arXiv subjects

Yi Fan

Publications and source records attributed to Yi Fan.

46 records · Page 3Linked to original sources

arXiv2022

Q$^2$Chemistry: A quantum computation platform for quantum chemistry

Quantum computer provides new opportunities for quantum chemistry. In this article, we present a versatile, extensible, and efficient software package, named Q$^2$Chemistry, for developing quantum algorithms and quantum inspired classical algorithms in the field of quantum chemistry. In Q$^2$Chemistry, wave function and Hamiltonian can be conveniently mapped into the qubit space, then quantum circuits can be generated according to a specific quantum algorithm already implemented in the package or newly developed by the users. The generated circuits can be dispatched to either a physical quantum computer, if available, or to the internal virtual quantum computer realized by simulating quantum circuit on classical supercomputers. As demonstrated by our benchmark simulations with up to 72 qubit, Q$^2$Chemistry achieves excellent performance in simulating medium scale quantum circuits. Application of Q$^2$Chemistry to simulate molecules and periodic systems are given with performance analysis.

quant-ph↗

arXiv2022

Review of Energy Transition Policies in Singapore, London, and California

The paper contains the online supplementary materials for "Data-Driven Prediction and Evaluation on Future Impact of Energy Transition Policies in Smart Regions". We review the renewable energy development and policies in the three metropolitan cities/regions over recent decades. Depending on the geographic variations in the types and quantities of renewable energy resources and the levels of policymakers' commitment to carbon neutrality, we classify Singapore, London, and California as case studies at the primary, intermediate, and advanced stages of the renewable energy transition, respectively.

econ.GN↗

arXiv2022

Large-Scale Simulation of Quantum Computational Chemistry on a New Sunway Supercomputer

Quantum computational chemistry (QCC) is the use of quantum computers to solve problems in computational quantum chemistry. We develop a high performance variational quantum eigensolver (VQE) simulator for simulating quantum computational chemistry problems on a new Sunway supercomputer. The major innovations include: (1) a Matrix Product State (MPS) based VQE simulator to reduce the amount of memory needed and increase the simulation efficiency; (2) a combination of the Density Matrix Embedding Theory with the MPS-based VQE simulator to further extend the simulation range; (3) A three-level parallelization scheme to scale up to 20 million cores; (4) Usage of the Julia script language as the main programming language, which both makes the programming easier and enables cutting edge performance as native C or Fortran; (5) Study of real chemistry systems based on the VQE simulator, achieving nearly linearly strong and weak scaling. Our simulation demonstrates the power of VQE for large quantum chemistry systems, thus paves the way for large-scale VQE experiments on near-term quantum computers.

quant-ph↗

arXiv2022

Exploring accurate potential energy surfaces via integrating variational quantum eigensovler with machine learning

The potential energy surface (PES) is crucial for interpreting a variety of chemical reaction processes. However, predicting accurate PESs with high-level electronic structure methods is a challenging task due to the high computational cost. As an appealing application of quantum computing, we show in this work that variational quantum algorithms can be integrated with machine learning (ML) techniques as a promising scheme for exploring accurate PESs. Different from using a ML model to represent the potential energy, we encode the molecular geometry information into a deep neural network (DNN) for representing parameters of the variational quantum eigensolver (VQE), leaving the PES to the wave function ansatz. Once the DNN model is trained, the variational optimization procedure that hinders the application of the VQE to complex systems is avoided and thus the evaluation of PESs is significantly accelerated. Numerical results demonstrate that a simple DNN model is able to reproduce accurate PESs for small molecules.

physics.chem-ph↗

arXiv2021

A Quantum Algorithm to Calculate Band Structure at the EOM Level of Theory

Band structure is a cornerstone to understand electronic properties of materials. Accurate band structure calculations using a high-level quantum-chemistry theory can be computationally very expensive. It is promising to speed up such calculations with a quantum computer. In this study, we present a quantum algorithm for band structure calculation based on the equation-of-motion (EOM) theory. First, we introduce a new variational quantum eigensolver algorithm named ADAPT-C for ground-state quantum simulation of solids, where the wave function is built adaptively from a complete set of anti-Hermitian operators. Then, on top of the ADAPT-C ground state, quasiparticle energies and the band structure can be calculated using the EOM theory in a quantum-subspace-expansion (QSE) style, where the projected excitation operators guarantee that the killer condition is satisfied. As a proof of principle, such an EOM-ADAPT-C protocol is used to calculate the band structures of silicon and diamond using a quantum computer simulator.

quant-ph↗

arXiv2021

Effects of confinement on the dynamics and correlation scales in active fluids

We study the influence of solid boundaries on dynamics and structure of active fluids as the height of the container, $z$, changes. Along the varying dimension, the geometry systematically increases, therefore, the confinement ($z$) transits from "strong confinement", to "intermediate confinement" and to "weak confinement" (close to "unconfined"). In horizontal dimensions ($x,y$), the system remains "unconfined". Through tracking the tracers dispersed in the active fluids in three dimensions we observed that activity level, characterized by velocity fluctuations of flow tracers, increases as system size increases. Concomitantly, the velocity-velocity temporal correlation changes from weak correlation to strong positive correlation, indicating "memory" in active flows. We estimate the characteristic size of the flow structure by integrating the velocity-velocity spatial correlation function. The integral increases as confinement becomes weaker and saturates at approximately 400 microns as the system becomes "unconfined". This saturation indicates an intrinsic length scale which, along with the small-scale isotropy, demonstrates the multi-scale nature of this kinesin-driven bundled microtubule system.

cond-mat.soft↗

arXiv2018

Advancing Tabu and Restart in Local Search for Maximum Weight Cliques

The tabu and restart are two fundamental strategies for local search. In this paper, we improve the local search algorithms for solving the Maximum Weight Clique (MWC) problem by introducing new tabu and restart strategies. Both the tabu and restart strategies proposed are based on the notion of a local search scenario, which involves not only a candidate solution but also the tabu status and unlocking relationship. Compared to the strategy of configuration checking, our tabu mechanism discourages forming a cycle of unlocking operations. Our new restart strategy is based on the re-occurrence of a local search scenario instead of that of a candidate solution. Experimental results show that the resulting MWC solver outperforms several state-of-the-art solvers on the DIMACS, BHOSLIB, and two benchmarks from practical applications.

cs.AI↗

arXiv2015

Exploiting Reduction Rules and Data Structures: Local Search for Minimum Vertex Cover in Massive Graphs

The Minimum Vertex Cover (MinVC) problem is a well-known NP-hard problem. Recently there has been great interest in solving this problem on real-world massive graphs. For such graphs, local search is a promising approach to finding optimal or near-optimal solutions. In this paper we propose a local search algorithm that exploits reduction rules and data structures to solve the MinVC problem in such graphs. Experimental results on a wide range of real-word massive graphs show that our algorithm finds better covers than state-of-the-art local search algorithms for MinVC. Also we present interesting results about the complexities of some well-known heuristics.

cs.DS↗

arXiv2014

Modeling size segregation of granular materials: the roles of segregation, advection and diffusion

Predicting segregation of granular materials composed of different-sized particles is a challenging problem. In this paper, we develop and implement a theoretical model that captures the interplay between advection, segregation, and diffusion in size bidisperse granular materials. The fluxes associated with these three driving factors depend on the underlying kinematics, whose characteristics play key roles in determining particle segregation configurations. Unlike previous models for segregation, our model uses parameters based on kinematic measures from discrete element method simulations instead of arbitrarily adjustable fitting parameters, and it achieves excellent quantitative agreement with both experimental and simulation results when applied to quasi-two-dimensional bounded heaps. The model yields two dimensionless control parameters, both of which are only functions of physically control parameters (feed rate, particle sizes, and system size) and kinematic parameters (diffusion coefficient, flowing layer depth, and percolation velocity). The Péclet number, $Pe$, captures the interplay of advection and diffusion, and the second dimensionless parameter, $Λ$, describes the interplay between segregation and advection. A parametric study of $Λ$ and $Pe$ demonstrates how the particle segregation configuration depends on the interplay of advection, segregation, and diffusion. The model can be readily adapted to other flow geometries.

physics.flu-dyn↗

arXiv2012

Stratification, segregation and mixing of granular materials in quasi-2D bounded heaps

Segregation and mixing of granular mixtures during heap formation have important consequences in industry and agriculture. This research investigates three different final particle configurations of bi-disperse granular mixtures -- stratified, segregated and mixed -- during filling of quasi-two dimensional silos. We consider a larger number and relatively wider range of control parameters than previous studies, including particle size ratio, flow rate, system size and heap rise velocity. The boundary between stratified and unstratified states is primarily controlled by the two-dimensional flow rate, with the critical flow rate for the transition depending weakly on particle size ratio and flowing layer length. In contrast, the transition from segregated to mixed states is controlled by the rise velocity of the heap, a control parameter not previously considered. The critical rise velocity for the transition depends strongly on the particle size ratio.

cond-mat.soft↗