Tag: health

JEP 428, Structured Concurrency (Incubator), has been promoted from Proposed to Target to Targeted status for JDK 19. Under the umbrella of Project Loom, this JEP proposes simplifying multithreaded programming by introducing a library to treat multiple tasks running on different threads as an atomic operation. As a result, it will streamline error handling and cancellation, improve reliability, and enhance observability. This is still an incubating API.

This allows developers to organize their concurrency code using the StructuredTaskScope class. It will treat a family of subtasks as a unit. The subtasks will be created on their own threads by forking them individually but then joined as a unit and possibly canceled as a unit; their exceptions or successful results will be aggregated and handled by the parent task. Let’s see an example:

Response handle() throws ExecutionException, InterruptedException 
   try (var scope = new StructuredTaskScope.ShutdownOnFailure()) 
       Future<String> user = scope.fork(() -> findUser());
       Future<Integer> order = scope.fork(() -> fetchOrder());

       scope.join();          // Join both forks
       scope.throwIfFailed(); // ... and propagate errors

       // Here, both forks have succeeded, so compose their results
       return new Response(user.resultNow(), order.resultNow());
   

The above handle() method represents a task in a server application. It handles an incoming request by creating two subtasks. Like ExecutorService.submit(), StructuredTaskScope.fork() takes a Callable and returns a Future. Unlike ExecutorServicethe returned Future is not joined via Future.get(). This API runs on top of JEP 425, Virtual Threads (Preview), also targeted for JDK 19.

The examples above use the StructuredTaskScope API, so to run them on JDK 19, a developer must add the jdk.incubator.concurrent module, as well as enable preview features to use virtual threads:

Compile the above code as shown in the following command:

javac --release 19 --enable-preview --add-modules jdk.incubator.concurrent Main.java

The same flag is also required to run the program:

java --enable-preview --add-modules jdk.incubator.concurrent Main;

However, one can directly run this using the source code launcher. In that case, the command line would be:

java --source 19 --enable-preview --add-modules jdk.incubator.concurrent Main.java

The jshell option is also available, but requires enabling the preview feature as well:

jshell --enable-preview --add-modules jdk.incubator.concurrent

The benefits structured concurrency brings are numerous. It creates a child-parent relationship between the invoker method and its subtasks. For instance, from the example above, the handle() task is a parent and its subtasks, findUser() and fetchOrder(), are children. As a result, the whole block of code becomes atomic. It ensures observability by demonstrating the task hierarchy in the thread dump. It also enables short-circuiting in error handling. If one of the sub-tasks fails, the other tasks will be canceled if not completed. If the parent task’s thread is interrupted before or during the call to join(), both forks will be automatically canceled when the scope exits. These bring clarity to the structure of the concurrent code, and the developer can now reason and follow the code as if they read through as if they are running in a single-threaded environment.

In the early

For would-be quantum programmers scratching their heads over how to jump into the game as quantum computers proliferate and become publicly accessible, a new beginner’s guide provides a thorough introduction to quantum algorithms and their implementation on existing hardware.

“Writing quantum algorithms is radically different from writing classical computing programs and requires some understanding of quantum principles and the mathematics behind them,” said Andrey Y. Lokhov, a scientist at Los Alamos National Laboratory and lead author of the recently published guide in ACM Transactions on Quantum Computing. “Our guide helps quantum programmers get started in the field, which is bound to grow as more and more quantum computers with more and more qubits become commonplace.”

In succinct, stand-alone sections, the guide surveys 20 quantum algorithms—including famous, foundational quantum algorithms, such as Grover’s Algorithm for database searching and much more, and Shor’s Algorithm for factoring integers. Making the real-world connection, the guide then walks programmers through implementing the algorithms on IBM’s publicly available 5-qubit IBMQX4 quantum computer and others. In each case, the authors discuss the results of the implementation and explain the differences between the simulator and the actual hardware runs.

“This article was the result of a rapid-response effort by the Information Science and Technology Institute at Los Alamos, where about 20 Lab staff members self-selected to learn about and implement a standard quantum algorithm on the IBM Q quantum system,” said Stephan Eidenbenz, a senior quantum computing scientist at Los Alamos, a coauthor of the article and director of ISTI when work on it began.

The goal was to prepare the Los Alamos workforce for the quantum era by guiding those staff members with little or no quantum computing experience all the way through the implementation of a quantum algorithm on a real-life quantum computer, Eidenbenz said.

These staff members, in addition to a few students and well-established quantum experts, make up the long author list of this “crowd-sourced” overview article that has already been heavily cited, Eidenbenz said.

The first section of the guide covers the basics of quantum computer programming, explaining qubits and qubit systems, fundamental quantum concepts of superposition and entanglement and quantum measurements before tackling the deeper material of unitary transformations and gates, quantum circuits and quantum algorithms.

The section on the IBM quantum computer covers the set of gates available for algorithms, the actual physical gates implemented, how the qubits are connected and the sources of noise, or errors.

Another section looks at the various types of quantum algorithms. From there, the guide dives into the 20 selected algorithms, with a problem definition, description and steps for implementing each one on the IBM or, in a few cases, other computers.

Extensive references at the end of the guide will help interested readers go deeper in their explorations of quantum algorithms.