View backend details

qiskit-ibm-runtime~=0.43.1

This page explains how to find information about your available backends.

List or filter backends

List backends available to you

You can use either Qiskit or IBM Quantum Platform to view a list of backends available to you, or to search for a particular backend.

# Initialize your account
from qiskit_ibm_runtime import QiskitRuntimeService

service = QiskitRuntimeService()

service.backends()

[<IBMBackend('ibm_fez')>,
 <IBMBackend('ibm_torino')>,
 <IBMBackend('ibm_marrakesh')>]

service.backend("ibm_fez")

<IBMBackend('ibm_fez')>

Filter backends

# Optionally pass in an instance, region, or both, to
# further filter the backends.
service = QiskitRuntimeService()

service.backends(simulator=False, operational=True, min_num_qubits=100)

[<IBMBackend('ibm_fez')>,
 <IBMBackend('ibm_torino')>,
 <IBMBackend('ibm_marrakesh')>]

service.least_busy(operational=True, min_num_qubits=100)

<IBMBackend('ibm_fez')>

Static backend information

Some information about a backend does not change regularly, such as its name, version, the number of qubits it has, its processor type (the bird family name, which indicates the topology and approximate qubit count), and the types of features it supports. This information is available as attributes of the backend object. For a full list of attributes, see the IBMBackend API documentation. Find more information on versioning in the QPU versioning section below.

A backend's region (the location of the data center where your data and experiments will be hosted and processed) is listed in its detailed information card on the Compute resources page on IBM Quantum Platform.

backend = service.backend("ibm_fez")

print(
    f"Name: {backend.name}\n"
    f"Version: {backend.backend_version}\n"
    f"No. of qubits: {backend.num_qubits}\n"
    f"Processor type: {backend.processor_type}\n"
)

Name: ibm_fez
Version: 1.3.37
No. of qubits: 156
Processor type: {'family': 'Heron', 'revision': '2'}

QPU versioning

Each QPU has a version number in the form X.Y.Z (major.minor.revision). A circuit compiled for a given version number is guaranteed to run on that QPU. If the revision number changes, the circuit will continue to run. If the major or minor number changes, the circuit is not guaranteed to run, although it may do so.

The revision version number will increment for fixes that do not break the existing compiled circuit.

The code example earlier in this section demonstrates how to find a backend's version. On IBM Quantum Platform, you can view the version on a QPU's detailed information card (click the QPU's name on the Compute resources table to open the card).

The conditions under which a version or revision number can change are listed in the following table.

Supported instructions

Each processor family natively supports a limited set of instructions. This set includes single- and two-qubit gates, as well as non-unitary operations such as measure and reset. Every gate in the circuit must be translated (by the transpiler) to the elements of a QPU's supported instruction set before it can run on the QPU.

You can view the supported instruction sets for a QPU with Qiskit. The IBM Quantum Platform Compute resources page lists only the supported unitary gates (basis gates) for a QPU.

from qiskit_ibm_runtime import QiskitRuntimeService

service = QiskitRuntimeService()
backend = service.backend("ibm_kingston")

print(f"Backend: {backend.name}")
print(f"    Processor type: {backend.processor_type}")
print(f"    Supported instructions: {backend.supported_instructions}")

Backend: ibm_kingston
    Processor type: {'family': 'Heron', 'revision': '2'}
    Supported instructions: ['cz', 'id', 'delay', 'measure', 'measure_2', 'reset', 'rz', 'sx', 'x', 'if_else']

Table of supported instructions

Dynamic backend information

Backends also have properties that change whenever the backend is calibrated, such as qubit lifetime and operation error rates. Backend properties update after the calibration sequence completes. These properties can be used when optimizing quantum circuits or to construct noise models for a classical simulator. See the Calibration jobs guide for more information.

On IBM Quantum Platform, you can download calibration data as a CSV file. Click a QPU on the Compute resources page to view its detailed information card, then click the download icon in the upper right corner of the Calibration data section.

backend.properties(datetime=<datetime>)

Qubit properties

Jump to the list of qubit properties

backend.properties().qubit_property() returns information about the qubits' physical attributes. It contains a dictionary of various properties of the qubit, each paired with its value and the timestamp of the last calibration.

The following code examples demonstrate how to list all properties, or retrieve specific properties, of a particular qubit.

# fundamental physical properties of qubit 1

backend = service.backend("ibm_fez")

backend.qubit_properties(1)

Output:

QubitProperties(t1=0.00020833187791710663, t2=0.00023395229316289175)

# Retrieve qubit properties
qubit_index = 126  # Replace with your qubit index
qubit_props = backend.properties().qubit_property(qubit_index)

# Access specific properties
t1 = qubit_props.get("T1", (None,))[0]
t2 = qubit_props.get("T2", (None,))[0]
readout_error = qubit_props.get("readout_error", (None,))[0]
prob_meas0_prep1 = qubit_props.get("prob_meas0_prep1", (None,))[0]
prob_meas1_prep0 = qubit_props.get("prob_meas1_prep0", (None,))[0]
readout_length = qubit_props.get("readout_length", (None,))[0]

print(f"Qubit {qubit_index} Properties:")
print(f"  T1: {t1} seconds")
print(f"  T2: {t2} seconds")
print(f"  Readout Error: {readout_error}")
print(f"  P(0 | 1): {prob_meas0_prep1}")
print(f"  P(1 | 0): {prob_meas1_prep0}")
print(f"  Readout Length: {readout_length} seconds")

Output:

Qubit 126 Properties:
  T1: 0.00011004519582612597 seconds
  T2: 6.519695327562977e-05 seconds
  Readout Error: 0.003662109375
  P(0 | 1): 0.0048828125
  P(1 | 0): 0.00244140625
  Readout Length: 1.56e-06 seconds

View qubit properties

• T1 (Relaxation time)

  The $T_1$ time represents the average duration a qubit remains in its excited state $|1\rangle$ before decaying to its ground state $|0\rangle$ due to energy relaxation. This parameter is used to characterize the qubit's energy relaxation behavior, and is expressed in units of seconds (s).

  View with Qiskit	backend.properties().t1(<qubit>)
  View on IBM Quantum Platform	Calibration data section, Qubit dropdown menu; find the $T_1$ median value in the Details section

• T2 (Dephasing time)

  The $T_2$ time denotes the timescale over which a qubit maintains phase coherence of a superposition between the $|0\rangle$ and $|1\rangle$ states. It accounts for both energy relaxation and pure dephasing processes, providing insight into the qubit's coherence properties. $T_2$ is reported from a Hahn echo sequence.

  View with Qiskit	backend.properties().t2(<qubit>)
  View on IBM Quantum Platform	Calibration data section, Qubit dropdown menu; find the $T_2$ median value in the Details section

Instruction properties

Jump to the list of instruction properties

The backend.target attribute is a qiskit.transpiler.Target object: an object that contains all the information needed to transpile a circuit for that backend. This includes instruction errors and durations. For example, the following cell gets the properties for a cz gate acting between qubits 1 and 0.

backend.target["cz"][(1, 0)]

Output:

InstructionProperties(duration=6.8e-08, error=0.007854516178238763)

The following cell shows the properties for a measurement operation (including the readout error) on qubit 0.

backend.target["measure"][(0,)]

Output:

InstructionProperties(duration=1.56e-06, error=0.01416015625)

View instruction properties

• prob-meas0-prep1

  This parameter indicates the probability of measuring a qubit in the 0 state when it was intended to be prepared in the $|1\rangle$ state, denoted as $P(0 | 1)$. It reflects errors in state preparation and measurement (SPAM), particularly measurement errors in superconducting qubits.

  View with Qiskit	backend.properties().qubit_property(<qubit>, 'prob_meas0_prep1')
  View on IBM Quantum Platform	Calibration data section, Qubit dropdown menu

• prob-meas1-prep0

  Similarly, this parameter represents the probability of measuring a qubit in the 1 state when it was intended to be prepared in the $|0\rangle$ state, denoted as $P(1 | 0)$. Like prob_meas0_prep1, it reflects SPAM errors, with measurement errors being the predominant contributor in superconducting qubits.

  View with Qiskit	backend.properties().qubit_property(<qubit>, 'prob_meas0_prep0')
  View on IBM Quantum Platform	Calibration data section, Qubit dropdown menu

• 2Q error (Heron and Nighthawk: CZ, Eagle: ECR)

  The two-qubit error per edge from the same batch of measurements used to calculate the 2Q median error. 2Q error (best) refers to the lowest two-qubit error on any edge of the device, also from this batch of measurements.

  View with Qiskit	backend.target['<instruction>'][<qubit 1>, <qubit 2>]
  View on IBM Quantum Platform	Calibration section: hover over the qubit connection in Map view, or find the value in Table view under the CZ error (Heron and Nighthawk) or ECR error (Eagle) column; find the value for 2Q error (best) in the Details section

• Median 2Q error (Heron: CZ, Eagle: ECR)

  Average gate fidelity of the two-qubit operation from randomized benchmarking. Measured in "isolation": batches with a minimum separation of two qubits between edges. This randomized benchmarking uses alternating layers of single-qubit Cliffords and two-qubit gates, and thus the final 2Q error value includes the error of the layer of single-qubit Cliffords.

  Calculate with Qiskit	Follow the example in this Qiskit Community GitHub notebook
  View on IBM Quantum Platform	Details section; also find per-edge data in the Calibration data section

• 2Q error (layered)

  Average error per layered gate (EPLG) in a chain of 100 qubits. Average EPLG measures the average gate error in a layered chain of $N$ qubits ($N$=100 here). It is derived from a similar quantity known as the layer fidelity (LF) where EPLG$_{100}$ = 4/5(1-LF$^{\frac{1}{99}}$) and layer fidelity is the process fidelity of the layered chain of $N$ qubits. For details, see the paper Benchmarking quantum processor performance at scale. Note that in the paper EPLG is defined for process error, but for consistency with the individually reported gate errors here it is quoted for average gate error, thus the factor of 4/5.

  On IBM Quantum Platform, the detailed information card for each QPU has a section called Two-qubit gate error (layered), which provides the expanded view of the lowest two-qubit gate error (layered) measured as a function of the number of qubits in the chain. The final value, at chain length 100, is the value presented in the Details section. In practice, six 100-qubit chains (pre-selected based on expected optimal performance) are measured, and the value reported for number of qubits N is the lowest error found in a subset length N chain searching over the six 100-qubit chains.

  Calculate with Qiskit	Follow the example in this Qiskit Community GitHub notebook
  View on IBM Quantum Platform	Details section, and an expanded view in the Two-qubit gate error (layered) section

• RZZ error (Heron)

  Error in the RZZ gate averaged over the RZZ angles using a variant of randomized benchmarking for arbitrary unitaries.

  View with Qiskit	Important: Be sure you have set use_fractional_gates=True when you load the backend, then you can use backend.target['rzz'][<qubit 1>, <qubit 2>]
  View on IBM Quantum Platform	Calibration section: Select RZZ in the Connection dropdown menu and hover over the qubit connection in Map view. You can also select RZZ error in the Graph output dropdown menu in Graph view, or find the value in Table view under the RZZ error column

• ID error / √x (sx) error / Pauli-X error / RX error

  Error in the finite-duration discrete one-qubit gates, measured from randomized benchmarking. The randomized benchmarking sequence includes SX, ID, and X gates, and it is assumed their errors are the same. The ID gate is a delay of duration equal to the duration of the √X and X gates. The RX gate is also the same duration as the √X and X gates with variable amplitude, and so it is reported as having the same error as these gates.

  View with Qiskit	backend.target['<instruction>'][<qubit 1>, ]
  View on IBM Quantum Platform	Calibration section: Qubit dropdown menu

• SX error (median)

  Average gate fidelity of the √X (SX) gate from randomized benchmarking, measured simultaneously on all qubits. The randomized benchmarking sequence includes SX, ID, and X gates, and it is assumed their errors are the same.

  View on IBM Quantum Platform	Details section

• Z-axis rotation (RZ) error

  Error in the virtual RZ gate. Reported as all 0 since these are performed in software.

  View with Qiskit	backend.target['<instruction>'][<qubit 1>, ]
  View on IBM Quantum Platform	Calibration section: Connection dropdown menu

• Readout error

  The readout error quantifies the average probability of incorrectly measuring a qubit's state. It is commonly calculated as the mean of prob_meas0_prep1 and prob_meas1_prep0, providing a single metric for measurement fidelity.

  View with Qiskit	backend.properties().readout_error(<qubit>)
  View on IBM Quantum Platform	Calibration data section, Qubit dropdown menu

• Readout error (median)

  Fidelity of the readout operation. Readout error is measured by preparing the qubit in the 0 (1) state and measuring the probability of an output in the 1 (0) state. The reported value is the average of these two errors. The median is taken over all qubits.

  View on IBM Quantum Platform	Calibration data section, Details section

• Single-qubit gate length

  Duration of a single-qubit gate operation. Note that values shown on IBM Quantum Platform are in nanoseconds. Values returned in Qiskit are in seconds.

  View with Qiskit	backend.target['<instruction>'][<qubit 1>, ].duration
  View on IBM Quantum Platform	Calibration section: Qubit dropdown menu

• Gate length (2Q gates)

  Duration of the two-qubit gate operation. Note that values shown on IBM Quantum Platform are in nanoseconds. Values returned in Qiskit are in seconds.

  View with Qiskit	backend.target['<instruction>'][<qubit 1>, <qubit 2> ].duration
  View on IBM Quantum Platform	Calibration section: Qubit dropdown menu

• Readout length

  The readout length specifies the duration of the readout operation for a qubit. It measures the time from the initiation of the measurement pulse to the completion of signal digitization, after which the system is ready for the next operation. Understanding this parameter is crucial for optimizing circuit execution, especially when incorporating mid-circuit measurements.

  View with Qiskit	For measure: backend.properties().readout_length(<qubit>) For measure_2: backend.target['measure_2'][<qubit 1>, ].duration
  View on IBM Quantum Platform	Calibration data section, Qubit dropdown menu

Additional properties

• CLOPS (or CLOPS_h)

  Circuit layer operations per second (CLOPS) is a measure of how many layers of a 100x100 circuit (hardware-aware circuit) a QPU can execute per unit of time.

  Calculate with Qiskit	Find the CLOPS code in the Qiskit Community GitHub
  View on IBM Quantum Platform	Details section

• Status

  With BackendStatus, you can find the QPU status (for example, Active, Paused, Offline) as well as the number of pending jobs.

  View with Qiskit	print(backend.status().status_msg), print(backend.status().pending_jobs)
  View on IBM Quantum Platform	Details section

• Topology diagram or coupling map

  A diagram that indicates the pairs of qubits that support two-qubit gate operations between them. This is also called the coupling map or connectivity. Qubits are represented as circles and the supported two-qubit gate operations are displayed as lines connecting the qubits.

  View with Qiskit	from qiskit.visualization import plot_gate_map then plot_gate_map(backend)
  View on IBM Quantum Platform	Calibration data section; Click on Expand for a larger view

Next steps