Python is a free, easy to use a high-level programming language which has seen a huge expansion in the number of its users and developers in recent years.
GENERAL RELATIVITY AND QUANTUM COSMOLOGY COSMOLOGY AND NONGALACTIC ASTROPHYSICS INSTRUMENTATION AND METHODS FOR ASTROPHYSICS 83-04
The quantum approximate optimization algorithm (QAOA) is widely seen as a possible usage of noisy intermediate-scale quantum (NISQ) devices.
We show that, within our framework, the gradient of an expectation value with respect to a parameterized n-fold fermionic excitation can be evaluated by four expectation values of similar form and size, whereas most standard approaches based on the direct application of the parameter-shift-rule come with an associated cost of O(2^(2n)) expectation values.
QUANTUM PHYSICS CHEMICAL PHYSICS COMPUTATIONAL PHYSICS
In completely generic four-dimensional gauge-Yukawa theories, the renormalization group $ \beta $-functions are known to the 3-2-2 loop order in gauge, Yukawa, and quartic couplings, respectively.
HIGH ENERGY PHYSICS - PHENOMENOLOGY
Numerical weather prediction has traditionally been based on physical models of the atmosphere.
ATMOSPHERIC AND OCEANIC PHYSICS
The use of machine learning methods for accelerating the design of crystalline materials usually requires manually constructed feature vectors or complex transformation of atom coordinates to input the crystal structure, which either constrains the model to certain crystal types or makes it difficult to provide chemical insights.
Ranked #2 on Band Gap on Materials Project
Predicting the properties of a material from the arrangement of its atoms is a fundamental goal in materials science.
MATERIALS SCIENCE DISORDERED SYSTEMS AND NEURAL NETWORKS
Similarly, we show that MEGNet models trained on $\sim 60, 000$ crystals in the Materials Project substantially outperform prior ML models in the prediction of the formation energies, band gaps and elastic moduli of crystals, achieving better than DFT accuracy over a much larger data set.
Ranked #2 on Formation Energy on Materials Project
Our algorithm has a worst case complexity of $O(n \alpha(n))$, where $n$ is the number of physical qubits and $\alpha$ is the inverse of Ackermann's function, which is very slowly growing.