# Eager refresh of values for AsyncExpiringLazy

Some time ago I blogged about introducing a new library, called AsyncExpiringLazy, which can be used for managing lazy-resolved values that expire and must be refreshed - such as for example access tokens to web APIs.

Yesterday I pushed out a release 2.1.0 of the library, which features a unique new feature - built thanks to the great work of Lukasz - some new additional semantics for the way how the captured value gets refreshed.

# The curious case of ASP.NET Core integration test deadlock

One of the common approaches to testing ASP.NET Core applications is to use the integration testing available via the Microsoft.AspNetCore.TestHost package. In particular, the arguably most common scenario is integration testing of the MVC applications via the Microsoft.AspNetCore.Mvc.Testing, which provides a set of MVC-specific helpers on top of the test host.

In this post I want to share a curious case of deadlocking integration tests in an ASP.NET Core 3.1 application.

# Introduction to quantum computing with Q# – Part 19, Quantum Phase Estimation

Last time we discussed the Quantum Fourier Transform - one of the important building blocks for more complex quantum algorithms. In this post, we will build upon that knowledge and take advantage of the QFT functionality, to explore another important subroutine that is used in many quantum programs, namely quantum phase estimation.

# Introduction to quantum computing with Q# – Part 18, Quantum Fourier Transform

In today’s post we will explore one of the important algorithm building blocks in quantum computing theory, called the Quantum Fourier Transform. It is a quantum variant of the classical Discrete Fourier Transform and is used in a number of algorithms such as Shor’s factoring algorithm, quantum phase estimation or quantum algorithm for linear systems of equations.

# Introduction to quantum computing with Q# – Part 17, Grover’s algorithm

Last time we looked at the basic theory behind quantum search based on the Grover’s algorithm. We went through the most basic case, a data set consisting of four items, and applied the algorithm to that, learning in the process that it managed to find the relevant entry we were looking for in a single step - compared to an average expected 2.25 steps required by the classical computation theory.

In this part, we will look at the more general theory behind Grover’s algorithm, and implement the general Q# variant that can be used to find any number in an arbitrarily large data set.

# Introduction to quantum computing with Q# – Part 16, Quantum search

In this post we will start exploring the topic of quantum search - the ability to locate a specific qubit state in an unsorted data set represented in a qubit register. We will look at the mathematics behind this problem, at the Q# code illustrating some basic examples and explain how the different building blocks fit together. This will help us lay ground for a more comprehensive discussion of the so-called Grover’s algorithm next time.

# Introduction to quantum computing with Q# – Part 15, Deutsch-Jozsa algorithm

Last time, we discussed a problem originally stated by David Deutsch, focusing on determining whether a function is constant or balanced. We found out that for that specific problem, quantum computing provides a much better query complexity than classical computing - as it can solve the task in a single blackbox function evaluation, while classical computing requires two function evaluations to provide the same answer.

Today, we shall look at the generalization of that simple problem.

# Introduction to quantum computing with Q# – Part 14, Deutsch’s problem

Over the course of this series, we have developed a solid foundational understanding of quantum computing, as we learned about the basic paradigms, mathematics and various computational concepts that characterize this unique disciple. We are now well equipped to start exploring some of the most important quantum algorithms - starting with today’s part 14, which will be devoted to a simple oracle problem formulated by David Deutsch.

# Introduction to quantum computing with Q# – Part 13, CHSH Game

Last time we had an in-depth look at the original Bell’s inequality, and we wrote some Q# code that allowed us to quickly empirically test the predictions of quantum mechanics in that area.

In today’s post, we will continue with a generalization of Bell’s inequalities, called Clauser-Horne-Shimony-Holt

inequality (in short CHSH), and discuss a simple game based on that. In the process, we will arrive at a remarkable conclusions - we will learn that for a certain class of simple boolean logic problems, they can be solved more efficiently when adopting a quantum strategy compared to a classical “common sense” approach.

# Introduction to quantum computing with Q# – Part 12, Bell’s inequality

After a short multi-part detour into the world of quantum cryptography, in this part 12 of the series, we are going to return to some of the foundational concepts of quantum mechanics, and look at the programmatic verification of Bell’s inequality.

## About

Hi! I'm **Filip W.**, a cloud architect from Zürich 🇨🇭. I like Toronto Maple Leafs 🇨🇦, Rancid and quantum
computing. Oh, and I love
the Lowlands 🏴.

## Recent Posts

- 2023/01/27, Exploring the new astronomical features of Mathematica 13.2
- 2023/01/23, Simulating Q# programs with QIR runner
- 2022/12/22, dotnet WASI applications in the cloud
- 2022/12/08, Q# Holiday Calendar 2022 – Peeking into Santa’s gifts with Q#
- 2022/11/30, dotnet-script 1.4 is out with .NET 7.0 support

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