The Azure Quantum Resource Estimator
The Azure Quantum Resource Estimator is a resource-estimation tool that computes and displays the resources that are required for a quantum algorithm, assuming that it's executed on a fault-tolerant, large-scale quantum computer.
You can use the Resource Estimator to assess architectural decisions, compare qubit technologies, and determine the resources you need to execute a specific quantum algorithm. You can see the total number of physical qubits, runtime, the required computational resources, and the details of the formulas and values used for each estimate.
In this unit, you'll learn how you can customize the Azure Quantum Resource Estimator to different parameters.
How does the Azure Quantum Resource Estimator work?
The Azure Quantum Resource Estimator takes some target parameters as inputs. The target parameters have predefined values to easily get you started, or you can customize their values. The main target parameters are:
- Physical qubit parameters: The qubit architecture of the quantum computer, that is, the type of qubits.
- Quantum error correction (QEC) scheme: The type of error correction that you want to apply to your quantum algorithm.
- Error budget: The maximum error rate that you allow for your quantum algorithm.
Choose the physical qubit model
The Azure Quantum Resource Estimator has six predefined qubit models. Four of the models have gate-based instruction sets, and two models have Majorana instruction sets. These predefined qubit models represent different qubit architectures like ions or superconductors. The qubit models cover a range of operation times and error rates, so you can use them to explore the required resource costs for practical quantum applications.
| Predefined qubit models | Instruction set type |
|---|---|
"qubit_gate_ns_e3" |
gate-based |
"qubit_gate_ns_e4" |
gate-based |
"qubit_gate_us_e3" |
gate-based |
"qubit_gate_us_e4" |
gate-based |
"qubit_maj_ns_e4" |
Majorana |
"qubit_maj_ns_e6" |
Majorana |
For more information, see Qubit parameters of the Azure Quantum Resource Estimator.
Choose the QEC scheme
Quantum error correction (QEC) is crucial for any quantum-computing platform to achieve truly scalable quantum computation. The set of operations a quantum computing platform permit is limited by physical constraints and might not match the operations prescribed in the algorithm. Even if the operations that the quantum computer offers match the operations in the algorithm, the accuracy to which the quantum computer can perform each operation is likely to be limited.
The Azure Quantum Resource Estimator provides three predefined QEC schemes: two surface code protocols for gate-based and Majorana physical instruction sets, and the Floquet code protocol, which can be used only with a Majorana physical instruction set.
| QEC scheme | Instruction set type |
|---|---|
surface_code |
gate-based and Majorana |
floquet_code |
Majorana |
For more information, see Quantum error correction schemes in the Azure Quantum Resource Estimator.
Choose the error budget
The total error budget sets the overall allowed error for the algorithm. The allowed error is the number of times the algorithm is allowed to fail. The value of the error budget must be between 0 and 1, and the default value is 0.001. The default value corresponds to 0.1 percent, and means that the algorithm is allowed to fail once in 1,000 executions.
The error budget is highly specific to the application. For example, if you're running Shor’s algorithm for factoring integers, a large value for the error budget can be tolerated because you can check that the output is indeed the prime factors of the input. On the other hand, a smaller error budget might be needed for an algorithm solving a problem with a solution that can't be efficiently verified.
For more information, see Error budget in the Azure Quantum Resource Estimator.
In the next unit, you'll get started with the Azure Quantum Resource Estimator and learn how to use it to estimate the Shor's algorithm.