An incremental points-to analysis with CFL-reachability

Abstract. Developing scalable and precise points-to analyses is increasingly important for analysing and optimising object-oriented programs where pointers are used pervasively. An incremental analysis for a program updates the existing analysis information after program changes to avoid reanalysing it from scratch. This can be efficiently deployed in software development environments where code changes are often small and frequent. This paper presents an incremental approach for demand-driven context-sensitive points-to analyses based on Context-Free Language (CFL) reachability. By tracing the CFL-reachable paths traversed in computing points-to sets, we can precisely identify and recompute on demand only the points-to sets affected by the program changes made. Combined with a flexible policy for controlling the granularity of traces, our analysis achieves significant speedups with little space overhead over reanalysis from scratch when evaluated with a null dereferencing client using 14 Java benchmarks.

Quantum Approximate Optimization Algorithm Parameter Prediction Using a Convolutional Neural Network

The Quantum approximate optimization algorithm (QAOA) is a quantum-classical hybrid algorithm aiming to produce approximate solutions for combinatorial optimization problems. In the QAOA, the quantum part prepares a quantum parameterized state that encodes the solution, where the parameters are optimized by a classical optimizer. However, it is difficult to find optimal parameters when the quantum circuit becomes deeper. Hence, there is numerous active research on the performance and the optimization cost of QAOA. In this work, we build a convolutional neural network to predict parameters of depth QAOA instance by the parameters from the depth QAOA counterpart. We propose two strategies based on this model. First, we recurrently apply the model to generate a set of initial values for a certain depth QAOA. It successfully initiates depth 10 QAOA instances, whereas each model is only trained with the parameters from depths less than 6. Second, the model is applied repetitively until the maximum expected value is reached. An average approximation ratio of 0.9759 for Max-Cut over 264 Erd\H{o}s-R\'{e}nyi graphs is obtained, while the optimizer is only adopted for generating the first input of the model.Comment: 9 pages, 4 figures, 1 table

Promoted electron transport and sustained phonon transport by DNA down to 10 K

a b s t r a c t This work reports on a pioneering study of the electron transport in nanometer-thick Ir film supported by a DNA fiber, and the phonon transport sustained by the DNA itself. By evaluating the electrical resistivity (r e )~temperature relation based on the Block-Grüneisen theory, we find the Ir film has weak phonon softening indicated by 7e15% Debye temperature reduction. The Ir film's intrinsic r e is promoted by DNA electron thermal hopping and quantum tunneling, and is identical to that of bulk Ir. Although the nanocrystalline structure in ultrathin metallic films intends to give a higher Lorenz number since it reduces the electrical conductivity more than thermal conductivity, the DNA-promoted electron transport in the Ir film preserves a Lorenz number close to that of bulk crystalline Ir. By defining a new physical parameter entitled "thermal reffusivity", the residual phonon thermal resistivity of DNA is identified and evaluated for the first time. The thermal reffusivity concept can be widely used to predict the phonon thermal transport potential of defect-free materials. We predict that the thermal diffusivity of defect-free DNA fiber could be 36e61% higher than the samples studied herein. The structure domain size for phonon diffusion/scattering is determined as 0.8 nm in DNA

Iterative Layerwise Training for Quantum Approximate Optimization Algorithm

The capability of the quantum approximate optimization algorithm (QAOA) in solving the combinatorial optimization problems has been intensively studied in recent years due to its application in the quantum-classical hybrid regime. Despite having difficulties that are innate in the variational quantum algorithms (VQA), such as barren plateaus and the local minima problem, QAOA remains one of the applications that is suitable for the recent noisy intermediate scale quantum (NISQ) devices. Recent works have shown that the performance of QAOA largely depends on the initial parameters, which motivate parameter initialization strategies to obtain good initial points for the optimization of QAOA. On the other hand, optimization strategies focus on the optimization part of QAOA instead of the parameter initialization. Instead of having absolute advantages, these strategies usually impose trade-offs to the performance of the optimization problems. One of such examples is the layerwise optimization strategy, in which the QAOA parameters are optimized layer-by-layer instead of the full optimization. The layerwise strategy costs less in total compared to the full optimization, in exchange of lower approximation ratio. In this work, we propose the iterative layerwise optimization strategy and explore the possibility for the reduction of optimization cost in solving problems with QAOA. Using numerical simulations, we found out that by combining the iterative layerwise with proper initialization strategies, the optimization cost can be significantly reduced in exchange for a minor reduction in the approximation ratio. We also show that in some cases, the approximation ratio given by the iterative layerwise strategy is even higher than that given by the full optimization.Comment: 9 pages, 3 figure

Non-real eigenvalues of symmetric Sturm–Liouville problems with indefinite weight functions

The present paper deals with non-real eigenvalues of regular Sturm–Liouville problems with odd symmetry indefinite weight functions applying the two-parameter method. Sufficient conditions for the existence and non-existence of non-real eigenvalues are obtained. Furthermore, an explicit expression of the bound of non-real eigenvalues will be given in the paper

A Feasibility-Preserved Quantum Approximate Solver for the Capacitated Vehicle Routing Problem

The Capacitated Vehicle Routing Problem (CVRP) is an NP-optimization problem (NPO) that arises in various fields including transportation and logistics. The CVRP extends from the Vehicle Routing Problem (VRP), aiming to determine the most efficient plan for a fleet of vehicles to deliver goods to a set of customers, subject to the limited carrying capacity of each vehicle. As the number of possible solutions skyrockets when the number of customers increases, finding the optimal solution remains a significant challenge. Recently, a quantum-classical hybrid algorithm known as Quantum Approximate Optimization Algorithm (QAOA) can provide better solutions in some cases of combinatorial optimization problems, compared to classical heuristics. However, the QAOA exhibits a diminished ability to produce high-quality solutions for some constrained optimization problems including the CVRP. One potential approach for improvement involves a variation of the QAOA known as the Grover-Mixer Quantum Alternating Operator Ansatz (GM-QAOA). In this work, we attempt to use GM-QAOA to solve the CVRP. We present a new binary encoding for the CVRP, with an alternative objective function of minimizing the shortest path that bypasses the vehicle capacity constraint of the CVRP. The search space is further restricted by the Grover-Mixer. We examine and discuss the effectiveness of the proposed solver through its application to several illustrative examples.Comment: 9 pages, 8 figures, 1 tabl