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Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges due to the noisy intermediate-scale quantum era's inherent external noise issues. This paper discusses the integration of QC as a computational accelerator within classical scientific high-performance computing (HPC) systems. By leveraging a broad spectrum of simulators and hardware technologies, we propose a hardware-agnostic framework for augmenting classical HPC with QC capabilities. Drawing on the HPC expertise of the Oak Ridge National Laboratory (ORNL) and the HPC lifecycle management of the Department of Energy (DOE), our approach focuses on the strategic incorporation of QC capabilities and acceleration into existing scientific HPC workflows. This includes detailed analyses, benchmarks, and code optimization driven by the needs of the DOE and ORNL missions. Our comprehensive framework integrates hardware, software, workflows, and user interfaces to foster a synergistic environment for quantum and classical computing research. This paper outlines plans to unlock new computational possibilities, driving forward scientific inquiry and innovation in a wide array of research domains.

Scientific applications are starting to explore the viability of quantum computing. This exploration typically begins with quantum simulations that can run on existing classical platforms, albeit without the performance advantages of real quantum resources. In the context of high-performance computing (HPC), the incorporation of simulation software can often take advantage of the powerful resources to help scale-up the simulation size. The configuration, installation and operation of these quantum simulation packages on HPC resources can often be rather daunting and increases friction for experimentation by scientific application developers. We describe a framework to help streamline access to quantum simulation software running on HPC resources. This includes an interface for circuit-based quantum computing tasks, as well as the necessary resource management infrastructure to make effective use of the underlying HPC resources. The primary contributions of this work include a classification of different usage models for quantum simulation in an HPC context, a review of the software architecture for our approach and a detailed description of the prototype implementation to experiment with these ideas using two different simulators (TNQVM \& NWQ-Sim). We include initial experimental results running on the Frontier supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) using a synthetic workload generated via the SupermarQ quantum benchmarking framework.

Quantum approximated optimization algorithm (QAOA) has shown promise for solving combinatorial optimization problems by providing quantum speedup on near-term gate-based quantum computing systems. However, QAOA faces challenges in optimizing variational parameters for high-dimensional problems due to the large number of qubits required and the complexity of deep circuits, which limit its scalability for real-world applications. In this study, we propose a distributed QAOA (DQAOA), which leverages a high-performance computing-quantum computing (HPC-QC) integrated system. DQAOA leverages distributed computing strategies to decompose a large job into smaller tasks, which are then processed on the HPC-QC system. The global solution is iteratively updated by aggregating sub-solutions obtained from DQAOA, allowing convergence toward the optimal solution. We demonstrate that DQAOA can handle considerably large-scale optimization problems (e.g., 1,000-bit problem) achieving high accuracy (~99%) and short time-to-solution (~276 s). To apply this algorithm to material science, we further develop an active learning algorithm integrated with our DQAOA (AL-DQAOA), which involves machine learning, DQAOA, and active data production in an iterative loop. We successfully optimize photonic structures using AL-DQAOA, indicating that solving real-world optimization problems using gate-based quantum computing is feasible with our strategies. We expect the proposed DQAOA to be applicable to a wide range of optimization problems and AL-DQAOA to find broader applications in material design.

Optimizing metamaterials with complex geometries is a big challenge. Although an active learning algorithm, combining machine learning (ML), quantum computing, and optical simulation, has emerged as an efficient optimization tool, it still faces difficulties in optimizing complex structures that have potentially high performance. In this work, we comprehensively analyze the performance of an optimization algorithm for metamaterial design on the integrated HPC and quantum systems. We demonstrate significant time advantages through message-passing interface (MPI) parallelization on the high-performance computing (HPC) system showing approximately 54% faster ML tasks and 67 times faster optical simulation against serial workloads. Furthermore, we analyze the performance of a quantum algorithm designed for optimization, which runs with various quantum simulators on a local computer or HPC-quantum system. Results showcase ~24 times speedup when executing the optimization algorithm on the HPC-quantum hybrid system. This study paves a way to optimize complex metamaterials using the integrated HPC-quantum system., Comment: 11 pages, 9 figures

In the current era of Noisy Intermediate Scale Quantum (NISQ) computing, efficient digital simulation of quantum systems holds significant importance for quantum algorithm development, verification and validation. However, analysis of sparsity within these simulations remains largely unexplored. In this paper, we present a novel observation regarding the prevalent sparsity patterns inherent in quantum circuits. We introduce DiaQ, a new sparse matrix format tailored to exploit this quantum-specific sparsity, thereby enhancing simulation performance. Our contribution extends to the development of libdiaq, a numerical library implemented in C++ with OpenMP for multi-core acceleration and SIMD vectorization, featuring essential mathematical kernels for digital quantum simulations. Furthermore, we integrate DiaQ with SV-Sim, a state vector simulator, yielding substantial performance improvements across various quantum circuits (e.g., ~26.67% for GHZ-28 and ~32.72% for QFT-29 with multi-core parallelization and SIMD vectorization on Frontier). Evaluations conducted on benchmarks from SupermarQ and QASMBench demonstrate that DiaQ represents a significant step towards achieving highly efficient quantum simulations., Comment: 11 pages, 8 figures

Although there have been remarkable advances in quantum computing (QC), it remains crucial to simulate quantum programs using classical large-scale parallel computing systems to validate quantum algorithms, comprehend the impact of noise, and develop resilient quantum applications. This is particularly important for bridging the gap between near-term noisy-intermediate-scale-quantum (NISQ) computing and future fault-tolerant quantum computing (FTQC). Nevertheless, current simulation methods either lack the capability to simulate noise, or simulate with excessive computational costs, or do not scale out effectively. In this paper, we propose TANQ-Sim, a full-scale density matrix based simulator designed to simulate practical deep circuits with both coherent and non-coherent noise. To address the significant computational cost associated with such simulations, we propose a new density-matrix simulation approach that enables TANQ-Sim to leverage the latest double-precision tensorcores (DPTCs) in NVIDIA Ampere and Hopper GPUs. To the best of our knowledge, this is the first application of double-precision tensorcores for non-AI/ML workloads. To optimize performance, we also propose specific gate fusion techniques for density matrix simulation. For scaling, we rely on the advanced GPU-side communication library NVSHMEM and propose effective optimization methods for enhancing communication efficiency. Evaluations on the NERSC Perlmutter supercomputer demonstrate the functionality, performance, and scalability of the simulator. We also present three case studies to showcase the practical usage of TANQ-Sim, including teleportation, entanglement distillation, and Ising simulation. TANQ-Sim will be released on GitHub., Comment: 14 pages, 12 figures, 4 tables

Adiabatic quantum computers can solve difficult optimization problems (e.g., the quadratic unconstrained binary optimization problem), and they seem well suited to train machine learning models. In this paper, we describe an adiabatic quantum approach for training support vector machines. We show that the time complexity of our quantum approach is an order of magnitude better than the classical approach. Next, we compare the test accuracy of our quantum approach against a classical approach that uses the Scikit-learn library in Python across five benchmark datasets (Iris, Wisconsin Breast Cancer (WBC), Wine, Digits, and Lambeq). We show that our quantum approach obtains accuracies on par with the classical approach. Finally, we perform a scalability study in which we compute the total training times of the quantum approach and the classical approach with increasing number of features and number of data points in the training dataset. Our scalability results show that the quantum approach obtains a 3.5--4.5 times speedup over the classical approach on datasets with many (millions of) features.

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions., Comment: 65 pages, 15 figures; comments welcome

Quantum Computers offer an intriguing challenge in modern Computer Science. With the inevitable physical limitations to Moore's Law, quantum hardware provides avenues to solve grander problems faster by utilizing Quantum Mechanical properties at subatomic scales. These futuristic devices will likely never replace traditional HPC, but rather work alongside them to perform complex tasks, utilizing the best of decades of HPC and quantum computing research. We leverage the capabilities of scientific workflows to make traditional HPC and Quantum Computers work together. To demonstrate this capability, we implemented three algorithms: Grover's Search Algorithm, Shor's Factoring Algorithm, and a 4-node Traveling Salesman Algorithm. The algorithms' implementation and generated inputs are sent from ORNL HPC to IBMQ, the algorithms run on IBMQ, and the results return. The entire process is automated as a workflow by encoding it into the Parsl parallel scripting and workflow platform., Comment: 15 pages, 8 figures

As neutron stars merge they can approach very high nuclear density. Here, we summarized recent results for the evolution and gravitational wave emission from binary neutron star mergers using a a variety of nuclear equations of state with and without a crossover transition to quark matter. We discuss how the late time gravitational wave emission from binary neutron star mergers may possibly reveal the existence of a crossover transition to quark matter., Comment: Proceedings of the XVth Quark Confinement and the Hadron Spectrum, Stvanger, Norway, August 1-6, 2022

The Poisson equation has many applications across the broad areas of science and engineering. Most quantum algorithms for the Poisson solver presented so far either suffer from lack of accuracy and/or are limited to very small sizes of the problem, and thus have no practical usage. Here we present an advanced quantum algorithm for solving the Poisson equation with high accuracy and dynamically tunable problem size. After converting the Poisson equation to a linear system through the finite difference method, we adopt the HHL algorithm as the basic framework. Particularly, in this work we present an advanced circuit that ensures the accuracy of the solution by implementing non-truncated eigenvalues through eigenvalue amplification, as well as by increasing the accuracy of the controlled rotation angular coefficients, which are the critical factors in the HHL algorithm. Consequently, we are able to drastically reduce the relative error in the solution while achieving higher success probability as the amplification level is increased. We show that our algorithm not only increases the accuracy of the solutions but also composes more practical and scalable circuits by dynamically controlling problem size in NISQ devices. We present both simulated and experimental results and discuss the sources of errors. Finally, we conclude that though overall results on the existing NISQ hardware are dominated by the error in the CNOT gates, this work opens a path to realizing a multidimensional Poisson solver on near-term quantum hardware., Comment: 13 pages, 11 figures, 1 table

The Poisson equation has many applications across the broad areas of science and engineering. Most quantum algorithms for the Poisson solver presented so far, either suffer from lack of accuracy and/or are limited to very small sizes of the problem, and thus have no practical usage. Here we present an advanced quantum algorithm for solving the Poisson equation with high accuracy and dynamically tunable problem size. After converting the Poisson equation to the linear systems through the finite difference method, we adopt the Harrow-Hassidim-Lloyd (HHL) algorithm as the basic framework. Particularly, in this work we present an advanced circuit that ensures the accuracy of the solution by implementing non-truncated eigenvalues through eigenvalue amplification as well as by increasing the accuracy of the controlled rotation angular coefficients, which are the critical factors in the HHL algorithm. We show that our algorithm not only increases the accuracy of the solutions, but also composes more practical and scalable circuits by dynamically controlling problem size in the NISQ devices. We present both simulated and experimental solutions, and conclude that overall results on the quantum hardware are dominated by the error in the CNOT gates., Comment: Quantum Week QCE 2022, poster paper

It is anticipated that the gravitational radiation detected in future gravitational wave (GW) detectors from binary neutron star (NS) mergers can probe the high-density equation of state (EOS). We perform the first simulations of binary NS mergers which adopt various parametrizations of the quark-hadron crossover (QHC) EOS. These are constructed from combinations of a hadronic EOS ($n_{b} < 2~n_0$) and a quark-matter EOS ($n_{b} >~5~n_0$), where $n_{b}$ and $n_0$ are the baryon number density and the nuclear saturation density, respectively. At the crossover densities ($2~ n_0 < n_{b} < 5~ n_0$) the QHC EOSs continuously soften, while remaining stiffer than hadronic and first-order phase transition EOSs, achieving the stiffness of strongly correlated quark matter. This enhanced stiffness leads to significantly longer lifetimes of the postmerger NS than that for a pure hadronic EOS. We find a dual nature of these EOSs such that their maximum chirp GW frequencies $f_{max}$ fall into the category of a soft EOS while the dominant peak frequencies ($f_{peak}$) of the postmerger stage fall in between that of a soft and stiff hadronic EOS. An observation of this kind of dual nature in the characteristic GW frequencies will provide crucial evidence for the existence of strongly interacting quark matter at the crossover densities for QCD., Comment: 8 pages, 3 figures

The nuclear equation of state is an important component in the evolution of core collapse supernovae. In this paper we make a survey of various equations of state in the literature and analyze their effect on spherical core-collapse models in which the effects of three-dimensional turbulence is modeled by a general relativistic formulation of Supernova Turbulence in Reduced dimensionality (STIR). We show that the viability of the explosion is quite EOS dependent and that it best correlates with the early-time interior entropy density of the proto-neutron star. We check that this result is not progenitor dependent, although low-mass progenitors show different explosion properties, due to the different pre-collapse nuclear composition. Larger central entropies also induce more vigorous proto-neutron-star convection in our one-dimensional turbulence model, as well as a wider convective layer., Comment: Accepted to ApJ

The first detection of gravitational waves from the binary neutron star merger GW170817 by the LIGO-Virgo Collaboration has provided fundamental new insights into the astrophysical site for r-process nucleosynthesis and on the nature of dense neutron-star matter. The detected gravitational wave signal depends upon the tidal distortion of the neutron stars as they approach merger. We report on relativistic numerical simulations of the approach to binary merger in the conformally flat, quasi-circular orbit approximation. We show that this event serves as a calibration to the quasi-circular approximation and a confirmation of the validity of the conformally flat approximation to the three-metric. We then examine how the detected chirp depends upon the adopted equation of state. This establishes a new efficient means to constrain the nuclear equation of state in binary neutron star mergers., Comment: 5 pages, 1 figure

The core-cusp problem remains as a challenging discrepancy between observations and simulations in the standard $\Lambda$CDM model for the formation of galaxies. The problem is that $\Lambda$CDM simulations predict a steep power-law mass density profile at the center of galactic dark matter halos. However, observations of dwarf galaxies in the Local Group reveal a density profile consistent with a nearly flat distribution of dark matter near the center. A number of solutions to this dilemma have been proposed. Here, we summarize investigations into the possibility that the dark matter particles themselves self interact and scatter. Such self-interacting dark matter (SIDM) particles can smooth out the dark-matter profile in high-density regions. We also review the theoretical proposal that self-interacting dark matter may arise as an additional Higgs scalar in the 3-3-1 extension of the standard model. We present new simulations of galaxy formation and evolution for this formulation of self-interacting dark matter. Current constraints on this self-interacting dark matter are then summarized., Comment: 11 pages, 5 figures, accepted in MPLA

We present new simulations of the flux power spectrum of the Lyman $\alpha$ forest as a means to investigate the effects of time-dependent dark energy on structure formation. We use a linearized parameterization of the time-dependence of the dark energy equation of state and sample the parameters ($w_0,w_a$) from the the extrema of the allowed observational values as determined by the Planck results. Each chosen ($w_0,w_a$) pair is then used in a high-resolution, large-scale cosmological simulation run with a modified version of the publicly available SPH code {\tt GADGET-2}. From each of these simulations we extract synthetic Lyman $\alpha$ forest spectra and calculate the flux power spectrum. We use the k-sample Anderson-Darling test to analyze the effects of dark energy on the Lyman $\alpha$ forest. We compare each dark energy power spectrum to that due to a cosmological constant power spectrum. We find, however, that there is only a marginal effect of the choice of allowed dark energy models on the flux power spectrum., Comment: 19 pages, 10 figures, 5 tables

We present a new method of tracking and characterizing the environment in which galaxies and their associated circumgalactic medium evolve. We use a structure finding algorithm we developed to self-consistently parse and follow the evolution of poor clusters, filaments and voids in large scale simulations. We trace the complete evolution of the baryons in the gas phase and the star formation history within each structure in our simulated volume. We vary the structure measure threshold to probe the complex inner structure of star forming regions in poor clusters, filaments and voids. We find the majority of star formation occurs in cold, condensed gas in filaments at intermediate redshifts (z ~ 3). We also show that much of the star formation above a redshift z = 3 occurs in low contrast regions of filaments, but as the density contrast increases at lower redshift star formation switches to the high contrast regions, or inner parts, of filaments. Since filaments bridge the void and cluster regions, it suggests that the majority of star formation occurs in galaxies in intermediate density regions prior to the accretion onto poor clusters. We find that at the present epoch, the gas phase distribution is 43.1%, 30.0%, 24.7% and 2.2% in the diffuse, WHIM, hot halo and condensed phases, respectively. The majority of the WHIM is associated with filaments. However, their multiphase nature and the fact that the star formation occurs predominantly in the condensed gas both point to the importance of not conflating the filamentary environment with the WHIM. Moreover, in our simulation volume 8.77%, 79.1%, 2.11% of the gas at z = 0 is located in poor clusters, filaments, and voids, respectively. We find that both filaments and poor clusters are multiphase environments distinguishing themselves by different distribution of gas phases., Comment: 26 pages, 17 figures, submitted to MNRAS

We introduce a new self-consistent structure finding algorithm that parses large scale cosmological structure into clusters, filaments and voids. This structure finding algorithm probes the cosmological structure at multiple scales and clas- sifies the appropriate regions with the most probable structure type and size. We show that it reproduces key observational results, including the baryon fraction of ICM, and average temperatures and densities for the different structures., Comment: 17 pages, 5 figures

Magnetars have been suggested as the most promising site for the origin of observed soft gamma-ray repeaters (SGRs) and anomalous X-ray pulsars (AXPs). In this work we investigate the possibility that SGRs and AXPs might be observational evidence for a magnetic phase separation in magnetars. We study magnetic domain formation as a new mechanism for SGRs and AXPs in which magnetar-matter separates into two phases containing different flux densities. We identify the parameter space in matter density and magnetic field strength at which there is an instability for magnetic domain formation. We conclude that such instabilities will likely occur in the deep outer crust for the magnetic Baym, Pethick, and Sutherland (BPS) model and in the inner crust and core for magnetars described in relativistic Hartree theory. Moreover, we estimate that the energy released by the onset of this instability is comparable with the energy emitted by SGRs., Comment: 4 figures, ApJ in press

We comment on the paper by Tanusri Ghosh and Somenath Chakrabarty, Phys. Rev. D63, 043006 (2001). In that paper it was argued that a first-order transition to quark matter in the core of a magnetar is absolutely forbidden if its magnetic field strength exceeds $10^{15}$ G. However, we show in this comment that if the quark anomalous magnetic moment and the population of higher Landau levels is taken into account, it may still be possible for a first-order phase transition from nuclear matter to quark matter to occur in the core of a magnetar. These effects may also obscure the question of whether beta equilibrium favors strange quark matter in the core of a magnetar., Comment: To appear in Phys. Rev. D Comments

Recently, neutron stars with very strong surface magnetic fields have been suggested as the site for the origin of observed soft gamma repeaters (SGRs). We investigate the influence of a strong magnetic field on the properties and internal structure of such strongly magnetized neutron stars (magnetars). The presence of a sufficiently strong magnetic field changes the ratio of protons to neutrons as well as the neutron appearance density. We also study the pion production and pion condensation in a strong magnetic field. We discuss the pion condensation in the interior of magnetars as a possible source of SGRs., Comment: 5 pages with 3 figures, To appear in the Proceedings of the 5th Huntsville Gamma Ray Burst Symposium, Huntsville, Alabama, USA, Oct. 18-22, 1999

Neutron stars with very strong surface magnetic fields have been suggested as the site for the origin of observed soft gamma repeaters (SGRs). In this paper we investigate the influence of such strong magnetic fields on the properties and internal structure of these magnetized neutron stars (magnetars). We study properties of a degenerate equilibrium ideal neutron-proton-electron (npe) gas with and without the effects of the anomalous nucleon magnetic moments in a magnetic field. The presence of a sufficiently strong magnetic field changes the ratio of protons to neutrons as well as the neutron drip density. We also study the appearance of muons as well as pion condensation in strong magnetic fields. We discuss the possibility that boson condensation in the interior of magnetars might be a source of SGRs., Comment: 10 pages included 9 figures, ApJ in press

Recently, several white dwarfs with very strong surface magnetic fields have been observed. In this paper we explore the possibility that such stars could have sufficiently strong internal fields to alter their structure. We obtain a revised white dwarf mass-radius relation in the presence of strong internal magnetic fields. We first derive the equation of state for a fully degenerate ideal electron gas in a magnetic field using an Euler-MacLaurin expansion. We use this to obtain the mass-radius relation for magnetic $^{4}$He, $^{12}$C, and $^{56}$Fe white dwarfs of uniform composition., Comment: 7 pages, 7 figures and 1 table, To appear in ApJ

We explore constraints on the strength of the primordial magnetic field based upon the weak reaction freeze-out in the early universe. We find that limits on the strength of the magnetic field found in other works are recovered simply by examining the temperature at which the rate of weak reactions drops below the rate of universal expansion ($\Gamma_{w} \le$ H). The temperature for which the $n/p$ ratio at freeze-out leads to acceptable helium production implies limits on the magnetic field. This simplifies the application of magnetic fields to other cosmological variants of the standard big-bang. As an illustration we also consider effects of neutrino degeneracy on the allowed limits to the primordial magnetic field., Comment: Submitted to Phys. Rev. D., 6 pages, 2 figures

We have studied finite temperature corrections to the baryon transport cross sections and diffusion coefficients. These corrections are based upon the recently computed renormalized electron mass and the modified state density due to the background thermal bath in the early universe. It is found that the optimum nucleosynthesis yields computed using our diffusion coefficients shift to longer distance scales by a factor of about 3. We also find that the minimum value of $^4 He$ abundance decreases by $\Delta Y_p \simeq 0.01$ while $D$ and $^7 Li$ increase. Effects of these results on constraints from primordial nucleosynthesis are discussed. In particular, we find that a large baryonic contribution to the closure density ($\Omega_b h_{50}^{2} \lsim 0.4$) may be allowed in inhomogeneous models corrected for finite temperature., Comment: 7 pages, 6 figures, submitted to Phys. Rev. D

We have computed finite temperature corrections to the electron-hadron scattering cross sections. These are based upon the renormalized electron mass and the modified density of states due to the presence of a background thermal bath. It is found that the electron-hadron thermal transport scattering cross section can be much larger than the zero temperature one. In the case of electron-neutron transport scattering, we find \sigma_{ne}(T) / \sigma_{ne} (T=0) \simeq 5 at T \simeq 0.1 MeV., Comment: 6 pages, 3 figures, submitted to Phys. Rev. D

The conversion of neutron matter into strange matter in a neutron star occurs through the non-leptonic weak-interaction process. We study the energy loss of the neutron star by the emission of axions in that process. Owing to that process, the neutron star will liberate the energy which can in no way be negligible as an axion burst., Comment: 12 pages, 2 figures, submitted to Phys. Lett. B