For the last post of my quantum programming series, I show you the most famous quantum algorithm, Shor’s algorithm, with Q# programming.
You can also solve integer factorization with polynomial time computation (in the input’s bit size) using this quantum algorithm, though classical one needs exponential time.
In this post, I show you several arithmetic implementation (circuit), such as addition, subtraction, multiplication, and exponentiation, using Quantum Fourier Transform (QFT).
In this post I explain the outline of Quantum Fourier Transform and Quantum Phase Estimation algorithm and see the programming example with Q#.
Q# provides high level operator for both Quantum Fourier Transform and Phase Estimation, but in this post, we implement these algorithms with primitive operators for the purpose of your learning.
In this post, I show you benchmarks for Apache Hive LLAP on Azure HDInsight. You can quickly start and see how LLAP is different with regular Hive (container on Tez) using managed service cluster.
In this post I explain the outline of Grover’s quantum search algorithm and see the programming example with Q#.
You could find it’s effective algorithm compared with classical brute-force search.
Using Azure Machine Learning service, you can train the model on the Spark-based distributed platform (Azure Databricks) and serve your trained model (pipeline) on Azure Container Instance (ACI) or Azure Kubernetes Service (AKS).
In this post, I show you this step and background using AML Python SDK.
In order to solve the real problem with quantum computing, it’s also important to understand algorithms as well as quantum logic gates.
Here I show primitive programming sample to solve some problem for your very beginning and introductions.