Quantum Computing Python SDK
pyqpanda3
From your first circuit to hardware-aware workflows, the pyqpanda3 docs connect tutorials, API references, concepts, and examples into one focused quantum development console.
pip install pyqpanda3from pyqpanda3.core import QProg, CPUQVMbuild -> run -> get_countsCommon single-qubit, multi-qubit, and variational gates for tutorials and real algorithms.
Execution paths for CPUQVM, GPUQVM, density-matrix, stabilizer, and amplitude-query workflows.
Model decoherence, measurement error, and hardware constraints before running jobs.
A performance path for larger circuits, repeated experiments, and heavier workloads.
Documentation Matrix
Choose Your Entry
Move through the docs by task: learn, inspect APIs, reproduce workflows, track releases, or build theory.
Tutorials
Step-by-step guides for basics, circuits, simulation, noise, variational workflows, and cloud computing.
RecipesHow-To Guides
Task-focused examples for Bell, GHZ, Grover, VQE, QAOA, IR conversion, and more.
ReferenceAPI Reference
Core modules, Hamiltonian tools, quantum information, QCloud, visualization, and utilities.
TheoryConcepts
Theory for gates, noise models, transpilation, quantum information, and variational algorithms.
UpdatesBlog
Release announcements, workflow notes, and practical quantum computing articles.
HistoryRelease Notes
Follow features, fixes, and migration notes across pyqpanda3 versions.
Learning Vector
Recommended Route
New to pyqpanda3? These three stops build the right context quickly.
Executable Pattern
Install, build, and run in one pass
Keep the shortest path close: install the SDK, construct a quantum program, run CPUQVM, read measurement counts, then branch into noise, cloud, and transpilation workflows.
Get Started01pip install pyqpanda3
02from pyqpanda3.core import CPUQVM, QProg, H, CNOT, measure
03prog = QProg()
04prog << H(0) << CNOT(0, 1)
05prog << measure([0, 1], [0, 1])
06qvm = CPUQVM()
07qvm.run(prog, shots=1000)
08counts = qvm.result().get_counts()
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