View backend details
Package versions
The code on this page was developed using the following requirements. We recommend using these versions or newer.
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.
Use the QiskitRuntimeService.backends() method, as shown in the next code block. This method returns a list of IBMBackend instances.
To run the following code, be sure you have already authenticated to the service. See Set up your IBM Cloud account for more details.
# Initialize your account
from qiskit_ibm_runtime import QiskitRuntimeService
service = QiskitRuntimeService()
service.backends()Output:
[<IBMBackend('ibm_fez')>,
<IBMBackend('ibm_torino')>,
<IBMBackend('ibm_marrakesh')>]
To search for a specific backend, use the QiskitRuntimeService.backend() method (note that this is singular: backend), which takes the name of the backend as the input parameter and returns an IBMBackend instance representing that particular backend:
service.backend("ibm_fez")Output:
<IBMBackend('ibm_fez')>
To view the backends you have access to, navigate to the list of backends on the Compute resources page (note that your selected region might impact the QPUs listed). Click the filter icon and choose either "All my instances" or a specific instance name to see the available QPUs.
If you are logged in to a specific instance or region, or if you initialized the service with a specific instance or region by using QiskitRuntimeService(), only the backends available to you on that instance or region are returned.
Filter backends
You can filter the available backends by their configuration or status. For more general filters, set the filters argument to a function that accepts a backend object and returns True if it meets your criteria. Refer to the API documentation for more details.
The following code returns only backends that fit these criteria and are available to you on your currently selected instance:
- Are real quantum devices (
simulator=False) - Are currently operational (
operational=True) - Have at least a hundred qubits (
min_num_qubits=100)
# 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)Output:
[<IBMBackend('ibm_fez')>,
<IBMBackend('ibm_torino')>,
<IBMBackend('ibm_marrakesh')>]
A similar method is QiskitRuntimeService.least_busy(), which takes the same filters as backends() but returns the backend that matches the filters and has the least number of jobs pending in the queue:
service.least_busy(operational=True, min_num_qubits=100)Output:
<IBMBackend('ibm_fez')>
To view the backends you have access to, view the table on the Compute resources page. Click the filter icon to see filter options. You can filter by processor type and status. Note also that you can sort the table by any column by hovering over a column title, and then clicking the arrows that appear.
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.
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"
)Output:
Name: ibm_fez
Version: 2
No. of qubits: 156
Processor type: {'family': 'Heron', 'revision': '2'}
Navigate to the Compute resources page to view a table of all QPUs, then click the name of any QPU to open its detailed information card. Find backend information under the Details section of the card.
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 conditions under which a version or revision number can change are listed in the following table.
| Major version | Minor version | Revision version |
|---|---|---|
| Sample changes | Warmup / cool-down cycles | QPU software updates |
| Major changes to the control electronics | Swapping out some electronics, if the replacement appreciably affects operation | Manual calibrations to improve fidelities |
| Moving the QPU to a new location, if significant behavior changes result | Dropping a gate for some duration of time due to calibration issues, and corrections cannot readily be done in software | Small electronics changes that don’t affect operation |
| Changing the direction of a CNOT gate |
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}")Output:
Backend: ibm_kingston
Processor type: {'family': 'Heron', 'revision': '2'}
Supported instructions: ['cz', 'id', 'delay', 'measure', 'measure_2', 'reset', 'rz', 'sx', 'x', 'if_else']
Navigate to the Compute resources page to view a table of all QPUs, then click the name of any QPU to open its detailed information card. The supported instructions for that QPU are listed in the Details section.
Table of supported instructions
| Operation category | Name |
|---|---|
| Single-qubit gates | RZ, SX, X, ID, delay |
| Two-qubit gates | CZ, ECR |
| Fractional gates | RX (single-qubit), RZZ (two-qubit) |
| Non-unitary instructions | measure, reset |
| Control flow | if_else (classical feedforward) |
Dynamic backend information
Backends also have properties that change whenever the backed is calibrated, such as qubit frequency 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.
Qubit properties
Jump to a qubit property: T1 (Relaxation time) | T2 (Dephasing time) | frequency | anharmonicity | readout_error
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, frequency=None)
# calibration data with detailed properties of qubit 0
backend.properties().qubit_property(0)Output:
{'T1': (5.345445238259663e-05,
datetime.datetime(2025, 11, 25, 6, 52, 23, tzinfo=tzlocal())),
'T2': (4.569656984603511e-05,
datetime.datetime(2025, 11, 25, 6, 53, 9, tzinfo=tzlocal())),
'readout_error': (0.01416015625,
datetime.datetime(2025, 11, 25, 11, 42, 11, tzinfo=tzlocal())),
'prob_meas0_prep1': (0.02734375,
datetime.datetime(2025, 11, 25, 11, 42, 11, tzinfo=tzlocal())),
'prob_meas1_prep0': (0.0009765625,
datetime.datetime(2025, 11, 25, 11, 42, 11, tzinfo=tzlocal())),
'readout_length': (1.56e-06,
datetime.datetime(2025, 11, 25, 11, 42, 11, tzinfo=tzlocal()))}
# 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]
frequency = qubit_props.get("frequency", (None,))[0]
anharmonicity = qubit_props.get("anharmonicity", (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" Frequency: {frequency} Hz")
print(f" Anharmonicity: {anharmonicity} Hz")
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
Frequency: None Hz
Anharmonicity: None Hz
Readout Error: 0.003662109375
P(0 | 1): 0.0048828125
P(1 | 0): 0.00244140625
Readout Length: 1.56e-06 seconds
Click to view qubit properties
T1 (Relaxation time)
The time represents the average duration a qubit remains in its excited state before decaying to its ground state 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 |
T2 (Dephasing time)
The time denotes the timescale over which a qubit maintains phase coherence of a superposition between the and states. It accounts for both energy relaxation and pure dephasing processes, providing insight into the qubit's coherence properties.
| View with Qiskit | backend.properties().t2(<qubit>) |
| View on IBM Quantum Platform | Calibration data section, Qubit dropdown menu |
frequency
This parameter specifies the resonant frequency of the qubit, indicating the energy difference between the and states, expressed in hertz (Hz).
| View with Qiskit | backend.properties().frequency(<qubit>) |
| View on IBM Quantum Platform | n/a |
anharmonicity
Anharmonicity is the difference in energy between the first and second excited states of the qubit, also expressed in hertz (Hz).
| View with Qiskit | backend.properties().qubit_property(<qubit>, 'anharmonicity') |
| View on IBM Quantum Platform | n/a |
readout_error
The readout assignment 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 |
Instruction properties
Jump to an instruction property: prob_meas0_prep1 | prob_meas1_prep0 | readout_length | 2Q error | 2Q error (layered) | Median 2Q error | Median SX error | Median readout error | Median T1 (relaxation time) | Median T2 (dephasing time) | ID error / √x (sx) error / Pauli-X error / RX error | Single-qubit gate length (ns) | Z-axis rotation (RZ) error | Gate length (ns) | 2Q error | RZZ error
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)
Click to 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 state, denoted as . 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 state, denoted as . 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 |
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 | backend.properties().readout_length(<qubit>) |
| View on IBM Quantum Platform | Calibration data section, Qubit dropdown menu |
2Q error (Heron: CZ, Eagle: ECR)
The two-qubit error per edge from the same batch of measurements used to calculate the 2Q median and 2Q best errors.
| 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) or ECR error (Eagle) column |
Click to view additional properties
Additional properties
CLOPS (or CLOPS_h) - A Status - A Total pending jobs - A Topology diagram or coupling map A
Find QPU details on IBM Quantum Platform
In addition to finding details programmatically, you can also use IBM Quantum Platform to view information about specific QPUs. Navigate to the Compute resources page to view a table of all QPUs, then click the name of any QPU to open its detailed information card. The card contains details such as error rates, calibration data, and more.
Details section
The first section of the QPU information card provides the following QPU details:
Name | Qubits | 2Q error (best) | 2Q error (layered) | CLOPS (or CLOPS_h) | Status | Region | QPU version | Processor type | Basis gates | Total pending jobs | Median 2Q error | Median SX error | Median readout error | Median T1 (relaxation time) | Median T2 (dephasing time)
Click to view details
Name
The unique name assigned to a specific QPU. QPUs hosted on IBM Cloud® have names that begin with ibm_*. All QPUs are given a city name, e.g., ibm_kingston. This name does not indicate where the actual QPU is hosted. They are named after IBM® locations around the world.
Qubits
The number of physical qubits in a QPU.
2Q error (best)
The lowest two-qubit (2Q) error on any edge of the device from the same batch of measurements used to calculate the median (see Median 2Q error).
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 qubits (=100 here). It is derived from a similar quantity known as the layer fidelity (LF) where EPLG = 4/5(1-LF) and layer fidelity is the process fidelity of the layered chain of 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. Find an example notebook in the Qiskit Community GitHub.
CLOPS (or CLOPS_h)
Circuit layer operations per second, is a measure of how many layers of a 100x100 circuit (hardware-aware circuit) a QPU (quantum processing unit) can execute per unit of time. Find the CLOPS code in the Qiskit Community GitHub.
Status
The QPU status; for example, Online, Paused, Offline, and so on.
Region
Location of the data center where your data and experiments will be hosted and processed.
QPU version
The version number of a QPU in the form major.minor.revision. See QPU versioning for details on how this number is assigned.
Processor type
Reflects the topology and indicates the approximate qubit count.
Basis gates
Each processor family has a native gate set. By default, the QPUs in each family only support running the gates and operations in the native gate set. Thus, every gate in the circuit must be translated (by the transpiler) to the elements of this set. Note that the non-unitary operations are not listed here; use the method in Qiskit to see all native gates and operations for a QPU. See a list of all native gates in this table.
Total pending jobs
The total number of jobs that you have submitted to this QPU.
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. Find an example notebook in the Qiskit Community GitHub. Find per-edge data in the calibration data section of the QPU information card.
Median SX error
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.
Median readout error
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.
Median T1 (relaxation time)
The time represents the average duration a qubit remains in its excited state before decaying to its ground state due to energy relaxation. This parameter is used to characterize the qubit's energy relaxation behavior, and is expressed in units of seconds (s).
Median T2 (dephasing time)
The time denotes the timescale over which a qubit maintains phase coherence of a superposition between the and states. It accounts for both energy relaxation and pure dephasing processes, providing insight into the qubit's coherence properties. is reported from a Hahn echo sequence.
Calibration data section
If the benchmarking of a qubit or edge does not succeed over the course of several days, whether due to poor data quality or other internal factors, the reported error value is considered stale and will be reported as 1. This is not an indication that the qubit or edge is necessarily non-working or that the error is 1; rather, the error is considered undefined and you should proceed with caution when operating that qubit or gate.
The second section, Calibration data, provides qubit, connectivity, and gate data. You can choose to visualize the information as a map, graph, or table.
You can customize the data that is shown in each view, using the drop-down menus. For example, in the map view, you can choose the data you want to see for qubits and connections. The colored bars associated with the diagram or graph indicate the range that is shown, with the average value marked. The color maximum and minimum change depending on the QPU.
To download calibration data as a CSV file, click the download icon in the upper right corner of the Calibration data section.
The Calibration data section includes the following:
Topology diagram or coupling map | Readout assignment error | Prob meas0 prep1 | Prob meas1 prep0 | Readout length (ns) | ID error / √x (sx) error / Pauli-X error / RX error | Single-qubit gate length (ns)| Z-axis rotation (RZ) error | Operational | Gate length (ns) | 2Q error | RZZ error
Click to view calibration details
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.
Readout assignment 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.
Prob meas0 prep1
This parameter indicates the probability of measuring a qubit in the state when it was intended to be prepared in the state, denoted as . It reflects errors in state preparation and measurement (SPAM), particularly measurement errors in superconducting qubits.
Prob meas1 prep0
Similarly, this parameter represents the probability of measuring a qubit in the state when it was intended to be prepared in the state, denoted as . Like prob_meas0_prep1, it reflects SPAM errors, with measurement errors being the predominant contributor in superconducting qubits.
Readout length (ns)
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.
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.
Single-qubit gate length (ns)
Duration of a single-qubit gate operation.
Z-axis rotation (RZ) error
Error in the virtual RZ gate. Reported as all 0 since these are performed in software.
Operational
Indicates whether the qubit can be utilized in circuits.
Gate length (ns)
Duration of the two-qubit gate operation.
2Q error (Heron: CZ, Eagle: ECR)
The 2Q error per edge from the same batch of measurements used to calculate the 2Q median and 2Q best errors.
RZZ error (Heron)
Error in the RZZ gate averaged over the RZZ angles using a variant of randomized benchmarking for arbitrary unitaries.
Two-qubit gate error (layered) section
The third section 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.
Next steps
- Try the Grover's algorithm tutorial.
- Review the QiskitRuntime backend API reference.