcal{O}(N^4)$ gate complexity of performing Hamiltonian and unitary Coupled Cluster Trotter steps makes simulation based on such primitives challenging. We substantially reduce the gate complexity of such primitives through a two-step low-rank factorization of the Hamiltonian and cluster operator, accompanied by truncation of small terms. Using truncations that incur errors below chemical accuracy, we are able to perform Trotter steps of the arbitrary basis electronic structure Hamiltonian with $\mathcal{O}(N^3)$ gate complexity in small simulations, which reduces to $\mathcal{O}(N^2 \log N)$ gate complexity in the asymptotic regime, while our unitary Coupled Cluster Trotter step has $\mathcal{O}(N^3)$ gate complexity as a function of increasing basis size for a given molecule. In the case of the Hamiltonian Trotter step, these circuits have $\mathcal{O}(N^2)$ depth on a linearly connected array, an improvement over the $\mathcal{O}(N^3)$ scaling assuming no truncation. As a practical example, we show that a chemically accurate Hamiltonian Trotter step for a 50 qubit molecular simulation can be carried out in the molecular orbital basis with as few as 4,000 layers of parallel nearest-neighbor two-qubit gates, consisting of fewer than 100,000 non-Clifford rotations. We also apply our algorithm to iron-sulfur clusters relevant for elucidating the mode of action of metalloenzymes.

physics.comp-ph↗

arXiv2018

An efficient stochastic algorithm for the perturbative density matrix renormalization group in large active spaces

We present an efficient stochastic algorithm for the recently introduced perturbative density matrix renormalization group (p-DMRG) method for large active spaces. The stochastic implementation bypasses the computational bottleneck involved in solving the first order equation in the earlier deterministic algorithm. We demonstrate the efficiency and accuracy of the algorithm on the C$_2$ and Cr$_2$ molecular benchmark systems.

physics.chem-ph↗

arXiv2018

A Perturbative Density Matrix Renormalization Group Algorithm for Large Active Spaces

We describe a low cost alternative to the standard variational DMRG (density matrix renormalization group) algorithm that is analogous to the combination of selected configuration interaction plus perturbation theory (SCI+PT). We denote the resulting method p-DMRG (perturbative DMRG) to distinguish it from the standard variational DMRG. p-DMRG is expected to be useful for systems with very large active spaces, for which variational DMRG becomes too expensive. Similar to SCI+PT, in p-DMRG a zeroth-order wavefunction is first obtained by a standard DMRG calculation, but with a small bond dimension. Then, the residual correlation is recovered by a second-order perturbative treatment. We discuss the choice of partitioning for the perturbation theory, which is crucial for its accuracy and robustness. To circumvent the problem of a large bond dimension in the first-order wavefunction, we use a sum of matrix product states (MPS) to expand the first-order wavefunction, yielding substantial savings in computational cost and memory. We also propose extrapolation schemes to reduce the errors in the zeroth- and first-order wavefunctions. Numerical results for Cr 2 with a (28e,76o) active space and 1,3-butadiene with a (22e,82o) active space reveal that p-DMRG provides ground state energies of a similar quality to variational DMRG with very large bond dimensions, but at a significantly lower computational cost. This suggests that p-DMRG will be an efficient tool for benchmark studies in the future.

physics.chem-ph↗

arXiv2017

Time-step targeting time-dependent and dynamical density matrix renormalization group algorithms with ab initio Hamiltonians

We study the dynamical density matrix renormalization group (DDMRG) and time-dependent density matrix renormalization group (td-DMRG) algorithms in the ab initio context, to compute dynamical correlation functions of correlated systems. We analyze the strengths and weaknesses of the two methods in small model problems, and propose two simple improved formulations, DDMRG$^{++}$ and td-DMRG$^{++}$, that give increased accuracy at the same bond dimension, at a nominal increase in cost. We apply DDMRG$^{++}$ to obtain the oxygen core-excitation energy in the water molecule in a quadruple-zeta quality basis, which allows us to estimate the remaining correlation error in existing coupled cluster results. Further, we use DDMRG$^{++}$ to compute the local density of states and gaps, and td-DMRG$^{++}$ to compute the complex polarization function, in linear hydrogen chains with up to 50 H atoms, to study metallicity and delocalization as a function of bond-length.

physics.chem-ph↗

arXiv2017

Spin-projected matrix product states (SP-MPS): a versatile tool for strongly correlated systems

We present a new wavefunction ansatz that combines the strengths of spin projection with the language of matrix product states (MPS) and matrix product operators (MPO) as used in the density matrix renormalization group (DMRG). Specifically, spin-projected matrix product states (SP-MPS) are constructed as $|Ψ^{(N,S,M)}_{SP-MPS}\rangle=\mathcal{P}_S|Ψ_{MPS}^{(N,M)}\rangle$, where $\mathcal{P}_S$ is the spin projector for total spin $S$ and $|Ψ_{MPS}^{(N,M)}\rangle$ is an MPS wavefunction with a given particle number $N$ and spin projection $M$. This new ansatz possesses several attractive features: (1) It provides a much simpler route to achieve spin-adaptation (i.e. to create eigenfunctions of $\hat{S}^2$) compared to explicitly incorporating the non-Abelian SU(2) symmetry into the MPS. In particular, since the underlying state in the SP-MPS uses only Abelian symmetries, one does not need the singlet embedding scheme for non-singlet states, as normally employed in spin-adapted DMRG, to achieve a single consistent variationally optimized state. (2) Due to the use of $|Ψ_{MPS}^{(N,M)}\rangle$ as its underlying state, the SP-MPS can be closely connected to broken symmetry mean-field states. This allows to straightforwardly generate the large number of broken symmetry guesses needed to explore complex electronic landscapes in magnetic systems. Further, this connection can be exploited in the future development of quantum embedding theories for open-shell systems. (3) The sum of MPOs representation for the Hamiltonian and spin projector $\mathcal{P}_S$ naturally leads to an embarrassingly parallel algorithm for computing expectation values and optimizing SP-MPS. (4) Optimizing SP-MPS belongs to the variation-after-projection (VAP) class of spin projected theories.

physics.chem-ph↗

arXiv2017

The Python-based Simulations of Chemistry Framework (PySCF)

PySCF is a general-purpose electronic structure platform designed from the ground up to emphasize code simplicity, both to aid new method development, as well as for flexibility in computational workflow. The package provides a wide range of tools to support simulations of finite size systems, extended systems with periodic boundary conditions, low dimensional periodic systems, and custom Hamiltonians, using mean-field and post-mean-field methods with standard Gaussian basis functions. To ensure easy of extensibility, PySCF uses the Python language to implement almost all its features, while computationally critical paths are implemented with heavily optimized C routines. Using this combined Python/C implementation, the package is as efficient as the best existing C or Fortran based quantum chemistry programs. In this paper we document the capabilities and design philosophy of the current version of the PySCF package.

physics.chem-ph↗

arXiv2016

Matrix Product Operators, Matrix Product States, and ab initio Density Matrix Renormalization Group algorithms

Current descriptions of the ab initio DMRG algorithm use two superficially different languages: an older language of the renormalization group and renormalized operators, and a more recent language of matrix product states and matrix product operators. The same algorithm can appear dramatically different when written in the two different vocabularies. In this work, we carefully describe the translation between the two languages in several contexts. First, we describe how to efficiently implement the ab-initio DMRG sweep using a matrix product operator based code, and the equivalence to the original renormalized operator implementation. Next we describe how to implement the general matrix product operator/matrix product state algebra within a pure renormalized operator-based DMRG code. Finally, we discuss two improvements of the ab initio DMRG sweep algorithm motivated by matrix product operator language: Hamiltonian compression, and a sum over operators representation that allows for perfect computational parallelism. The connections and correspondences described here serve to link the future developments with the past, and are important in the efficient implementation of continuing advances in ab initio DMRG and related algorithms.

physics.chem-ph↗