Forget about quantum hardware queues with asynchronous results retrieval in Fire Opal

Our mission is to make quantum technology useful, and part of how we achieve this is to improve the performance of quantum hardware so that you can perform more development and testing on real devices.

Even with the best simulators loaded with “realistic” noise models, actual quantum devices provide the best indication of quantum algorithm performance. That’s why we developed Fire Opal, an out-of-the-box solution that automatically reduces error and boosts algorithmic success on real quantum hardware in the NISQ era and beyond. 

And we’re not the only ones to recognize the value of running algorithms on real hardware. High demand for devices creates a new problem: the dreaded queue. Jobs submitted to a hardware backend can sometimes wait hours before they’re executed. So if you’re using a synchronous service, you have to wait…and wait…and wait…otherwise, if the connection is closed, the results are gone.

Have no fear - we’ve got the solution you need to beat the queue! With the new asynchronous jobs retrieval feature you can submit jobs and then come back to get the results anytime after they’ve finished executing. Now with Fire Opal, you can:

1. View a list of previous jobs
2. Use a job's unique ID to retrieve the results.
3. Get a list of job metadata

Check out our user guide to try out the features firsthand!

View previous jobs to track progress

The Activity Monitor is a handy way to view a list of functions that have been run, including status and time of creation. With a simple call to the activity_monitor function, you can access the previous jobs that you’ve created, and you can even filter them by status. With this record of your experiments you can track your own progress and chart a path for your future research.

Retrieve job results when it’s convenient for you

Previously, if your connection to Fire Opal was lost (we’re looking at you, flaky internet dropout), you had no other choice but to obtain measurement results directly from the hardware provider. Those results wouldn’t include the full benefits of Fire Opal’s post-processing, meaning you were forced to leave performance on the table. You had to stay connected and hope for a short queue to minimize the chance of a disconnection.

With this new feature, you can rest assured that you’re always obtaining the best achievable results at any time that works for you! All previous jobs, with Fire Opal’s post processing, are stored and easy to retrieve.

Simply use the get_result function to receive the results with optimization from the entire Fire Opal pipeline.

Retrieve job metadata to gain new insights

You can also access additional information about a job using the get_action_metadata function. This includes fields such as job status, creation time, time of last update, and job ID. With this metadata, you can perform aggregate-level processing and post-analysis across job results, such as understanding the average timeframe for job completion.

Queue time has been a major pain point that we’ve both experienced firsthand and heard from many of our users. We’re thrilled to offer this solution, and we will continue to enhance Fire Opal’s product experience. Your feedback is a huge part of that!

Try it out today and let us know what you think! And if you haven’t already, it’s never too late to get started with Fire Opal.