IA Scholar Query: On Procedures as Open Subroutines. II.
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgWed, 30 Nov 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses
https://scholar.archive.org/work/fet5idxa6nb7dmsnxu7gm4gcey
We study differentially private (DP) stochastic optimization (SO) with loss functions whose worst-case Lipschitz parameter over all data points may be extremely large. To date, the vast majority of work on DP SO assumes that the loss is uniformly Lipschitz continuous over data (i.e. stochastic gradients are uniformly bounded over all data points). While this assumption is convenient, it often leads to pessimistic excess risk bounds. In many practical problems, the worst-case Lipschitz parameter of the loss over all data points may be extremely large due to outliers. In such cases, the error bounds for DP SO, which scale with the worst-case Lipschitz parameter of the loss, are vacuous. To address these limitations, this work provides near-optimal excess risk bounds that do not depend on the uniform Lipschitz parameter of the loss. Building on a recent line of work [WXDX20, KLZ22], we assume that stochastic gradients have bounded k-th order moments for some k ≥ 2. Compared with works on uniformly Lipschitz DP SO, our excess risk scales with the k-th moment bound instead of the uniform Lipschitz parameter of the loss, allowing for significantly faster rates in the presence of outliers and/or heavy-tailed data. For convex and strongly convex loss functions, we provide the first asymptotically optimal excess risk bounds (up to a logarithmic factor). In contrast to [WXDX20, KLZ22], our bounds do not require the loss function to be differentiable/smooth. We also devise an accelerated algorithm for smooth losses that runs in linear time and has excess risk that is tight in certain practical parameter regimes. Additionally, our work is the first to address non-convex non-uniformly Lipschitz loss functions satisfying the Proximal-PL inequality; this covers some practical machine learning models. Our Proximal-PL algorithm has near-optimal excess risk.Andrew Lowy, Meisam Razaviyaynwork_fet5idxa6nb7dmsnxu7gm4gceyWed, 30 Nov 2022 00:00:00 GMTDEVELOPMENT AND IMPLEMENTATION OF A TESTING FACILITY FOR REAL-TIME HYBRID SIMULATION WITH A NONLINEAR SPECIMEN
https://scholar.archive.org/work/3qkavzkusnebhcmavzemaj22ry
Real-time hybrid simulation (RTHS) has demonstrated certain advantages over conventional large-scale testing. In an RTHS, the system that is under study is partitioned into a numerical and a physical substructure, where the numerical part is comprised of those elements that are easier to model mathematically, while the physical part consists of those that present a complex behavior difficult to capture in a numerical model. The most complex part of this study is the isolation system, a technology used to protect structures against earthquakes by modifying how they respond to ground motions. Unbonded Fiber Reinforced Elastomeric Isolators (UFREIs) are devices that can accomplish this task and have gained attention in recent years because of their modest but valuable features that make them suitable for implementation in low-rise buildings and in developing countries because of their low cost. Our end goal for this work is to enable the testing of scaled versions of these elastomeric isolators to understand their behavior under shear tests and realistic loading. A testing instrument was designed and constructed to apply a uniaxial compressive force up to 22kN and a shear force of 8kN simultaneously to the specimens. A testing program was conducted where four primary sources of signal distortion were identified as caused by the servo-hydraulic system. From these results, a mechanics-based model was developed to understand better the dynamics that the sliding table can introduce to the measured signals accounting for inertial and dissipative forces. Two Bouc-Wen models were implemented to simulate the behavior of the UFREIs. The first only accounts for the hysteretic behavior of the isolator, and the second accounts for the additional nonlinearities found in the isolator's behavior. These models were assembled in a virtual RTHS which is available to users interested in learning the applications of RTHS of a base-isolated structure with a nonlinear component. An RTHS experiment was conducted in the IISL where the cont [...]Edwin Dielmig Patino Reyeswork_3qkavzkusnebhcmavzemaj22ryTue, 29 Nov 2022 00:00:00 GMT2017
https://scholar.archive.org/work/uowfgqp7hzh2lltvflrcjwdtsm
Fresnel and Fraunhoffer diffraction-Polarization methods for the production of polarized light. Einstein's coefficients (expression for energy density). Requisites of a Laser system. Condition for laser action. Principle, Construction and working of He-Ne laser Holography-Principle of Recording and reconstruction of images. Propagation mechanism in optical fibers. Angle of acceptance. Numerical aperture. Types of optical fibers and modes of propagation. Attenuation, Block diagram discussion of point to point communication, applications. Module -4 10 hours Crystal Structure: Space lattice, Bravais lattice-Unit cell, primitive cell. Lattice parameters. Crystal systems. Direction and planes in a crystal. Miller indices. Expression for interplanar spacing. Coordination number. Atomic packing factors (SC, FCC, BCC). Bragg's law, Determination of crystal structure using Bragg's X-ray diffractometer. Polymorphism and Allotropy. Crystal Structure of Diamond. Module -5 10 hours ELEMENTS OF ELECTRONICS ENGINEERING Subject Code 17SEC13/23 IA Marks 50 Number of lecture hours/week 04 Exam Marks 50 Total number of lecture hours 50 Credits 04 Course Objectives: 1. To provide basic concepts D.C circuits and circuit analysis techniques 2. To provide knowledge on A.C circuit fundamental techniques 3. To understand construction and operation of BJT and Junction FET 4. Explain the different modes of communications from wired to wireless and the computing involved. 5. To provide fundamental knowledge of Digital Logic. Course Outcomes: CO1: Understand concepts of electrical circuits and elements. CO2: Apply basic electric laws in solving circuit problems. CO3: Analyse simple circuits containing transistors CO4: Understand concept of cellular wireless networks. CO5: Understand Number systems and design basic digital circuits.BTECH.MECHwork_uowfgqp7hzh2lltvflrcjwdtsmMon, 28 Nov 2022 00:00:00 GMT2021
https://scholar.archive.org/work/gze4zbzdt5a6xe6yg6qrijqlca
BTECH.ECEwork_gze4zbzdt5a6xe6yg6qrijqlcaMon, 28 Nov 2022 00:00:00 GMT2021
https://scholar.archive.org/work/n7rhmaerpvfrhha4draeqwscs4
Course Objectives: 1. Learn and understand basic concepts and principles of Physics. 2. Make students familiar with latest trends in material science research and learn about novel materials and its applications. 3. Make students confident in analyzing engineering problems and apply its solutions effectively and meaningfully. 4. Gain knowledge in interference and diffraction of light and its applications in new technology. Course Outcomes: CO1: Learn and understand more about basic principles and to develop problem solving skills and implementation in technology. CO2: Study material properties and their application and its use in engineering applications and studies. CO3: Understand crystal structure and applications to boost the technical skills and its applications. CO4: Apply light phenomena in new technology. Module 1 Classical free electron theory-Free-electron concept (Drift velocity, Thermal velocity, Mean collision time, Mean free path, relaxation time) -Expression for electrical conductivity-Failure of classical free electron theory. Quantum free electron theory, Assumptions, Fermi factor, Fermi-Dirac Statistics. Expression for electrical conductivity based on quantum free electron theory. Merits of quantum free electron theory. Temperature dependence of electrical resistivity -Specific heat -Thermionic emission. Hall effect (Qualitative) -Wiedemann-Franz law. Teaching Methodology: Chalk and talk method: Classical free electron theory-Free-electron concept (Drift velocity, Thermal velocity, Mean collision time, Mean free path, relaxation time) -Expression for electrical conductivity-Failure of classical free electron theory. Powerpoint presentation: Quantum free electron theory, Assumptions, Fermi factor, Fermi-Dirac Statistics. Expression for electrical conductivity based on quantum free electron theory. Merits of quantum free electron theory. Temperature dependence of electrical resistivity -Specific heat -Thermionic emission. Wiedemann-Franz law. Self-study material: Hall effect (Qualitative) 9 Hours Module 2 Interaction of radiation with matter -Absorption-Spontaneous emission -Stimulated emission-Einstein's coefficients (expression for energy density). Requisites of a Laser system. Condition for laser action. Principle, Construction and working of He-Ne laser. Propagation mechanism in optical fibers. Angle of acceptance. Numerical aperture. Types of optical fibers-Step index and Graded index fiber. Modes of propagation-Single mode and Multimode fibers. Attenuation-Attenuation mechanisms. Teaching Methodology: Chalk and talk method: Interaction of radiation with matter -Absorption-Spontaneous emission -Stimulated emission-Einstein's coefficients (expression for energy density). Requisites of a Laser system. Condition for laser action. Propagation mechanism in optical fibers. Angle of acceptance. Numerical aperture. Powerpoint presentation: Types of optical fibers-Step index and Graded index fiber. Modes of propagation-Single mode and Multimode fibers. Video: Construction and working of He-Ne laser. Self-study material: Attenuation-Attenuation mechanisms. 9 Hours Module 3 Temperature dependence of resistivity in metals and superconducting materials. Effect of magnetic field (Meissner effect). Isotope effect -Type I and Type II superconductors-Temperature dependence of critical field. BCS theory (qualitative). High temperature superconductors-Josephson effect -SQUID-Applications of superconductors-Maglev vehicles (qualitative). Magnetic dipole-dipole moment-flux density-magnetic field intensity-Intensity of magnetization-magnetic permeability-susceptibility-relation between permeability and susceptibility. Classification of magnetic materials-Dia, Para, Ferromagnetism. Hysteresis-soft and hard magnetic materials. Teaching Methodology: Chalk and talk method: Temperature dependence of resistivity in metals and superconducting materials. Effect of magnetic field (Meissner effect). Isotope effect -Type I and Type II superconductors-Temperature dependence of critical field. BCS theory (qualitative). High temperature superconductors-Powerpoint presentation: Josephson effect -SQUID-Applications of superconductors. Magnetic dipole-dipole moment-flux density-magnetic field intensity-Intensity of magnetization-magnetic permeability-susceptibility-relation between permeability and susceptibility. Hysteresis-soft and hard magnetic materials. Video: Maglev vehicles (qualitative). Self-study material: Classification of magnetic materials-Dia, Para, Ferromagnetism 9 Hours Module 4 Amorphous and crystalline materials-Space lattice, Bravais lattice-Unit cell, primitive cell. Lattice parameters. Crystal systems. Direction and planes in a crystal. Miller indices -Determination of Miller indices of a plane. Expression for interplanar spacing. Atoms per unit cell -Co-ordination number. Relation between atomic radius and lattice constant -Atomic packing factors (SC, FCC, BCC). Bragg's law. Determination of crystal structure using Bragg's X-ray diffractometer -X-ray spectrum. Teaching Methodology: Chalk and talk method: Direction and planes in a crystal. Miller indices -Determination of Miller indices of a plane. Powerpoint presentation: Atoms per unit cell -Co-ordination number. Relation between atomic radius and lattice constant -Atomic packing factors (SC, FCC, BCC). Bragg's law. Determination of crystal structure using Bragg's X-ray diffractometer -X-ray spectrum. Self-study material: Amorphous and crystalline materials-Space lattice, Bravais lattice-Unit cell, primitive cell. Lattice parameters. Crystal systems. 9 Hours Module 5 Interference of light -Superposition of two coherent waves-Constructive and destructive interference. Interference in thin films -Wedge shaped thin film-Air wedge -Application to find the diameter of a thin wire. Newton's rings -Application to find the refractive index of a liquid. Diffraction of light -Classes of diffraction -Fresnel and Fraunhofer diffraction. Fresnel theory of half period zone -Zone plate.BTECH.CSwork_n7rhmaerpvfrhha4draeqwscs4Mon, 28 Nov 2022 00:00:00 GMTClassically optimized Hamiltonian simulation
https://scholar.archive.org/work/ftn6jhn7wfchvcrpskpxcepso4
Hamiltonian simulation is a promising application for quantum computers to achieve a quantum advantage. We present classical algorithms based on tensor network methods to optimize quantum circuits for this task. We show that, compared to Trotter product formulas, the classically optimized circuits can be orders of magnitude more accurate and significantly extend the total simulation time.Conor Mc Keever, Michael Lubaschwork_ftn6jhn7wfchvcrpskpxcepso4Mon, 28 Nov 2022 00:00:00 GMTOptimal and Adaptive Monteiro-Svaiter Acceleration
https://scholar.archive.org/work/tzue7usegbgkpfy2re7e45oe6e
We develop a variant of the Monteiro-Svaiter (MS) acceleration framework that removes the need to solve an expensive implicit equation at every iteration. Consequently, for any p≥ 2 we improve the complexity of convex optimization with Lipschitz pth derivative by a logarithmic factor, matching a lower bound. We also introduce an MS subproblem solver that requires no knowledge of problem parameters, and implement it as either a second- or first-order method by solving linear systems or applying MinRes, respectively. On logistic regression our method outperforms previous second-order momentum methods, but under-performs Newton's method; simply iterating our first-order adaptive subproblem solver performs comparably to L-BFGS.Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidfordwork_tzue7usegbgkpfy2re7e45oe6eMon, 28 Nov 2022 00:00:00 GMTActive volume: An architecture for efficient fault-tolerant quantum computers with limited non-local connections
https://scholar.archive.org/work/bn5ufifdg5gedaernazflyc56y
In existing general-purpose architectures for surface-code-based fault-tolerant quantum computers, the cost of a quantum computation is determined by the circuit volume, i.e., the number of qubits multiplied by the number of non-Clifford gates. We introduce an architecture using non-2D-local connections in which the cost does not scale with the number of qubits, and instead only with the number of logical operations. Each logical operation has an associated active volume, such that the cost of a quantum computation can be quantified as a sum of active volumes of all operations. For quantum computations with thousands of logical qubits, the active volume can be orders of magnitude lower than the circuit volume. Importantly, the architecture does not require all-to-all connectivity between N logical qubits. Instead, each logical qubit is connected to O(log N) other sites. As an example, we show that, using the same number of logical qubits, a 2048-bit factoring algorithm can be executed 44 times faster than on a general-purpose architecture without non-local connections. With photonic qubits, long-range connections are available and we show how photonic components can be used to construct a fusion-based active-volume quantum computer.Daniel Litinski, Naomi Nickersonwork_bn5ufifdg5gedaernazflyc56yMon, 28 Nov 2022 00:00:00 GMTBinary salt structure classification with convolutional neural networks: Application to crystal nucleation and melting point calculations
https://scholar.archive.org/work/bpsqqxkbjfb2tjqotdswl4tr4y
Convolutional neural networks are constructed and validated for the crystal structure classification of simple binary salts such as the alkali halides. The inputs of the neural network classifiers are the local bond orientational order parameters of Steinhardt, Nelson, and Ronchetti [Phys. Rev. B 28, 784 (1983)], which are derived solely from the relative positions of atoms surrounding a central reference atom. This choice of input gives classifiers that are invariant to density, increasing their transferability. The neural networks are trained and validated on millions of data points generated from a large set of molecular dynamics (MD) simulations of model alkali halides in nine bulk phases (liquid, rock salt, wurtzite, CsCl, 5-5, sphalerite, NiAs, AntiNiAs, and β-BeO) across a range of temperatures. One-dimensional time convolution is employed to filter out short-lived structural fluctuations. The trained neural networks perform extremely well, with accuracy up to 99.99% on a balanced validation dataset constructed from millions of labeled bulk phase structures. A typical analysis using the neural networks, including neighbor list generation, order parameter calculation, and class inference, is computationally inexpensive compared to MD simulations. As a demonstration of their accuracy and utility, the neural network classifiers are employed to follow the nucleation and crystal growth of two model alkali halide systems, crystallizing into distinct structures from the melt. We further demonstrate the classifiers by implementing them in automated MD melting point calculations. Melting points for model alkali halides using the most commonly employed rigid-ion interaction potentials are reported and discussed.H. O. Scheiber, G. N. Pateywork_bpsqqxkbjfb2tjqotdswl4tr4yMon, 28 Nov 2022 00:00:00 GMTCryptography with Certified Deletion
https://scholar.archive.org/work/oyt5k6yzdfho5jlqesnhjq4mei
We propose a new, unifying framework that yields an array of cryptographic primitives with certified deletion. These primitives enable a party in possession of a quantum ciphertext to generate a classical certificate that the encrypted plaintext has been information-theoretically deleted, and cannot be recovered even given unbounded computational resources. - For X ∈public-key, attribute-based, fully-homomorphic, witness, timed-release, our compiler converts any (post-quantum) X encryption to X encryption with certified deletion. In addition, we compile statistically-binding commitments to statistically-binding commitments with certified everlasting hiding. As a corollary, we also obtain statistically-sound zero-knowledge proofs for QMA with certified everlasting zero-knowledge assuming statistically-binding commitments. - We also obtain a strong form of everlasting security for two-party and multi-party computation in the dishonest majority setting. While simultaneously achieving everlasting security against all parties in this setting is known to be impossible, we introduce everlasting security transfer (EST). This enables any one party (or a subset of parties) to dynamically and certifiably information-theoretically delete other participants' data after protocol execution. We construct general-purpose secure computation with EST assuming statistically-binding commitments, which can be based on one-way functions or pseudorandom quantum states. We obtain our results by developing a novel proof technique to argue that a bit b has been information-theoretically deleted from an adversary's view once they output a valid deletion certificate, despite having been previously information-theoretically determined by the ciphertext they held in their view. This technique may be of independent interest.James Bartusek, Dakshita Khuranawork_oyt5k6yzdfho5jlqesnhjq4meiSun, 27 Nov 2022 00:00:00 GMTA Depolarizing Noise-aware Transpiler for Optimal Amplitude Amplification
https://scholar.archive.org/work/yilyqiaoe5cfna3nb3vhtbwd7a
Amplitude amplification provides a quadratic speed-up for an array of quantum algorithms when run on a quantum machine perfectly isolated from its environment. However, the advantage is substantially diminished as the NISQ-era quantum machines lack the large number of qubits necessary to provide error correction. Noise in the computation grows with the number of gate counts in the circuit with each iteration of amplitude amplification. After a certain number of amplifications, the loss in accuracy from the gate noise starts to overshadow the gain in accuracy due to amplification, forming an inflection point. Beyond this point, accuracy continues to deteriorate until the machine reaches a maximally mixed state where the result is uniformly random. Hence, quantum transpilers should take the noise parameters of the underlying quantum machine into consideration such that the circuit can be optimized to attain the maximal accuracy possible for that machine. In this work, we propose an extension to the transpiler that predicts the accuracy of the result at every amplification with high fidelity by applying pure Bayesian analysis to individual gate noise rates. Using this information, it finds the inflection point and optimizes the circuit by halting amplification at that point. The prediction is made without needing to execute the circuit either on a quantum simulator or an actual quantum machine.Debashis Ganguly, Wonsun Ahnwork_yilyqiaoe5cfna3nb3vhtbwd7aSat, 26 Nov 2022 00:00:00 GMTMonotone Inclusions, Acceleration and Closed-Loop Control
https://scholar.archive.org/work/kwq24zklhffobbejti4zm6lzee
We propose and analyze a new dynamical system with a closed-loop control law in a Hilbert space ℋ, aiming to shed light on the acceleration phenomenon for monotone inclusion problems, which unifies a broad class of optimization, saddle point and variational inequality (VI) problems under a single framework. Given A: ℋ⇉ℋ that is maximal monotone, we propose a closed-loop control system that is governed by the operator I - (I + λ(t)A)^-1, where a feedback law λ(·) is tuned by the resolution of the algebraic equation λ(t)(I + λ(t)A)^-1x(t) - x(t)^p-1 = θ for some θ > 0. Our first contribution is to prove the existence and uniqueness of a global solution via the Cauchy-Lipschitz theorem. We present a simple Lyapunov function for establishing the weak convergence of trajectories via the Opial lemma and strong convergence results under additional conditions. We then prove a global ergodic convergence rate of O(t^-(p+1)/2) in terms of a gap function and a global pointwise convergence rate of O(t^-p/2) in terms of a residue function. Local linear convergence is established in terms of a distance function under an error bound condition. Further, we provide an algorithmic framework based on the implicit discretization of our system in a Euclidean setting, generalizing the large-step HPE framework. Although the discrete-time analysis is a simplification and generalization of existing analyses for a bounded domain, it is largely motivated by the above continuous-time analysis, illustrating the fundamental role that the closed-loop control plays in acceleration in monotone inclusion. A highlight of our analysis is a new result concerning p^th-order tensor algorithms for monotone inclusion problems, complementing the recent analysis for saddle point and VI problems.Tianyi Lin, Michael. I. Jordanwork_kwq24zklhffobbejti4zm6lzeeSat, 26 Nov 2022 00:00:00 GMTConditional Gradient Methods
https://scholar.archive.org/work/b2imrksvmfclhaik7ghfh6bcte
The purpose of this survey is to serve both as a gentle introduction and a coherent overview of state-of-the-art Frank--Wolfe algorithms, also called conditional gradient algorithms, for function minimization. These algorithms are especially useful in convex optimization when linear optimization is cheaper than projections. The selection of the material has been guided by the principle of highlighting crucial ideas as well as presenting new approaches that we believe might become important in the future, with ample citations even of old works imperative in the development of newer methods. Yet, our selection is sometimes biased, and need not reflect consensus of the research community, and we have certainly missed recent important contributions. After all the research area of Frank--Wolfe is very active, making it a moving target. We apologize sincerely in advance for any such distortions and we fully acknowledge: We stand on the shoulder of giants.Gábor Braun, Alejandro Carderera, Cyrille W. Combettes, Hamed Hassani, Amin Karbasi, Aryan Mokhtari, Sebastian Pokuttawork_b2imrksvmfclhaik7ghfh6bcteFri, 25 Nov 2022 00:00:00 GMTNatural parameterized quantum circuit
https://scholar.archive.org/work/f5nlr7vganegjpmnt6n6wxxhce
Noisy intermediate scale quantum computers are useful for various tasks such as state preparation and variational quantum algorithms. However, the non-Euclidean quantum geometry of parameterized quantum circuits is detrimental for these applications. Here, we introduce the natural parameterized quantum circuit (NPQC) that can be initialised with a Euclidean quantum geometry. The initial training of variational quantum algorithms is substantially sped up as the gradient is equivalent to the quantum natural gradient. Further, we show how to estimate the parameters of the NPQC by sampling the circuit, which could be used for benchmarking or calibrating NISQ hardware. For a general class of quantum circuits, the NPQC has the minimal quantum Cram\'er-Rao bound which highlights its potential for quantum metrology. Finally, we show how to generate arbitrary superpositions of two states with the NPQCs for state preparation tasks. Our results can be used to enhance currently available quantum processors.Tobias Haug, M. S. Kimwork_f5nlr7vganegjpmnt6n6wxxhceFri, 25 Nov 2022 00:00:00 GMTTBPLaS: a Tight-Binding Package for Large-scale Simulation
https://scholar.archive.org/work/nd65eghj6newzgettvlrtcz35a
TBPLaS is an open-source software package for the accurate simulation of physical systems with arbitrary geometry and dimensionality utilizing the tight-binding (TB) theory. It has an intuitive object-oriented Python application interface (API) and Cython/Fortran extensions for the performance critical parts, ensuring both flexibility and efficiency. Under the hood, numerical calculations are mainly performed by both exact diagonalizatin and the tight-binding propagation method (TBPM) without diagonalization. Especially, the TBPM is based on the numerical solution of time-dependent Schr\"odinger equation, achieving linear scaling with system size in both memory and CPU costs. Consequently, TBPLaS provides a numerically cheap approach to calculate the electronic, transport and optical properties of large tight-binding models with billions of atomic orbitals. Current capabilities of TBPLaS include the calculation of band structure, density of states, local density of states, quasi-eigenstates, optical conductivity, electrical conductivity, Hall conductivity, polarization function, dielectric function, plasmon dispersion, carrier mobility and velocity, localization length and free path, Z2 topological invariant, wave-packet propagation, etc. All the properties can be obtained with only a few lines of code. Other algorithms involving tight-binding Hamiltonians can be implemented easily thanks to its extensible and modular nature. In this paper, we discuss the theoretical framework, implementation details and common workflow of TBPLaS, and give a few demonstrations of its applications.Yunhai Li, Zhen Zhan, Xueheng Kuang, Yonggang Li, Shengjun Yuanwork_nd65eghj6newzgettvlrtcz35aFri, 25 Nov 2022 00:00:00 GMTTwo Dimensional Isometric Tensor Networks on an Infinite Strip
https://scholar.archive.org/work/fywatfocvfgmlhg6ccx7sxbji4
The exact contraction of a generic two-dimensional (2D) tensor network state (TNS) is known to be exponentially hard, making simulation of 2D systems difficult. The recently introduced class of isometric TNS (isoTNS) represents a subset of TNS that allows for efficient simulation of such systems on finite square lattices. The isoTNS ansatz requires the identification of an "orthogonality column" of tensors, within which one-dimensional matrix product state (MPS) methods can be used for calculation of observables and optimization of tensors. Here we extend isoTNS to infinitely long strip geometries and introduce an infinite version of the Moses Move algorithm for moving the orthogonality column around the network. Using this algorithm, we iteratively transform an infinite MPS representation of a 2D quantum state into a strip isoTNS and investigate the entanglement properties of the resulting state. In addition, we demonstrate that the local observables can be evaluated efficiently. Finally, we introduce an infinite time-evolving block decimation algorithm (iTEBD2) and use it to approximate the ground state of the 2D transverse field Ising model on lattices of infinite strip geometry.Yantao Wu, Sajant Anand, Sheng-Hsuan Lin, Frank Pollmann, Michael P. Zaletelwork_fywatfocvfgmlhg6ccx7sxbji4Fri, 25 Nov 2022 00:00:00 GMTError mitigated quantum circuit cutting
https://scholar.archive.org/work/ytkpp6rw5rbg3bgppwcvhqkiu4
We investigate an error mitigated tomographic approach to the quantum circuit cutting problem in the presence of gate and measurement noise. We explore two tomography specific error mitigation techniques; readout error mitigated conditional fragment tomography, which uses knowledge of readout errors on all cut and conditional qubit measurements in the tomography reconstruction procedure; and dominant eigenvalue truncation (DEVT), which aims to improve the performance of circuit cutting by performing truncation of the individual conditional tomography fragments used in the reconstruction. We find that the performance of both readout error mitigated tomography and DEVT tomography are comparable for circuit cutting in the presence of symmetric measurement errors. For gate errors our numerical results show that probability estimates for the original circuit obtained using DEVT outperforms general circuit cutting for measurement, depolarization and weakly biased Pauli noise models, but does not improve performance for amplitude damping and coherent errors, and can greatly decrease performance for highly biased Pauli noise. In cases where DEVT was effective, it as also found to improve performance of partial tomographic reconstruction using at least 50% of the full tomographic data with a conditional least-squares tomographic fitter, while linear inversion tomography with or without DEVT mitigation was found to perform poorly with with partial data.Ritajit Majumdar, Christopher J. Woodwork_ytkpp6rw5rbg3bgppwcvhqkiu4Thu, 24 Nov 2022 00:00:00 GMTFast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks
https://scholar.archive.org/work/kepdwpbilfhtda7jfolqvkgfde
Next-generation wireless services are characterized by a diverse set of requirements, to sustain which, the wireless access points need to probe the users in the network periodically. In this regard, we study a novel multi-armed bandit (MAB) setting that mandates probing all the arms periodically while keeping track of the best current arm in a non-stationary environment. In particular, we develop that balances the regret guarantees of classical Thompson sampling (TS) with the broadcast probing (BP) of all the arms simultaneously in order to actively detect a change in the reward distributions. The main innovation in the algorithm is in identifying the changed arm by an optional subroutine called group exploration (GE) that scales as log_2(K) for a K-armed bandit setting. We characterize the probability of missed detection and the probability of false-alarm in terms of the environment parameters. We highlight the conditions in which the regret guarantee of outperforms that of the state-of-the-art algorithms, in particular, and . We demonstrate the efficacy of by employing it in two wireless system application - task offloading in mobile-edge computing (MEC) and an industrial internet-of-things (IIoT) network designed for simultaneous wireless information and power transfer (SWIPT).Gourab Ghatakwork_kepdwpbilfhtda7jfolqvkgfdeThu, 24 Nov 2022 00:00:00 GMTDifferentially Private Fair Division
https://scholar.archive.org/work/ncxaqfnpn5afppxzvrne6kfxee
Fairness and privacy are two important concerns in social decision-making processes such as resource allocation. We study privacy in the fair allocation of indivisible resources using the well-established framework of differential privacy. We present algorithms for approximate envy-freeness and proportionality when two instances are considered to be adjacent if they differ only on the utility of a single agent for a single item. On the other hand, we provide strong negative results for both fairness criteria when the adjacency notion allows the entire utility function of a single agent to change.Pasin Manurangsi, Warut Suksompongwork_ncxaqfnpn5afppxzvrne6kfxeeWed, 23 Nov 2022 00:00:00 GMTTechniques, Tricks and Algorithms for Efficient GPU-Based Processing of Higher Order Hyperbolic PDEs
https://scholar.archive.org/work/zg64xidkpbbqjnhpadh76ajmky
GPU computing is expected to play an integral part in all modern Exascale supercomputers. It is also expected that higher order Godunov schemes will make up about a significant fraction of the application mix on such supercomputers. It is, therefore, very important to prepare the community of users of higher order schemes for hyperbolic PDEs for this emerging opportunity. We focus on three broad and high-impact areas where higher order Godunov schemes are used. The first area is computational fluid dynamics (CFD). The second is computational magnetohydrodynamics (MHD) which has an involution constraint that has to be mimetically preserved. The third is computational electrodynamics (CED) which has involution constraints and also extremely stiff source terms. Together, these three diverse uses of higher order Godunov methodology, cover many of the most important applications areas. In all three cases, we show that the optimal use of algorithms, techniques and tricks, along with the use of OpenACC, yields superlative speedups on GPUs! As a bonus, we find a most remarkable and desirable result: some higher order schemes, with their larger operations count per zone, show better speedup than lower order schemes on GPUs. In other words, the GPU is an optimal stratagem for overcoming the higher computational complexities of higher order schemes! Several avenues for future improvement have also been identified. A scalability study is presented for a real-world application using GPUs and comparable numbers of high-end multicore CPUs. It is found that GPUs offer a substantial performance benefit over comparable number of CPUs, especially when all the methods designed in this paper are used.Sethupathy Subramanian, Dinshaw S. Balsara, Deepak Bhoriya, Harish Kumarwork_zg64xidkpbbqjnhpadh76ajmkyWed, 23 Nov 2022 00:00:00 GMT