InQuanto

https://docs.quantinuum.com/inquanto/
Admin
InQuanto is Quantinuum's Python-based quantum computational chemistry platform designed to help...
Tokens:288,210
Snippets:2,406
Trust Score:10
Update:2 months ago
Show doc for...
Tokens:
Raw
### Quantum Circuit Simulation Example

Source: https://docs.quantinuum.com/inquanto/_downloads/b5addb5c35580c0fce44e6c478dda65a/InQ_htut_Aer_H2.ipynb

This snippet demonstrates the basic setup and execution of a quantum circuit simulation.

```python
import numpy as np
from inquanto.simulators import AerSimulator
from inquanto.circuits import Circuit

# Define a simple circuit
circuit = Circuit().h(0).cx(0, 1)

# Initialize the simulator
simulator = AerSimulator()

# Run the simulation
result = simulator.run(circuit)
print(result)
```

--------------------------------

### Phayes output example

Source: https://docs.quantinuum.com/inquanto/extensions/inquanto-phayes.html

Example output showing the energy estimation results after 50 updates.

```text
i_update       = 50
Energy(mu)     =  -1.137409 hartree
Energy(sigma)  =   0.001065 hartree
Representation = von Mises
```

--------------------------------

### Initialize VQE with InQuanto

Source: https://docs.quantinuum.com/inquanto/_downloads/bf905acceac178acdbe7862a0b7db03e/InQ_tut_vqe_2.ipynb

Basic setup for configuring a VQE algorithm instance.

```python
from inquanto.algorithms import VQE
from inquanto.ansatz import AnsatzUCCSD
from inquanto.core import QuantumState

# Example configuration
ansatz = AnsatzUCCSD(qubit_operator=hamiltonian)
vqe = VQE(ansatz=ansatz, expectation_estimator=estimator)
```

--------------------------------

### Initialize VQE with InQuanto

Source: https://docs.quantinuum.com/inquanto/_downloads/bf905acceac178acdbe7862a0b7db03e/InQ_tut_vqe_2.ipynb

Basic setup for a VQE instance using the InQuanto library.

```python
from inquanto.algorithms import VQE
from inquanto.ansatz import Ansatz
from inquanto.operators import QubitOperator

# Example setup
ansatz = Ansatz(...)
operator = QubitOperator(...)
vqe = VQE(ansatz=ansatz, operator=operator)
```

--------------------------------

### Protocol output examples

Source: https://docs.quantinuum.com/inquanto/tutorials/InQ_tut_helios.html

Example output for circuit count and energy evaluation.

```text
Number of circuits:  2
```

```text
-1.1357335665698658
```

--------------------------------

### Bayesian QPE Output Examples

Source: https://docs.quantinuum.com/inquanto/extensions/inquanto-phayes.html

Example console outputs showing the update iteration and representation mode.

```text
i_update       = 1
representation = Fourier
```

```text
i_update       = 11
representation = Fourier
```

```text
i_update       = 21
representation = Fourier
```

```text
i_update       = 31
representation = von Mises
```

```text
i_update       = 41
representation = von Mises
```

--------------------------------

### Configure simulator and optimizer

Source: https://docs.quantinuum.com/inquanto/tutorials/InQ_tut_vqe_1.html

Setup the AerStateBackend for simulation and the Scipy minimizer for parameter optimization.

```python
# install pytket-qiskit using e.g. pip install pytket-qiskit if necessary


from pytket.extensions.qiskit import AerStateBackend
from inquanto.minimizers import MinimizerScipy

backend = AerStateBackend()
minimizer = MinimizerScipy(method="L-BFGS-B")
```

--------------------------------

### Example Output

Source: https://docs.quantinuum.com/inquanto/manual/algorithms/qpe_iterative.html

Sample output showing energy and precision results.

```text
Energy     =  -1.906250 hartree
precision =   0.015625 hartree
```

--------------------------------

### Example Output

Source: https://docs.quantinuum.com/inquanto/manual/algorithms/algorithms_sceom.html

Sample output showing timing information and calculated eigenvalues.

```text
# TIMER Measure and calculate M matrix elements: BEGINS AT 2026-07-22 12:01:04.062064
# TIMER Measure and calculate M matrix elements: ENDS - DURATION (s):  0.0759027 [0:00:00.075903]
# TIMER Find eigenvalues of M matrix: BEGINS AT 2026-07-22 12:01:04.138061
# TIMER Find eigenvalues of M matrix: ENDS - DURATION (s):  0.0005775 [0:00:00.000578]
QSCEOM eigenvalues:  [ 0.552 -0.136 -0.495]
Exact eigvals : [-1.137 -0.495 -0.495 -0.495 -0.136  0.552]
```

--------------------------------

### Install keyrings.alt backend

Source: https://docs.quantinuum.com/inquanto/introduction/troubleshooting.html

Install the keyrings.alt package to provide alternative backends for the keyring module on Unix systems.

```bash
pip install keyrings.alt
```

--------------------------------

### Initialize VQE with InQuanto

Source: https://docs.quantinuum.com/inquanto/_downloads/bf905acceac178acdbe7862a0b7db03e/InQ_tut_vqe_2.ipynb

Setup the VQE algorithm using a specified ansatz and optimizer.

```python
from inquanto.algorithms import VQE
from inquanto.ansatz import AnsatzCircuit
from inquanto.optimizers import Optimizer

# Example setup for VQE
ansatz = AnsatzCircuit(circuit=my_circuit)
optimizer = Optimizer(method="COBYLA")
vqe = VQE(ansatz=ansatz, optimizer=optimizer)
```

--------------------------------

### Calculate derivative overlap with UCCSD ansatz

Source: https://docs.quantinuum.com/inquanto/manual/protocols/derivative.html

Example demonstrating the setup and execution of the HadamardTestDerivativeOverlap protocol for a UCCSD ansatz.

```python
from inquanto.express import get_system
from inquanto.ansatzes import FermionSpaceAnsatzUCCSD
from inquanto.protocols import HadamardTestDerivativeOverlap

_, fermion_space, hf_state = get_system("h2_sto3g.h5")
ansatz = FermionSpaceAnsatzUCCSD(
   fermion_space, hf_state
)
params = ansatz.state_symbols.construct_random()
symbols = ansatz.state_symbols.symbols
print(params)

protocol = HadamardTestDerivativeOverlap(AerBackend(), shots_per_circuit=5000)
protocol.build(
   parameters=params,
   state=ansatz,
   diff_symbols=(symbols[1], symbols[1])
)
protocol.compile_circuits()
protocol.run()
protocol.evaluate_derivative_overlap()
```

--------------------------------

### Initialize Selene Backend

Source: https://docs.quantinuum.com/inquanto/_downloads/9e888857feb4adf83cbe1e2da5e2c4b8/InQ_tut_helios.ipynb

Sets up the Selene backend using the QuEST simulator via the hugr module.

```python
from inquanto.hugr.hugr_utils import SELENE

backend = SELENE.QUEST
```

--------------------------------

### Timer block start

Source: https://docs.quantinuum.com/inquanto/manual/ansatze/hva.html

Internal timer block start marker.

```text
# TIMER BLOCK-0 BEGINS AT 2026-07-22 12:04:00.572631
```

--------------------------------

### Initialize VQE and Run

Source: https://docs.quantinuum.com/inquanto/_downloads/bf905acceac178acdbe7862a0b7db03e/InQ_tut_vqe_2.ipynb

This snippet demonstrates the initialization of the VQE algorithm and its execution within the InQuanto framework.

```python
from inquanto.algorithms import VQE
from inquanto.ansatz import AnsatzProduct
from inquanto.computational_framework import ComputationalFramework
from inquanto.operators import QubitOperator
from inquanto.optimizer import OptimizerSciPy

# Define the Hamiltonian
hamiltonian = QubitOperator("X0 X1") + QubitOperator("Z0")

# Define the Ansatz
ansatz = AnsatzProduct([QubitOperator("Y0"), QubitOperator("X1")])

# Initialize VQE
vqe = VQE(ansatz=ansatz, hamiltonian=hamiltonian, optimizer=OptimizerSciPy(method="COBYLA"))

# Run VQE
result = vqe.run()
```

--------------------------------

### VQE Output Examples

Source: https://docs.quantinuum.com/inquanto/manual/minimizers.html

Example outputs for energy and gradient calculations.

```text
VQE energy with parameters at [0, 0, 0]: -1.117505884204331
Gradients of parameters at [0, 0, 0]: [0.359 0.    0.   ]
```

--------------------------------

### Initialize and Print QubitState Objects

Source: https://docs.quantinuum.com/inquanto/api/inquanto/states.html

Demonstrates creating QubitState instances using list-based configurations, QubitStateString objects, and dictionary mappings of state strings to coefficients.

```python
>>> qs0 = QubitState([1, 1, 0, 0], 1)
>>> print(qs0)
(1, {q[0]: 1, q[1]: 1, q[2]: 0, q[3]: 0})
>>> qss = QubitStateString({Qubit(0):1,Qubit(1):1,Qubit(2):0,Qubit(5):0})
>>> qs1 = QubitState(qss, 1)
>>> print(qs1)
(1, {q[0]: 1, q[1]: 1, q[2]: 0, q[5]: 0})
>>> qss0 = QubitStateString({Qubit(0):1,Qubit(1):1,Qubit(2):0,Qubit(5):0})
>>> qss1 = QubitStateString({Qubit(0):1,Qubit(1):0,Qubit(2):1,Qubit(5):0})
>>> qs2 = QubitState(((0.9, qss0), (0.1, qss1)))
>>> print(qs2)
(0.9, {q[0]: 1, q[1]: 1, q[2]: 0, q[5]: 0}), (0.1, {q[0]: 1, q[1]: 0, q[2]: 1, q[5]: 0})
>>> qss0 = QubitStateString({Qubit(0):1,Qubit(1):1,Qubit(2):0,Qubit(5):0})
>>> qss1 = QubitStateString({Qubit(0):1,Qubit(1):0,Qubit(2):1,Qubit(5):0})
>>> qs3 = QubitState({qss0: 0.9, qss1: 0.1})
>>> print(qs3)
(0.9, {q[0]: 1, q[1]: 1, q[2]: 0, q[5]: 0}), (0.1, {q[0]: 1, q[1]: 0, q[2]: 1, q[5]: 0})
```

--------------------------------

### Timer block start

Source: https://docs.quantinuum.com/inquanto/manual/algorithms/algorithms_vqe.html

Log output indicating the start of a timer block.

```text
# TIMER BLOCK-0 BEGINS AT 2026-07-22 12:01:58.398104
```

--------------------------------

### Output of Psi4 FCIDUMP Example

Source: https://docs.quantinuum.com/inquanto/manual/spaces.html

Expected output from the Psi4 integration example.

```text
<class 'inquanto.operators._chemistry_integral_operator.ChemistryRestrictedIntegralOperator'>
0.5291772106699999
0.5291772106699999
```

--------------------------------

### Configure and run MinimizerBasinHopping

Source: https://docs.quantinuum.com/inquanto/manual/minimizers.html

Demonstrates initializing MinimizerBasinHopping and using it within an AlgorithmVQE workflow.

```python
from inquanto.minimizers import MinimizerBasinHopping

minimizer=MinimizerBasinHopping(disp=True, niter=5, T=1e-3)

vqe = AlgorithmVQE(
    objective_expression=ev,
    minimizer=minimizer,
    initial_parameters=ansatz.state_symbols.construct_zeros()
)
vqe.build(protocol_objective=SparseStatevectorProtocol(AerStateBackend()))
vqe.run()

print("VQE Energy:", vqe.generate_report()["final_value"])
```

--------------------------------

### Configure and Run VQE

Source: https://docs.quantinuum.com/inquanto/_downloads/bf905acceac178acdbe7862a0b7db03e/InQ_tut_vqe_2.ipynb

This snippet demonstrates the core setup for a VQE calculation, including the definition of the ansatz and the optimizer.

```python
from inquanto.algorithms import VQE
from inquanto.ansatz import FermionAnsatz
from inquanto.minimizers import ScipyMinimizer

# Define the ansatz
ansatz = FermionAnsatz(n_qubits=4, n_electrons=2)

# Define the optimizer
optimizer = ScipyMinimizer(method="COBYLA")

# Initialize VQE
vqe = VQE(ansatz=ansatz, minimizer=optimizer)

# Run the VQE
result = vqe.run(hamiltonian=hamiltonian)
```

--------------------------------

### HadamardTest output examples

Source: https://docs.quantinuum.com/inquanto/manual/protocols/expval.html

Example outputs for the number of circuits and the evaluated energy.

```text
Number of circuits:  14
```

```text
np.float64(-1.141235691507334)
```

--------------------------------

### Example Code Snippet

Source: https://docs.quantinuum.com/inquanto/_downloads/7194654a526c8b6b757928e1198420cd/InQ_htut_qsys_H2.ipynb

This is an example code snippet extracted from the provided source.

```text
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
```

--------------------------------

### SCEOM Analysis Dataframe Output Example

Source: https://docs.quantinuum.com/inquanto/manual/algorithms/algorithms_sceom.html

Example output of the SCEOM analysis dataframe.

```text
   <Sz>  <S2>  <GS|ES>
0  -0.0   0.0     -0.0
1  -0.0   0.0     -0.0
2  -0.0   2.0     -0.0
```

--------------------------------

### Configure Simulator and Optimizer

Source: https://docs.quantinuum.com/inquanto/_downloads/fd812ffb8a2255aa46fcc33ef071bfee/InQ_tut_vqe_1.ipynb

Initializes the Qiskit AerStateBackend for state vector simulation and sets up a Scipy L-BFGS-B minimizer for parameter optimization.

```python
# ############################### #
# SIMULATOR AND OPTIMIZER DETAILS #
# ############################### #

# install pytket-qiskit using e.g. pip install pytket-qiskit if necessary


from pytket.extensions.qiskit import AerStateBackend
from inquanto.minimizers import MinimizerScipy

backend = AerStateBackend()
minimizer = MinimizerScipy(method="L-BFGS-B")
```

--------------------------------

### Initialize AlgorithmQSE

Source: https://docs.quantinuum.com/inquanto/manual/algorithms/algorithms_qse.html

Configure the QSE algorithm with the computable matrices and VQE parameters.

```python
from inquanto.computables.composite import QSEMatricesComputable
from inquanto.algorithms import AlgorithmQSE

vqe_parameters = ansatz.state_symbols.construct_from_array(
    [0.0, 0.0, -0.107233493519281]
)

computable = QSEMatricesComputable(
    state=ansatz,
    hermitian_operator=qubit_hamiltonian,
    expansion_operators=expansion_operators,
)

algorithm = AlgorithmQSE(
    computable_qse_matrices=computable,
    parameters=vqe_parameters,
)
```

--------------------------------

### PauliAveraging output example

Source: https://docs.quantinuum.com/inquanto/manual/protocols/expval.html

Example output showing the number of Hamiltonian terms and circuits generated by the protocol.

```text
Number of terms in hamiltonian:  15
Number of circuits:  2
```

--------------------------------

### Configure SCEOMMatrixComputable and AlgorithmSCEOM

Source: https://docs.quantinuum.com/inquanto/manual/algorithms/algorithms_sceom.html

Set up the computable matrix and initialize the SCEOM algorithm with optimized VQE parameters.

```python
from inquanto.computables.composite import SCEOMMatrixComputable
from inquanto.algorithms import AlgorithmSCEOM

vqe_parameters = ansatz.state_symbols.construct_from_array([-0.10723347230091537])
vqe_state = ansatz.to_CircuitAnsatz(vqe_parameters)

computable = SCEOMMatrixComputable(
    space=fermion_space,
    fermion_state=fermion_state,
    ground_state=vqe_state,
    mapping=jw,
    hermitian_operator=qubit_hamiltonian,
    expansion_operators=expansion_operators,
)

algorithm = AlgorithmSCEOM(
    computable_sceom_matrix=computable,
    parameters = vqe_parameters
)
```

--------------------------------

### ProjectiveMeasurements output example

Source: https://docs.quantinuum.com/inquanto/manual/protocols/other.html

Example output showing the basis states, probabilities, uncertainties, and counts obtained from the protocol.

```text
  Basis state  Probability  Uncertainty  Count
0        0110        0.600     0.015492    600
1        0011        0.224     0.013184    224
2        1100        0.164     0.011709    164
3        1001        0.012     0.003443     12
```

--------------------------------

### Configure and run MinimizerSPSA

Source: https://docs.quantinuum.com/inquanto/manual/minimizers.html

Demonstrates initializing MinimizerSPSA and using it within an AlgorithmVQE workflow.

```python
from inquanto.minimizers import MinimizerSPSA

minimizer=MinimizerSPSA(max_iterations=5, disp=True)

vqe = AlgorithmVQE(
    objective_expression=ev,
    minimizer=minimizer,
    initial_parameters=ansatz.state_symbols.construct_zeros()
)
vqe.build(protocol_objective=SparseStatevectorProtocol(AerStateBackend()))
vqe.run()

print("VQE Energy:", vqe.generate_report()["final_value"])
```

--------------------------------

### Initialize Quantinuum backend

Source: https://docs.quantinuum.com/inquanto/manual/protocols/resource_estimation.html

Sets up the Quantinuum backend using an offline API handler for local emulation.

```python
# Prepare the Quantinuum backend
from pytket.extensions.quantinuum import QuantinuumBackend, QuantinuumAPIOffline

backend = QuantinuumBackend(device_name="H2-1LE", api_handler=QuantinuumAPIOffline())
```

--------------------------------

### Constructing and running an IterativePhaseDiffEstimation protocol

Source: https://docs.quantinuum.com/inquanto/manual/protocols/phase_est.html

This example demonstrates setting up a QPDE protocol with a controlled excitation circuit and a simple Hamiltonian to estimate a phase difference.

```python
from inquanto.protocols import IterativePhaseDiffEstimation, UStrat

# Reference-state preparation on the single system qubit (|0>).
state = CircuitAnsatz(Circuit(1))

# Build the controlled excitation circuit
ctrl_excit_circ = Circuit(2)
ctrl_excit_circ.CX(0, 1)
ctrl_excit = CircuitAnsatz(ctrl_excit_circ)

# System Hamiltonian H = (1/2) Z; the |0>-|1> phase difference is one half-turn.
qopl = QubitOperator("Z0", 0.5).trotterize()

# Construct and build the QPDE protocol.
protocol = IterativePhaseDiffEstimation(
    backend=backend,
    n_shots=n_shots,
).build(
    state=state,
    ctrl_excit=ctrl_excit,
    evolution_operator_exponents=qopl,
    u_strat=UStrat.PAULI_EXP_BOX,
)

# Choose beta to cancel the known phase difference (k = 1, phi = 1 half-turn).
protocol.update_k_and_beta(k=1, beta=1.0)
protocol.run()

# With beta tuned to the phase difference, every shot returns 0.
protocol.get_measurement_outcome()
```

--------------------------------

### SCEOM State Output Example

Source: https://docs.quantinuum.com/inquanto/manual/algorithms/algorithms_sceom.html

Example output showing coefficients, basis states, indices, and probabilities for excited states.

```text
0th excited state:
           Coefficient Basis state  State index  Probability
0  0.994256+0.000000j        0011            3     0.988545
1  0.107028+0.000000j        1100           12     0.011455
```

```text
1th excited state:
           Coefficient Basis state  State index  Probability
0  0.707107+0.000000j        0110            6          0.5
1 -0.707107+0.000000j        1001            9          0.5
```

```text
2th excited state:
           Coefficient Basis state  State index  Probability
0 -0.707107+0.000000j        0110            6          0.5
1 -0.707107+0.000000j        1001            9          0.5
None
```

--------------------------------

### AlgorithmFermionicAdaptVQE Output

Source: https://docs.quantinuum.com/inquanto/manual/algorithms/algorithms_adapt.html

Example output generated by the AlgorithmFermionicAdaptVQE execution.

```text
# TIMER BLOCK-1 ENDS - DURATION (s):  0.0925028 [0:00:00.092503]
Minimum Energy: -1.1368465754720531
d0 : -0.10723347230091586
```

--------------------------------

### HugrAdapter.process_programs

Source: https://docs.quantinuum.com/inquanto/api/extensions/inquanto-nexus_api.html

Starts an execution job of HUGR programs on the Nexus backend.

```APIDOC
## HugrAdapter.process_programs

### Description
Starts an execution job of HUGR programs on Nexus.

### Parameters
- **hugr_programs** (Sequence[Hugr]) - Required - Programs to process on the backend.
- **n_shots** (Union[list[int], list[None], None]) - Optional - Number of shots for each program.
- **valid_check** (bool) - Optional - Whether to perform validation checks.
- **execute_job_annotations** (Optional[ProtocolAnnotations]) - Optional - Annotations of the execution job.
- **program_annotations** (Optional[ProtocolAnnotations]) - Optional - Annotations of the HUGR program.

### Returns
- **ExecuteJobRef** - Reference to the submitted execution job.
```

--------------------------------

### Initialize InQuanto and Define Molecule

Source: https://docs.quantinuum.com/inquanto/_downloads/2633a4610029448fb62611b6fa6eb3da/InQ_tut_fe4n2_3.ipynb

Setup the InQuanto environment and define the Fe4N2 molecular structure.

```python
import inquanto as iq
from inquanto.core import Molecule

# Define the Fe4N2 molecule
molecule = Molecule.from_xyz("Fe4N2.xyz")
print(molecule)
```

--------------------------------

### Deprecation warning output

Source: https://docs.quantinuum.com/inquanto/tutorials/InQ_tut_fe4n2_3.html

Example of a deprecation warning encountered during execution.

```text
/Users/ciaran.ward/repos/inquanto-docs/.venv/lib/python3.12/site-packages/qnexus/client/utils.py:170: DeprecationWarning: The `circuits` argument is deprecated and will be removed in a future version. Please use `programs`.
  warnings.warn(
```

--------------------------------

### Load Hamiltonian and Setup Space

Source: https://docs.quantinuum.com/inquanto/manual/minimizers.html

Initializes the Hamiltonian and qubit mapping required for VQE calculations.

```python
from inquanto.express import load_h5
from inquanto.spaces import FermionSpace
from inquanto.mappings import QubitMappingJordanWigner

h2_sto3g = load_h5("h2_sto3g.h5")
hamiltonian = h2_sto3g["hamiltonian_operator"]
space = FermionSpace(4)
state = space.generate_occupation_state_from_list([1, 1, 0, 0])
qubit_hamiltonian = QubitMappingJordanWigner().operator_map(hamiltonian)
```

--------------------------------

### Ansatz report output

Source: https://docs.quantinuum.com/inquanto/tutorials/InQ_tut_vqe_1.html

Example output from the ansatz generation report.

```text
ANSATZ REPORT:
{'n_parameters': 3, 'n_qubits': 4}

 2-qubit GATES:  64

 ANSATZ GENERATION CIRCUIT:
```

--------------------------------

### Example Output Distribution

Source: https://docs.quantinuum.com/inquanto/manual/protocols/phase_est.html

The expected output format from the get_distribution method.

```text
{(0,): np.float64(1.0), (1,): np.float64(0.0)}
```

--------------------------------

### Initialize and inspect MultiConfigurationStateBox

Source: https://docs.quantinuum.com/inquanto/manual/ansatze/multi_configurational.html

Demonstrates creating a MultiConfigurationStateBox from a QubitState object and retrieving circuit resource metrics.

```python
from inquanto.ansatzes import MultiConfigurationStateBox
from inquanto.states import QubitState, QubitStateString

from pytket.extensions.qiskit import AerStateBackend

from math import sqrt

config1, c_1 = QubitStateString([1, 1, 0, 0]), 1/sqrt(2)
config2, c_2 = QubitStateString([0, 0, 1, 1]), 1/sqrt(2)
qubit_states = QubitState({config1: c_1, config2: c_2})

multi_state = MultiConfigurationStateBox(qubit_states)
print(multi_state.circuit_resources())
```

```text
{'depth': 26, 'gates': 29, 'qubits': 4, 'gates_2q': 14, 'gates_1q': 15}
```

--------------------------------

### AlgorithmAdaptVQE Execution Output

Source: https://docs.quantinuum.com/inquanto/manual/algorithms/algorithms_adapt.html

Example output showing the minimum energy and final parameters after running the AdaptVQE algorithm.

```text
# TIMER BLOCK-0 BEGINS AT 2026-07-22 12:00:49.992101
# TIMER BLOCK-0 ENDS - DURATION (s):  0.0837740 [0:00:00.083774]
Minimum Energy: -1.1368465754720531
d0 : -0.10723347230091586
```

--------------------------------

### Output Results

Source: https://docs.quantinuum.com/inquanto/tutorials/InQ_tut_helios.html

Example output for circuit count and energy evaluation.

```text
Number of circuits:  2
```

```text
-1.1284691520823564
```

--------------------------------

### PauliAveraging energy evaluation result

Source: https://docs.quantinuum.com/inquanto/manual/protocols/expval.html

Example output of the evaluated energy.

```text
-1.1369585430190199
```

--------------------------------

### Iteration 2 log output

Source: https://docs.quantinuum.com/inquanto/manual/minimizers.html

Example output for the second iteration.

```text
Iteration 2
```

--------------------------------

### Initialize a Nexus Project

Source: https://docs.quantinuum.com/inquanto/_downloads/11b736c3ae23729fbf8320852d45c888/InQ_tut_Nexus_1.ipynb

Create or retrieve a Nexus project to manage chemistry jobs.

```python
from qnexus.client import projects

project_name = f"InQuanto Documentation"

project_ref = projects.get_or_create(
    name=project_name, description="", properties={}
)
```

--------------------------------

### Run initial VQE for ground state

Source: https://docs.quantinuum.com/inquanto/manual/algorithms/algorithms_vqd.html

Sets up the system, ansatz, and minimizer to perform an initial VQE experiment, which is a prerequisite for VQD.

```python
from inquanto.express import get_system
from inquanto.ansatzes import FermionSpaceAnsatzkUpCCGSD
from inquanto.computables import ExpectationValue
from inquanto.mappings import QubitMappingJordanWigner
from inquanto.minimizers import MinimizerScipy
from inquanto.algorithms import AlgorithmVQE
from pytket.extensions.qiskit import AerStateBackend
from inquanto.protocols import SparseStatevectorProtocol

fermion_hamiltonian, fermion_fock_space, fermion_state = get_system("h2_sto3g.h5")
mapping = QubitMappingJordanWigner()
qubit_hamiltonian = mapping.operator_map(fermion_hamiltonian)
ansatz = FermionSpaceAnsatzkUpCCGSD(fermion_fock_space, fermion_state, k_input=2)
expectation_value = ExpectationValue(ansatz, qubit_hamiltonian)

minimizer = MinimizerScipy(method="L-BFGS-B")

vqe = (
    AlgorithmVQE(
        minimizer,
        expectation_value,
        initial_parameters=ansatz.state_symbols.construct_zeros(),
    )
    .build(
        protocol_objective=SparseStatevectorProtocol(AerStateBackend())
    )
    .run()
)
```

--------------------------------

### VQE report output

Source: https://docs.quantinuum.com/inquanto/manual/algorithms/algorithms_vqe.html

Example dictionary output from the generate_report method.

```text
{'minimizer': {'n_iterations': 3, 'function_evaluations': 20, 'final_value': -1.136846575472054, 'final_parameters': array([-0.107,  0.   ,  0.   ])}, 'final_value': -1.136846575472054, 'initial_parameters': [{'Ordering': 0, 'Symbol': 'd0', 'Value': 0.0}, {'Ordering': 1, 'Symbol': 's0', 'Value': 0.0}, {'Ordering': 2, 'Symbol': 's1', 'Value': 0.0}], 'final_parameters': [{'Ordering': 0, 'Symbol': 'd0', 'Value': np.float64(-0.10723348543796114)}, {'Ordering': 1, 'Symbol': 's0', 'Value': np.float64(0.0)}, {'Ordering': 2, 'Symbol': 's1', 'Value': np.float64(0.0)}]}
```

--------------------------------

### Prepare a multiconfiguration state with two configurations

Source: https://docs.quantinuum.com/inquanto/manual/ansatze/multi_configurational.html

Demonstrates initializing a MultiConfigurationStateSparse object from a QubitState defined by two specific configurations and their coefficients.

```python
from inquanto.ansatzes import MultiConfigurationStateSparse
from inquanto.states import QubitState, QubitStateString

from pytket.extensions.qiskit import AerStateBackend

from math import sqrt

config1, c_1 = QubitStateString([1, 1, 0, 0]), 1/sqrt(2)
config2, c_2 = QubitStateString([0, 0, 1, 1]), 1/sqrt(2)
qubit_states = QubitState({config1: c_1, config2: c_2})

multi_state = MultiConfigurationStateSparse(qubit_states)
print(multi_state.circuit_resources())
```

--------------------------------

### Configure Quantinuum Emulator Backend

Source: https://docs.quantinuum.com/inquanto/_downloads/a2b0d2d0587ce927f9532bc1675e02b4/InQ_htut_qsys_qse.ipynb

Sets up a remote Quantinuum emulator backend via qnexus for noise profile simulation.

```python
from qnexus.client import auth, projects, devices
from qnexus import QuantinuumConfig

# auth.login()

# qnexus requires the specification of a project for all submitted jobs
project_ref = projects.get_or_create(name="InQuanto Documentation", description="", properties={})

# Change the machine name if necessary.
machine = "H2-1E"
backend = QuantinuumConfig(device_name=machine)
```

--------------------------------

### quantum_number_orb(i)

Source: https://docs.quantinuum.com/inquanto/api/inquanto/spaces.html

Get spatial orbital index from a spin orbital index.

```APIDOC
### quantum_number_orb(i)

**Parameters:**
- **i** (int) - Spin orbital index.

**Returns:**
- (int) - The spatial orbital index.
```

--------------------------------

### Output of Pauli Symmetries

Source: https://docs.quantinuum.com/inquanto/tutorials/InQ_tut_Nexus_1.html

Example output showing the Pauli symmetry operators.

```text
(1.0, Z2 Z3)
(1.0, Z0 Z1 Z2 Z3 Z4 Z5)
(-1.0, Z0 Z2 Z4)
```

--------------------------------

### Initialize UCCGSD and k-UpCCGSD Ansatzes

Source: https://docs.quantinuum.com/inquanto/manual/ansatze/ucc_family.html

Demonstrates the initialization of UCCGSD and k-UpCCGSD ansatzes using FermionSpace and a qubit mapping.

```python
from inquanto.states import FermionState
from inquanto.spaces import FermionSpace
from inquanto.mappings import QubitMappingJordanWigner
from inquanto.ansatzes import FermionSpaceAnsatzkUpCCGSD, FermionSpaceAnsatzUCCGSD

space = FermionSpace(4)
state = FermionState([1, 1, 0, 0])
jw_map = QubitMappingJordanWigner()

ansatz_uccgsd = FermionSpaceAnsatzUCCGSD(
    fermion_space=space, fermion_state=state, qubit_mapping=jw_map
)

ansatz_kupccsd = FermionSpaceAnsatzkUpCCGSD(
    fermion_space=space, fermion_state=state, k_input=2, qubit_mapping=jw_map
)

print("\nNumber of parameters: {} (UCCGSD), {} (k-UpCCGSD)".format(
    ansatz_uccgsd.generate_report()['n_parameters'],
    ansatz_kupccsd.generate_report()['n_parameters'])
)
```

--------------------------------

### Example Tapered Hamiltonian Output

Source: https://docs.quantinuum.com/inquanto/manual/symmetry.html

Representation of the output format for a tapered Hamiltonian.

```text
(-0.2926446722772267, I0), (0.824861211927104, Z0), (0.17966867956301552, X0)
```
Tokens:
Raw