Uh, what? The algorithms are trained on simulations, where you have as many events as the available computing time allows.In other words, this is a situation where a few events must be found inside a very large data set, and the signal looks remarkably similar to the noise. That makes it quite difficult to apply machine learning, let alone train the algorithm in the first place.
That sentence switches from an event-by-event basis (first part) to the overall analysis statement (second part), in the way it is phrased it is very misleading.Just that tiny portion of the data must be refined from a "may contain traces of Higgs" state to a definitive "contains Higgs" or "Higgs free."
It doesn't make any sense to say "two virtual top quarks and a virtual anti-top quark".two gluons collide, and via a love triangle of two virtual top quarks and a virtual anti-top quark
The azimuthal angle (##\varphi##) is measured as well, otherwise you couldn't reconstruct the mass.The detector only registers the following: the energy of the photons and their angle with respect to the beam. From that, you can figure out how much momentum, in the direction perpendicular to the beam, the photons have (called transverse momentum).
What do you mean by "should be"? ATLAS and CMS have used these algorithms for years.So far so good. But there are a bunch of non-quantum machine learning algorithms that should be able to do the same.
That's what ATLAS and CMS did already.The researchers chose several of them and set them loose on the data.
Fine... but no one will use a training dataset of just 200 collisions.or algorithms trained on around 200 collisions, the quantum algorithm significantly outperforms the classical algorithms.
In other words, using the quantum computer made it worse.It is worth noting that the Higgs can be found in the two photon decay mode without any machine learning. Machine learning just improves the sensitivity. The Higgs discovery was announced early July 2012, without machine learning it might have been late July 2012 or August 2012, probably not later. Not as important as the article might suggest. There are other studies where it is more important.On the other hand, the quantum algorithm is significantly worse than the classical algorithms after training on large data sets. This, however, is probably a product of the performance of the underlying hardware rather than the actual algorithm.
Quantum machine learning is a field that combines principles of quantum mechanics and machine learning to develop algorithms and techniques for processing and analyzing large sets of data.
The "Higgs in the Haystack" problem refers to the challenge of identifying the elusive Higgs boson particle in large amounts of data collected from particle accelerator experiments. This particle was hypothesized to exist in the 1960s and was finally discovered in 2012.
Quantum machine learning uses the power of quantum computing to process and analyze large amounts of data simultaneously, allowing for more efficient and accurate identification of the Higgs boson particle.
Quantum machine learning has the potential to revolutionize various industries, including finance, drug discovery, and artificial intelligence, by enabling faster and more accurate data analysis and prediction.
One major limitation of quantum machine learning is the current lack of practical quantum computers. These machines are still in the early stages of development and are not yet widely available for use. Additionally, quantum algorithms are still being developed and optimized, so their performance may not always be superior to classical algorithms.