Tutorials

Welcome to the pyqpanda3 tutorial series! This section provides a structured learning path from basic quantum computing concepts to advanced features of the pyqpanda3 SDK.

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Learning Path

The tutorials are organized in a progressive order. Each tutorial builds upon concepts introduced in previous ones. We recommend following them in sequence.

graph LR
    A[Getting Started] --> B[Quantum Basics]
    B --> C[Circuit Construction]
    C --> D[Simulation]
    D --> E[Noise Simulation]
    C --> F[Dynamic Circuits]
    C --> G[Variational Circuits]
    D --> H[Hamiltonian]
    D --> I[Quantum Information]
    C --> J[Visualization]
    C --> K[Transpilation]
    D --> L[Cloud Computing]
    B --> M[State Preparation]

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Tutorial List

#	Tutorial	Description	Prerequisites
1	Getting Started	Installation, setup, and your first quantum program	None
2	Quantum Basics	Qubits, quantum gates, measurement, and Bloch sphere	Tutorial 1
3	Circuit Construction	Building circuits with QProg, QCircuit, and the << operator	Tutorial 2
4	Simulation	Running circuits on the simulator family, precision selection and capability queries (0.5.0)	Tutorial 3
5	Noise Simulation	NoiseModel, 7 error channels, per-gate and per-qubit noise configuration	Tutorial 4
6	Dynamic Circuits	Classical control flow with qif, qelse, qwhile for mid-circuit measurement	Tutorial 3
7	Variational Circuits	VQCircuit, Parameter, gradient computation, and batch evaluation	Tutorial 4
8	Hamiltonian & Pauli Operators	PauliOperator algebra, Hamiltonian construction, and expectation values	Tutorial 4
9	Quantum Information	StateVector, DensityMatrix, quantum channels, distance metrics, and quantum-state fidelity (state_fidelity)	Tutorial 4
10	Visualization	Bloch sphere, circuit drawing, state plots, probability charts, and time-scheduling diagrams	Tutorial 3
11	Transpilation	Topology-aware transpilation, gate decomposition, optimization, fidelity-aware layout, LightSABRE routing, and transpilation metrics	Tutorial 3
12	Quantum Cloud User Guide	QCloud map for beginners, algorithm developers, hardware users, and debuggers	Tutorial 4
13	Cloud Computing	QCloudService, job submission, backend selection, result retrieval, instruction round-trips, OriginIR CommProtocol encoding, and best qubit blocks	Tutorial 4
14	Quantum State Preparation	10 encoding methods: amplitude, angle, IQP, Schmidt, sparse isometry, and more	Tutorial 3
15	Fidelity Layout with QCloud	QCloud chip metadata → ChipBackend → fidelity-aware layout and transpilation → scheduled instruction stream: the complete hardware workflow	Tutorial 11
16	OriginIR Syntax	OriginIR intermediate-representation grammar: program structure, gate declarations, angle expressions, DAGGER/CONTROL, QIF/QWHILE, custom gates, and a common-error reference (every example verified by running convert_originir_string_to_qprog)	Tutorial 3

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Recommended Reading Order

Beginner Path

If you are new to quantum computing:

1. Start with Getting Started to set up your environment
2. Follow Quantum Basics to understand core concepts
3. Learn Circuit Construction to build your first circuits
4. Try Simulation to run your circuits

Intermediate Path

Once you are comfortable with the basics:

5. Explore Noise Simulation for realistic hardware modeling
6. Study Hamiltonian & Pauli Operators for variational algorithms
7. Learn Variational Circuits for parameterized circuits and gradients
8. Try Dynamic Circuits for classical-quantum hybrid programs

Advanced Path

For specialized topics:

9. Quantum Information for state analysis and channel representations
10. Transpilation for hardware-aware circuit optimization
11. Quantum Cloud User Guide for choosing the right cloud workflow by role
12. Cloud Computing for running on real quantum hardware
13. Quantum State Preparation for advanced encoding techniques
14. Visualization for circuit and state visualization

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Conventions

Throughout these tutorials, we use the following conventions:

• All code examples use the from pyqpanda3 import core import style
• Code blocks include descriptive comments above each example
• Mathematical formulas use LaTeX notation: inline $...$ and block-level $$...$$
• Diagrams use Mermaid syntax for flowcharts and sequence diagrams