An Enhanced Hardware Description Language Implementation for Improved Design-Space Exploration in High-Energy Physics Hardware Design

Detectors in High-Energy Physics (HEP) have increased tremendously in accuracy, speed and integration. Consequently HEP experiments are confronted with an immense amount of data to be read out, processed and stored. Originally low-level processing has been accomplished in hardware, while more elaborate algorithms have been executed on large computing farms. Field-Programmable Gate Arrays (FPGAs) meet HEP's need for ever higher real-time processing performance by providing programmable yet fast digital logic resources. With the fast move from HEP Digital Signal Processing (DSPing) applications into the domain of FPGAs, related design tools are crucial to realise the potential performance gains. This work reviews Hardware Description Languages (HDLs) in respect to the special needs present in the HEP digital hardware design process. It is especially concerned with the question, how features outside the scope of mainstream digital hardware design can be implemented efficiently into HDLs. It will argue that functional languages are especially suitable for implementation of domain-specific languages, including HDLs. Casestudies examining the implementation complexity of HEP-specific language extensions to the functional HDCaml HDL will prove the viability of the suggested approach

Quantum Machine Learning in High Energy Physics

Machine learning has been used in high energy physics since a long time, primarily at the analysis level with supervised classification. Quantum computing was postulated in the early 1980s as way to perform computations that would not be tractable with a classical computer. With the advent of noisy intermediate-scale quantum computing devices, more quantum algorithms are being developed with the aim at exploiting the capacity of the hardware for machine learning applications. An interesting question is whether there are ways to combine quantum machine learning with High Energy Physics. This paper reviews the first generation of ideas that use quantum machine learning on problems in high energy physics and provide an outlook on future applications.Comment: 25 pages, 9 figures, submitted to Machine Learning: Science and Technology, Focus on Machine Learning for Fundamental Physics collectio

Non-Markovian Monte Carlo Algorithm for the Constrained Markovian Evolution in QCD

We revisit the challenging problem of finding an efficient Monte Carlo (MC) algorithm solving the constrained evolution equations for the initial-state QCD radiation. The type of the parton (quark, gluon) and the energy fraction x of the parton exiting emission chain (entering hard process) are predefined, i.e. constrained throughout the evolution. Such a constraint is mandatory for any realistic MC for the initial state QCD parton shower. We add one important condition: the MC algorithm must not require the a priori knowledge of the full numerical exact solutions of the evolution equations, as is the case in the popular ``Markovian MC for backward evolution''. Our aim is to find at least one solution of this problem that would function in practice. Finding such a solution seems to be definitely within the reach of the currently available computer CPUs and the sophistication of the modern MC techniques. We describe in this work the first example of an efficient solution of this kind. Its numerical implementation is still restricted to the pure gluon-strahlung. As expected, it is not in the class of the so-called Markovian MCs. For this reason we refer to it as belonging to a class of non-Markovian MCs. We show that numerical results of our new MC algorithm agree very well (to 0.2%) with the results of the other MC program of our own (unconstrained Markovian) and another non-MC program QCDnum16. This provides a proof of the existence of the new class of MC techniques, to be exploited in the precision perturbative QCD calculations for the Large Hadron Collider