HLQuantum
Welcome to the official documentation for HLQuantum (High Level Quantum).
HLQuantum is a high-level Python package designed to simplify working with quantum hardware. Write your quantum logic once and run it on any supported backend — CUDA-Q, Qiskit, Cirq, Braket, PennyLane, or IonQ.
Quick Start
import hlquantum as hlq
from hlquantum import kernel
# Option 1: Build a circuit directly
qc = hlq.Circuit(2)
qc.h(0).cx(0, 1).measure_all()
result = hlq.run(qc, shots=1000)
print(result.counts) # {'00': ~500, '11': ~500}
# Option 2: Use the @kernel decorator
@kernel(num_qubits=2)
def bell(qc):
qc.h(0)
qc.cx(0, 1)
qc.measure_all()
result = hlq.run(bell, shots=1000)
Features
- Backend-Agnostic Circuits — A single
CircuitIR that translates to any supported framework. - Quantum Pipelines — Build modular architectures using ML-inspired
LayerandSequentialmodels. - Resilient Workflows — Orchestrate complex executions with loops, branching, parallelism, and state persistence (save/resume).
- Asynchronous Execution — Multi-backend concurrency with
async/awaitsupport and throttling. - Unitary-Agnostic
@kernel— Write quantum logic as plain Python functions. - GPU Acceleration — Unified
GPUConfigacross all backends. - Built-in Algorithms — QFT, Grover, Bernstein-Vazirani, Deutsch-Jozsa, VQE, QAOA, GQE, arithmetic circuits, and parameter-shift gradients.
- Model Context Protocol (MCP) — Expose your quantum algorithms and raw stack to AI agents.
- Transpilation — Built-in optimisation passes (redundant-gate removal, rotation merging).
- Error Mitigation — Pluggable post-processing for noisy results.
Supported Backends
| Backend | Framework | Install extra |
|---|---|---|
CudaQBackend |
NVIDIA CUDA-Q | pip install hlquantum[cudaq] |
QiskitBackend |
IBM Qiskit | pip install hlquantum[qiskit] |
CirqBackend |
Google Cirq | pip install hlquantum[cirq] |
BraketBackend |
Amazon Braket | pip install hlquantum[braket] |
PennyLaneBackend |
Xanadu PennyLane | pip install hlquantum[pennylane] |
IonQBackend |
IonQ (via qiskit-ionq) | pip install hlquantum[ionq] |
What's Next?
- Core API — Circuits, kernels, parameters, and results.
- Backends — Per-backend configuration and examples.
- Algorithms — Built-in quantum algorithms and friendly aliases.
- Layers & Pipelines — ML-style circuit composition.
- Workflows — Async orchestration, branching, and checkpoints.
- Transpiler — Optimisation passes.
- Mitigation — Error mitigation techniques.
- GPU Acceleration — Multi-GPU and precision configuration.
- AI-Driven Quantum (MCP) — Expose your algorithms to AI assistants.
- Examples — End-to-end demos and tutorials.