API Reference¶

This section provides detailed API documentation for all QSCI modules and classes.

Core Modules¶

QSCI Algorithms¶

qsci_algorithms - Core QSCI algorithm implementations
VanillaQSCI - Standard QSCI algorithm
TimeEvolvedQSCI - Base class for TE-QSCI variants
SingleTimeTE_QSCI - Single time evolution QSCI
TimeAverageTE_QSCI - Time-averaged QSCI
StateVectorTE_QSCI - State vector TE-QSCI

Algorithm Interfaces¶

qsci_algo_interface - QURI Algo compatible interfaces
Algorithm wrappers for quri-algo integration
Standard interfaces for all QSCI variants
Result handling and data structures

Probability Calculator¶

probability_calculator - Probability computation utilities
State probability calculations
Measurement outcome processing
Statistical analysis tools

QURI VM Analysis¶

qsci_vm_analysis - Circuit analysis and resource estimation
LogicalCircuit analysis
ArchLogicalCircuit analysis with STAR architecture
Gate count and depth estimation
Quantum resource analysis

Module	Description	Key Classes
qsci_algorithms	Core algorithm implementations	`VanillaQSCI`, `TimeEvolvedQSCI`
qsci_algo_interface	QURI Algo interfaces	Algorithm wrappers and interfaces
probability_calculator	Probability utilities	Probability computation functions
qsci_vm_analysis	Circuit analysis	QURI VM integration tools

Usage Examples¶

Basic Algorithm Usage¶

from qsci_algorithms import VanillaQSCI
from quri_parts.core.operator import Operator
from quri_parts.qulacs.sampler import create_qulacs_vector_concurrent_sampler

# Create QSCI algorithm instance
qsci = VanillaQSCI(
    hamiltonian=your_hamiltonian,
    sampler=create_qulacs_vector_concurrent_sampler(),
    num_states_pick_out=16
)

# Run algorithm
result = qsci.run(initial_states, total_shots=2000)
print(f"Ground state energy: {result.ground_state_energy}")

Time-Evolved QSCI¶

from qsci_algorithms import SingleTimeTE_QSCI

# Create TE-QSCI algorithm
te_qsci = SingleTimeTE_QSCI(
    hamiltonian=your_hamiltonian,
    sampler=sampler,
    evolution_time=1.0,
    num_trotter_steps=10
)

result = te_qsci.run(initial_states, total_shots=3000)

QURI Algo Integration¶

from qsci_algo_interface import QSCIGroundState

# Use with quri-algo
algorithm = QSCIGroundState(
    hamiltonian=hamiltonian,
    variant="vanilla",
    sampler=sampler
)

result = algorithm(initial_state)

Circuit Analysis¶

from qsci_vm_analysis import analyze_qsci_circuit

# Analyze circuit resources
analysis = analyze_qsci_circuit(
    circuit=your_circuit,
    architecture="star",
    analysis_level="arch_logical"
)

print(f"Gate count: {analysis.gate_count}")
print(f"Circuit depth: {analysis.circuit_depth}")
print(f"Estimated fidelity: {analysis.circuit_fidelity}")

Type Annotations¶

All public functions and classes include comprehensive type annotations for better IDE support and documentation clarity.

Error Handling¶

The API includes proper error handling with descriptive error messages. Common exceptions:

ValueError - Invalid parameter values
TypeError - Incorrect parameter types
RuntimeError - Algorithm execution errors
NotImplementedError - Unimplemented features

Performance Considerations¶

Use create_qulacs_vector_concurrent_sampler() for best performance
Set appropriate num_states_pick_out values (typically 2**n_qubits for small systems)
Consider time evolution parameters for TE-QSCI variants
Use circuit analysis for resource planning

For detailed documentation of specific functions and classes, see the individual module pages linked above.