Divi

Divi

Divi: High-Level Quantum Program Development

Divi is Qoro’s open-source Python library for building, executing, and managing quantum programs. It provides high-level abstractions for variational quantum algorithms, combinatorial optimization, quantum chemistry, and Hamiltonian simulation — with built-in support for parallel execution, error mitigation, checkpointing, and multi-backend deployment.

Divi handles the complexity of quantum-classical hybrid workflows so you can focus on the problem, not the plumbing.

Core Concepts

Quantum Programs

Every quantum workload in Divi is a QuantumProgram — a composable unit that encapsulates circuit generation, parameter management, execution, and result processing. Higher-level constructs like VQE, QAOA, and PCE all extend this base.

Parallel Execution

Divi’s execution model is inherently parallel. Whether running locally on ParallelSimulator or remotely via QoroService, circuits are dispatched in batches and results are aggregated automatically.

Backends

Backend Description
ParallelSimulator Local multi-threaded simulation using Qiskit Aer or Maestro
QoroService Cloud execution through Composer, with access to Maestro and QPU hardware

Built-In Algorithms

Algorithm Use Case
VQE Ground-state energy estimation for quantum chemistry
QAOA Combinatorial optimization (MaxCut, Max-Clique, QUBO, etc.)
PCE Pauli Correlation Encoding — logarithmic qubit reduction for QUBO problems
TimeEvolution Hamiltonian time evolution via Trotter-Suzuki decomposition
CustomVQA Optimize any parameterized PennyLane or Qiskit circuit

Workflows

Workflow Description
GraphPartitioningQAOA Divide-and-conquer QAOA on large graphs via partitioning
QUBOPartitioningQAOA Partitioned QAOA for large QUBO problems
VQEHyperparameterSweep Sweep bond lengths and ansatze for molecular energy surfaces

Ansatze

Divi provides a library of parameterized circuit templates:

Ansatz Description
HartreeFockAnsatz Chemistry-inspired initial state
UCCSDAnsatz Unitary Coupled Cluster Singles and Doubles
QAOAAnsatz Standard QAOA mixer/cost layers
HardwareEfficientAnsatz Hardware-native entangling layers
GenericLayerAnsatz Custom gate sequences repeated in layers

All ansatze extend the Ansatz abstract base class. Custom ansatze can be created by subclassing it.

Optimizers

Divi offers classical optimizers as composable objects:

Optimizer Type Method
ScipyOptimizer Gradient-based & gradient-free L-BFGS-B, Nelder-Mead, COBYLA, and all SciPy methods
MonteCarloOptimizer Stochastic global search Random parameter sampling
PymooOptimizer Evolutionary CMA-ES, Differential Evolution

See the Optimizers page for details.

Circuit Transformations

  • Observable Grouping — Reduces measurement overhead by grouping commuting observables
  • Zero-Noise Extrapolation (ZNE) — Error mitigation via Mitiq integration

See Circuit Transformations for examples.

Additional Features

Checkpointing

Save and resume optimization state at any iteration. Useful for long-running VQE/QAOA jobs on cloud backends. See Checkpointing for details.

Top-N Solutions API

Access the best solutions found during optimization via get_top_solutions(), with probability-ranked results and decoded variable assignments (for PCE).

Circuit Depth Tracking

All backends automatically track circuit depths during execution, enabling performance analysis and depth-reduction strategies.

BinaryQuadraticModel Support

QAOA and PCE accept D-Wave’s BinaryQuadraticModel as input, in addition to NumPy arrays and sparse matrices.

Quick Start 

from divi import ParallelSimulator
from divi.qprog import QAOA, GraphProblem
from divi.qprog.optimizers import ScipyOptimizer, ScipyMethod
import networkx as nx

G = nx.random_regular_graph(3, 10, seed=42)

qaoa = QAOA(
    problem=G,
    graph_problem=GraphProblem.MAXCUT,
    n_layers=2,
    optimizer=ScipyOptimizer(ScipyMethod.COBYLA),
    max_iterations=20,
    backend=ParallelSimulator(),
)

qaoa.run()
qaoa.compute_final_solution()
print(f"Solution: {qaoa.solution}")

Installation 

pip install qoro-divi

For cloud execution, set your API key:

from divi import QoroService, JobConfig

backend = QoroService("your-api-key", config=JobConfig(shots=5000))