qiskit-transpiler-service 0.3.0

A library to use Qiskit Transpiler service(https://docs.quantum.ibm.com/transpile/qiskit-transpiler-service) and the AI transpiler passes(https://docs.quantum.ibm.com/transpile/ai-transpiler-passes)

qiskit_transpiler_service

A library to use Qiskit Transpiler service and the AI transpiler passes.

Note The Qiskit transpiler service and the AI transpiler passes use different experimental services that are only available for IBM Quantum Premium Plan users. This library and the releated services are an alpha release, subject to change.

Installing the qiskit-transpiler-service

To use the Qiskit transpiler service, install the qiskit-transpiler-service package:

pip install qiskit-transpiler-service

By default, the package tries to authenticate to IBM Quantum services with the defined Qiskit API token, and uses your token from the QISKIT_IBM_TOKEN environment variable or from the file ~/.qiskit/qiskit-ibm.json (under the section default-ibm-quantum).

Note: This library requires Qiskit 1.0 by default.

How to use the library

Using the Qiskit Transpiler service

The following examples demonstrate how to transpile circuits using the Qiskit transpiler service with different parameters.

1. Create a circuit and call the Qiskit transpiler service to transpile the circuit with ibm_sherbrooke as the backend_name, 3 as the optimization_level, and not using AI during the transpilation.

   from qiskit.circuit.library import EfficientSU2
   from qiskit_transpiler_service.transpiler_service import TranspilerService
   
   circuit = EfficientSU2(101, entanglement="circular", reps=1).decompose()
   
   cloud_transpiler_service = TranspilerService(
       backend_name="ibm_sherbrooke",
       ai=False,
       optimization_level=3,
   )
   transpiled_circuit = cloud_transpiler_service.run(circuit)
   

Note: you only can use backend_name devices you are allowed to with your IBM Quantum Account. Apart from the backend_name, the TranspilerService also allows coupling_map as parameter.

2. Produce a similar circuit and transpile it, requesting AI transpiling capabilities by setting the flag ai to True:

   from qiskit.circuit.library import EfficientSU2
   from qiskit_transpiler_service.transpiler_service import TranspilerService
   
   circuit = EfficientSU2(101, entanglement="circular", reps=1).decompose()
   
   cloud_transpiler_service = TranspilerService(
       backend_name="ibm_sherbrooke",
       ai=True,
       optimization_level=1,
   )
   transpiled_circuit = cloud_transpiler_service.run(circuit)
   

Using the AIRouting pass manually

The AIRouting pass acts both as a layout stage and a routing stage. It can be used within a PassManager as follows:

from qiskit.transpiler import PassManager
from qiskit_transpiler_service.ai.routing import AIRouting
from qiskit.circuit.library import EfficientSU2

ai_passmanager = PassManager([
  AIRouting(backend_name="ibm_sherbrooke", optimization_level=2, layout_mode="optimize")
])

circuit = EfficientSU2(101, entanglement="circular", reps=1).decompose()

transpiled_circuit = ai_passmanager.run(circuit)

Here, the backend_name determines which backend to route for, the optimization_level (1, 2, or 3) determines the computational effort to spend in the process (higher usually gives better results but takes longer), and the layout_mode specifies how to handle the layout selection. The layout_mode includes the following options:

• keep: This respects the layout set by the previous transpiler passes (or uses the trivial layout if not set). It is typically only used when the circuit must be run on specific qubits of the device. It often produces worse results because it has less room for optimization.
• improve: This uses the layout set by the previous transpiler passes as a starting point. It is useful when you have a good initial guess for the layout; for example, for circuits that are built in a way that approximately follows the device's coupling map. It is also useful if you want to try other specific layout passes combined with the AIRouting pass.
• optimize: This is the default mode. It works best for general circuits where you might not have good layout guesses. This mode ignores previous layout selections.

Using the AI circuit synthesis passes

The AI circuit synthesis passes allow you to optimize pieces of different circuit types (Clifford, Linear Function, Permutation) by re-synthesizing them. The typical way one would use the synthesis pass is the following:

from qiskit.transpiler import PassManager

from qiskit_transpiler_service.ai.routing import AIRouting
from qiskit_transpiler_service.ai.synthesis import AILinearFunctionSynthesis
from qiskit_transpiler_service.ai.collection import CollectLinearFunctions
from qiskit.circuit.library import EfficientSU2

ai_passmanager = PassManager([
  AIRouting(backend_name="ibm_cairo", optimization_level=3, layout_mode="optimize"),  # Route circuit
  CollectLinearFunctions(),  # Collect Linear Function blocks
  AILinearFunctionSynthesis(backend_name="ibm_cairo")  # Re-synthesize Linear Function blocks
])

circuit = EfficientSU2(10, entanglement="full", reps=1).decompose()

transpiled_circuit = ai_passmanager.run(circuit)

The synthesis respects the coupling map of the device: it can be run safely after other routing passes without "messing up" the circuit, so the overall circuit will still follow the device restrictions. By default, the synthesis will replace the original sub-circuit only if the synthesized sub-circuit improves the original (currently only checking cnot count), but this can be forced to always replace the circuit by setting replace_only_if_better=False.

The following synthesis passes are available from qiskit_transpiler_service.ai.synthesis:

• AICliffordSynthesis: Synthesis for Clifford circuits (blocks of H, S and CX gates). Currently up to 9 qubit blocks.
• AILinearFunctionSynthesis: Synthesis for Linear Function circuits (blocks of CX and SWAP gates). Currently up to 9 qubit blocks.
• AIPermutationSynthesis: Synthesis for Permutation circuits (blocks of SWAP gates). Currently available for 65, 33, and 27 qubit blocks.

We expect to gradually increase the size of the supported blocks.

All passes use a thread pool to send several requests in parallel. By default it will use as max threads as number of cores plus four (default values for ThreadPoolExecutor python object). However, you can set your own value with the max_threads argument at pass instantation. For example, the following line will instantiate the AILinearFunctionSynthesis pass allowing it to use a maximum of 20 threads.

AILinearFunctionSynthesis(backend_name="ibm_cairo", max_threads=20)  # Re-synthesize Linear Function blocks using 20 threads max

You can also set the environment variable AI_TRANSPILER_MAX_THREADS to the desired number of maximum threads, and all synthesis passes instantiated after that will use that value.

For sub-circuit to be synthesized by the AI synthesis passes, it must lay on a connected subgraph of the coupling map (this can be ensured by just doing a routing pass previous to collecting the blocks, but this is not the only way to do it). The synthesis passes will automatically check if a the specific subgraph where the sub-circuit lays is supported, and if it is not supported it will raise a warning and just leave the original sub-circuit as it is.

To complement the synthesis passes we also provide custom collection passes for Cliffords, Linear Functions and Permutations that can be imported from qiskit_transpiler_service.ai.collection:

• CollectCliffords: Collects Clifford blocks as Instruction objects and stores the original sub-circuit to compare against it after synthesis.
• CollectLinearFunctions: Collects blocks of SWAP and CX as LinearFunction objects and stores the original sub-circuit to compare against it after synthesis.
• CollectPermutations: Collects blocks of SWAP circuits as Permutations.

These custom collection passes limit the sizes of the collected sub-circuits so that they are supported by the AI synthesis passes, so it is recommended to use them after the routing passes and before the synthesis passes to get a better optimization overall.