The main component of Shor's algorithm, for example, evaluates $f(x) = a^x\operatorname$ can be precomputed and reduced modulo N before running the algorithm. Many quantum algorithms rely on oracles that evaluate mathematical functions on a superposition of inputs. Finally, a quantum machine learning glossary, with the main concepts and ingredients of the training process.
#Maschine library how to#
How to load your own data sets to train a classifier model with the Quantum Development Kit (QDK).How to design your own classifier with Q#, learning the basic concepts behind the design of circuit models for the quantum circuit centric classifier.To do that, you will train a simple sequential model using a classifier structure defined in Q#. A basic classification with QDK, to run quantum sequential classifier written in Q# using the Quantum Machine Learning library of the QDK.An introduction to quantum machine learning concepts, and specifically their realization in quantum circuit centric classifiers (also known as quantum sequential classifiers).The quantum machine learning library documentation provides an implementation of the sequential classifiers that take advantage of quantum computing to understand data. The model implemented in this library is based on the quantum-classical training scheme presented in Circuit-centric quantum classifiers. You can expect low performance compared to current classical machine learning frameworks (remember that everything is running on top of the simulation of a quantum device that is already computationally expensive). Use samples and tutorials to get introduced to the field of quantum machine learning.Load your own data to classify with quantum simulators.The Quantum Machine Learning Library is an API, written in Q#, that gives you the ability to run hybrid quantum/classical machine learning experiments. The quantum chemistry library for the Quantum Development Kit is designed to work well with computational chemistry packages, most notably the NWChem computational chemistry platform developed by the Environmental Molecular Sciences Laboratory (EMSL) at Pacific Northwest National Laboratory.įor more information, see Installation of quantum chemistry library. For more information, see Quantum Chemistry Library. The documentation of the quantum chemistry library for the Quantum Development Kit provides an introduction and several examples to simulating electronic structure problems on quantum computers in order to help the reader understand the role that many aspects of the Hamiltonian simulation library play within the space. The list of applications in this space is vast. It would also allow us to simulate correlated electronic materials such as high-temperature superconductors. Simulating problems in chemistry and material science remains perhaps the most evocative application of quantum computing and would allow us to probe chemical reaction mechanisms that hitherto were beyond our ability to measure or simulate. This is because quantum dynamics are widely believed to be intractable to simulate on classical computers, meaning that the complexity of simulating the system scales exponentially with the size of the quantum system in question. Simulation of physical systems has long played a central role in quantum computing. The functionality of the standard libraries includes classical mathematics, type conversions between various Q# data types, diagnostic of mistakes and errors in quantum programs, quantum error correction, characterization of the effects of operations in order to develop useful quantum algorithms, and many more features.įor more information, see Standard Libraries. The standard libraries provide a set of essential and very useful functions and operations that can be used when writing quantum programs in Q#. The NuGet package installed during installation and validation automatically provides the Q# standard library. The Quantum Development Kit (QDK) provides additional domain-specific functionality through NuGet packages that can be added to your Q# projects.