Circuit Analytics

Quantum circuit analysis is important for understanding the structure, complexity, and feasibility of executing a circuit on quantum hardware or simulators. It helps to identify key properties such as gate composition, depth, connectivity, and noise susceptibility, which impact performance and accuracy.

Analytics are computed cloud-side, which makes them the only way to measure circuits containing opaque Haiqu-generated gates — data-loading results and compression CircuitModel outputs — where local circuit.depth() and gate counts are misleading. See Working with Haiqu Circuits.

Haiqu SDK provides a number of analytics functions, properties and widgets:

Property or Function	Description
`haiqu_circuit.analytics`	Provides ALL raw metrics data, represented as Python objects.
`haiqu_circuit.core_metrics()`	Renders a widget that displays a table of core analytics metrics.
`haiqu_circuit.compare_metrics()`	Renders a widget with a comparison table of core analytics for provided circuits.

Each analytics widget includes built-in help for the displayed metrics, turned on with help=True option. Whenever possible, the widget displays a column to provide assistance for each metric. Data can be exported in formats including JSON, CSV, or used in Pandas for further analysis. Let’s start by defining an example quantum circuit, then display it:

from qiskit.circuit.random import random_circuit

# Generate a random quantum circuit with the following parameters:
circuit = random_circuit(
    num_qubits=16,   # Total number of qubits in the circuit (16-qubit system)
    depth=4,         # The number of gate layers applied
    max_operands=4,  # Maximum number of qubits any multi-qubit gate can act on
    seed=42,         # Random seed for reproducibility (ensures same circuit structure on each run)
    measure=False    # Do not include measurement operations at the end of the circuit
)

# Assign a name to the circuit for easy identification
circuit.name = "Haiqu 16-qubit hello world"

# Display the generated circuit
haiqu.draw(circuit)

To utilize the analytics functions invoke the haiqu.log() method. This computes the complete set of quantum metrics using the Haiqu API and returns them in a single metadata object. Let’s submit our circuit to the Haiqu quantum backend for analysis:

haiqu_circuit = haiqu.log(circuit)

Basic analysis

Before executing a quantum circuit, it is crucial to assess its fundamental properties to ensure feasibility and compatibility with available simulators or hardware. This step helps determine circuit size, gate composition, and whether it contains custom or unsupported operations. By analyzing these factors, users can make informed decisions about device selection, potential optimizations, and whether additional modifications are needed before execution. The circuit’s basic metrics are displayed using core_metrics:

haiqu_circuit.core_metrics(help=True)

To use this function in a terminal or a script and get metrics as a dictionary, use it with the widget=False parameter:

haiqu_circuit.core_metrics(widget=False)

>>> {'qubits': 8,
 'num_parameters': 0,
 'depth': 5,
 'depth_2q': 4,
 'gates_1q': 1,
 'gates_2q': 7,
 'other_gates': 8,
 'gates_total': 16,
 'other_ops': 8,
 'instructions_total': 24}

Accessing metrics directly

Let’s submit the example quantum circuit for analysis:

from qiskit.circuit.random import random_circuit
qc = random_circuit(num_qubits=8, depth=5, max_operands=4, measure=True)
haiqu_circuit = haiqu.log(qc, name="Example Circuit")

Now the metrics could be accessed as regular Python objects. To access the number of qubits, use:

haiqu_circuit.analytics.qubits

>>> 8

To access the depth of circuit, use:

haiqu_circuit.analytics.depth

>>> 5

And so on.

Display a breakdown of the different gate types present in the circuit

To access the number of operations with the details for each of the gate type, use:

haiqu_circuit.analytics.operations_counts

>>> {'measure': 8,
 'cswap': 4,
 'rccx': 2,
 'sdg': 2,
 'z': 2,
 's': 1,
 'dcx': 1,
 'u3': 1,
 'cu': 1,
 'rxx': 1,
 'x': 1,
 'rzz': 1,
 'cp': 1,
 'p': 1,
 'xx_plus_yy': 1,
 'sx': 1}

Comparing circuits

The function haiqu.compare_metrics shows a table with the core metrics of several circuits. This can be useful to understand the difference between the circuits or how a circuit changes with respect to a parameter.

haiqu.compare_metrics(circuit1, circuit2, ...)

For instance, the following example compares two circuits obtained as a transpilation of the same initial circuit but with different options:

transpiled_circuit_opt0 = haiqu.transpile(circuit, device, optimization_level=0)
transpiled_circuit_opt3 = haiqu.transpile(circuit, device, optimization_level=3)
haiqu.compare_metrics(transpiled_circuit_opt0, transpiled_circuit_opt3)

Quick Start

Core features

Hybrid programs

Integrations

Catalog

Circuit Analytics

Basic analysis

Accessing metrics directly

Display a breakdown of the different gate types present in the circuit

Comparing circuits

​Basic analysis

​Accessing metrics directly

​Display a breakdown of the different gate types present in the circuit

​Comparing circuits

Basic analysis

Accessing metrics directly

Display a breakdown of the different gate types present in the circuit

Comparing circuits