Execute with Fire Opal
Get meaningful results from your algorithms on real quantum computers with Fire Opal's automated error reduction
Submit jobs
Learn how to submit different types of jobs in Fire Opal
Iterative and batch workloads
Understanding how Fire Opal can optimize workloads that require multiple jobs
How to view previous jobs and retrieve results
Viewing metadata and retrieving results from jobs previously submitted to Fire Opal
How to run batch workloads
Running batch workloads with the `iterate` function
Configure circuits
Learn how to start configuring circuits in Fire Opal
Fire Opal circuit requirements
A comprehensive list of circuit requirements for Fire Opal compatibility
Factors that impact Fire Opal's performance improvement
Understanding how to optimize the performance improvement provided by Fire Opal
How to import OpenQASM
Importing OpenQASM from external files to Python
How to create a circuit with multiple registers
Creating and running a circuit with multiple quantum and classical registers with Fire Opal
How to run parameterized quantum circuits
Running parameterized quantum circuits with Fire Opal
Run algorithms
Learn how Fire Opal can run different types of algorithms
Learn to run algorithms using Fire Opal
Create and run Simon's algorithm with Fire Opal
How to run custom variational algorithms
Running variational algorithms with the `iterate` function
Improve the results of quantum phase estimation
Using Fire Opal's error suppression to enhance the performance of quantum phase estimation on real hardware
Fire Opal's QAOA Solver
An overview of Fire Opal's end-to-end managed tool for execution of QAOA algorithms
How to define a QAOA cost function
Defining the input cost function for Fire Opal's QAOA Solver
