Optimal Scheduling Using Branch and Bound with SPIN 4.0

The use of model checkers to solve discrete optimisation problems is appealing. A model checker can first be used to verify that the model of the problem is correct. Subsequently, the same model can be used to find an optimal solution for the problem. This paper describes how to apply the new PROMELA primitives of SPIN 4.0 to search effectively for the optimal solution. We show how Branch-and-Bound techniques can be added to the LTL property that is used to find the solution. The LTL property is dynamically changed during the verification. We also show how the syntactical reordering of statements and/or processes in the PROMELA model can improve the search even further. The techniques are illustrated using two running examples: the Travelling Salesman Problem and a job-shop scheduling problem

Verification and Optimization of a PLC Control Schedule

We report on the use of the SPIN model checker for both the verification of a process control program and the derivation of optimal control schedules. This work was carried out as part of a case study for the EC VHS project (Verification of Hybrid Systems), in which the program for a Programmable Logic Controller (PLC) of an experimental chemical plant had to be designed and verified. The intention of our approach was to see how much could be achieved here using the standard model checking environment of SPIN/Promela. As the symbolic calculations of real-time model checkers can be quite expensive it is interesting to try and exploit the efficiency of established non-real-time model checkers like SPIN in those cases where promising work-arounds seem to exist. In our case we handled the relevant real-time properties of the PLC controller using a time-abstraction technique; for the scheduling we implemented in Promela a so-called variable time advance procedure. For this case study these techniques proved sufficient to verify the design of the controller and derive (time-)optimal schedules with reasonable time and space requirements

Model checking object-Z using ASM

A major problem with creating tools for Object-Z is that its high-level abstractions are difficult to deal with directly. Integrating Object-Z with a more concrete notation is a sound strategy. With this in mind, in this paper we introduce an approach to model-checking Object-Z specifications based on first integrating Object-Z with the Abstract State Machine (ASM) notation to get the notation OZ-ASM. We show that this notation can be readily translated into the specification language ASM-SL, a language that can be automatically translated into the language of the temporal logic model checker SMV

Solitary fibrous tumor of the orbit—two cases and a review of the literature

Solitary fibrous tumors of the orbit (SFT) are mesenchymal lesions that can develop either as malignant or benign neoplasias. We describe the histological features leading to the diagnosis in two females and review the current literature. Diagnosis of SFT only can be performed by histological examination, since clinical signs and radiological features are not specific enough. Even a malignant or benign course cannot be predicted, since clinical and radiological features do not correlate with histological signs of malignancy and vice versa. Radical resection is the treatment of choice, since no other treatment option has been proven to be efficien

Parallel Recursive State Compression for Free

This paper focuses on reducing memory usage in enumerative model checking, while maintaining the multi-core scalability obtained in earlier work. We present a tree-based multi-core compression method, which works by leveraging sharing among sub-vectors of state vectors. An algorithmic analysis of both worst-case and optimal compression ratios shows the potential to compress even large states to a small constant on average (8 bytes). Our experiments demonstrate that this holds up in practice: the median compression ratio of 279 measured experiments is within 17% of the optimum for tree compression, and five times better than the median compression ratio of SPIN's COLLAPSE compression. Our algorithms are implemented in the LTSmin tool, and our experiments show that for model checking, multi-core tree compression pays its own way: it comes virtually without overhead compared to the fastest hash table-based methods.Comment: 19 page

Hierarchical Temporal Representation in Linear Reservoir Computing

Recently, studies on deep Reservoir Computing (RC) highlighted the role of layering in deep recurrent neural networks (RNNs). In this paper, the use of linear recurrent units allows us to bring more evidence on the intrinsic hierarchical temporal representation in deep RNNs through frequency analysis applied to the state signals. The potentiality of our approach is assessed on the class of Multiple Superimposed Oscillator tasks. Furthermore, our investigation provides useful insights to open a discussion on the main aspects that characterize the deep learning framework in the temporal domain.Comment: This is a pre-print of the paper submitted to the 27th Italian Workshop on Neural Networks, WIRN 201

The effect of disorder on the critical temperature of a dilute hard sphere gas

We have performed Path Integral Monte Carlo (PIMC) calculations to determine the effect of quenched disorder on the superfluid density of a dilute 3D hard sphere gas. The disorder was introduced by locating set of hard cylinders randomly inside the simulation cell. Our results indicate that the disorder leaves the superfluid critical temperature basically unchanged. Comparison to experiments of helium in Vycor is made.Comment: 4 pages, 4 figure

Statistical phase estimation and error mitigation on a superconducting quantum processor

Quantum phase estimation (QPE) is a key quantum algorithm, which has been widely studied as a method to perform chemistry and solid-state calculations on future fault-tolerant quantum computers. Recently, several authors have proposed statistical alternatives to QPE that have benefits on early fault-tolerant devices, including shorter circuits and better suitability for error mitigation techniques. However, practical implementations of the algorithm on real quantum processors are lacking. In this paper we practically implement statistical phase estimation on Rigetti's superconducting processors. We specifically use the method of Lin and Tong [PRX Quantum 3, 010318 (2022)] using the improved Fourier approximation of Wan et al. [PRL 129, 030503 (2022)], and applying a variational compilation technique to reduce circuit depth. We then incorporate error mitigation strategies including zero-noise extrapolation and readout error mitigation with bit-flip averaging. We propose a simple method to estimate energies from the statistical phase estimation data, which is found to improve the accuracy in final energy estimates by one to two orders of magnitude with respect to prior theoretical bounds, reducing the cost to perform accurate phase estimation calculations. We apply these methods to chemistry problems for active spaces up to 4 electrons in 4 orbitals, including the application of a quantum embedding method, and use them to correctly estimate energies within chemical precision. Our work demonstrates that statistical phase estimation has a natural resilience to noise, particularly after mitigating coherent errors, and can achieve far higher accuracy than suggested by previous analysis, demonstrating its potential as a valuable quantum algorithm for early fault-tolerant devices.Comment: 24 pages, 13 figure