<h2><p align="center">TyxonQ</p></h2>
<h3><p align="center">Full-stack Quantum Software Framework on Real Machine</p></h3>
[](https://opensource.org/licenses/Apache-2.0)
[](https://www.python.org/downloads/)
[](https://www.tyxonq.com/)
TyxonQ 太玄量子 is a full-stack quantum software framework for quantum simulation, optimization, and quantum machine learning. Forked from the open-source project [TensorCircuit](https://github.com/tencent-quantum-lab/tensorcircuit) and licensed under Apache License 2.0, it integrates modern quantum programming paradigms including automatic differentiation, just-in-time compilation, and hardware acceleration.
**🚀 REAL QUANTUM HARDWARE READY**: TyxonQ supports **real quantum machine execution** through our quantum cloud services powered by **QureGenAI**. Currently featuring the **Homebrew_S2** quantum processor, enabling you to run your quantum algorithms on actual quantum hardware, not just simulators.
***Try Real Quantum Computer Right Now！***: [Getting a Key](https://www.tyxonq.com/) to register and obtain your API key
Innovatively combining generative AI, heterogeneous computing architectures, TyxonQ delivers end-to-end solutions for quantum chemistry, drug discovery, and materials science.
## 🏗️ Quantum-Classical Hybrid Architecture
TyxonQ implements a comprehensive quantum-classical hybrid workflow that bridges high-level quantum algorithms to executable quantum programs:
```mermaid
graph TB
subgraph "Real Problems"
A[Quantum Algorithm] --> B[Circuit Structure]
end
subgraph "Quantum Circuit Design"
B --> C[Sampling<br/>Heuristic Algorithm<br/>RL<br/>Machine Learning]
C --> D[Unitary Matrix]
D --> E[Logic Circuit Synthesis<br/>Logic Circuit Optimization]
E --> F[Logic Circuit]
end
subgraph "Quantum Compilation"
F --> G[Gate Count<br/>Circuit Depth<br/>Execution Time<br/>Fidelity]
G --> H[Dynamic Programming<br/>Heuristic Algorithm<br/>Reduction<br/>Machine Learning]
end
subgraph "Hardware Design"
H --> I[Qubit Mapping<br/>Qubit Routing]
I --> J[Executable Program<br/>Homebrew_S2]
end
style J fill:#e1f5fe
style A fill:#f3e5f5
```
### Architecture Components:
- **🧮 Quantum Algorithm Layer**: High-level quantum algorithm specification
- **🔄 Circuit Structure**: Parameterized quantum circuits with rotation parameters
- **⚙️ Logic Circuit Synthesis**: Automated circuit optimization and compilation
- **🎯 Qubit Mapping**: Physical qubit topology-aware mapping and routing
- **💻 Hardware Execution**: Direct execution on **Homebrew_S2** quantum processor
## Features
### 🔥 Real Quantum Hardware Integration
- **Production-Ready Quantum Execution**: Direct integration with **QureGenAI's Homebrew_S2** quantum processor
- **Pulse-Level Control**: Support for both gate-level operations and **pulse-level signals** for advanced quantum control
- **Real-Time Quantum Computing**: Execute your quantum algorithms on actual quantum hardware with low latency
- **Quantum-Classical Hybrid Workflows**: Seamlessly combine classical preprocessing with quantum execution
### 🚀 Upcoming API & MCP Services (Coming Soon)
- **🔗 Quantum API Gateway**: RESTful APIs for direct quantum hardware access
- **🤖 LLM Integration**: Model Control Protocol (MCP) services for large language model integration
- **☁️ Quantum Cloud Services**: Scalable quantum computing as a service
- **📊 Real-time Monitoring**: Quantum job monitoring and result analytics
### Unified Quantum-Classical Hybrid Computing Paradigm
- Supports efficient simulation and optimization of variational quantum algorithms (VQE, QAOA), featuring a built-in automatic differentiation engine for seamless integration with PyTorch/TensorFlow gradient computation workflows.
- Provides a hybrid task scheduler that dynamically allocates quantum hardware and classical computing resources (CPU/GPU) for acceleration.
### Multi-Level Hardware Support
- **Direct Quantum Hardware Integration**: Compatible with mainstream quantum processors (e.g., superconducting), supporting low-level control from gate-level operations to **pulse-level signals** :fire: :fire: :fire:.
- **Heterogeneous Computing Optimization**: Enhances simulation throughput via GPU vectorization and quantum instruction compilation.
### Generative AI Integration
- Built-in [Generative Quantum Eigensolver (GQE)](https://arxiv.org/abs/2401.09253) and [Quantum Machine Learning (QML)](https://arxiv.org/abs/2502.01146) modules for direct pre-trained model deployment in tasks like molecular structure generation and protein folding computing.
- Supports large language model (LLM) interaction, enabling automated "natural language → quantum circuit" generation (experimental feature).
### Domain-Specific Toolkits
- **Quantum Chemistry Suite**: Includes molecular Hamiltonian builders and electronic structure analysis tools, compatible with classical quantum chemistry and drug discovery framework like [PySCF](https://pyscf.org/), [ByteQC](https://github.com/bytedance/byteqc) and [OpenMM](https://openmm.org/).
- **Materials Simulation Library**: Integrates quantum-accelerated density functional theory (DFT) modules for predicting novel material band structures.
## 🚀 Roadmap & Development Status
### ✅ Current Features (v1.x)
- [x] Quantum circuit simulation and optimization
- [x] **Real quantum hardware execution** (Homebrew_S2)
- [x] Automatic differentiation engine
- [x] Multi-backend support (NumPy, PyTorch, TensorFlow, JAX)
- [ ] Variational quantum algorithms (VQE,GQE,QAOA)
- [ ] Quantum chemistry toolkit integration
### 🔄 In Progress (v2.x)
- [ ] **Quantum API Gateway** - RESTful APIs for quantum hardware access
- [ ] **MCP Services** - Large language model integration protocols
- [ ] Advanced quantum error correction protocols
- [ ] Enhanced pulse-level control interface
- [ ] Real-time quantum job monitoring dashboard
- [ ] Quantum circuit optimization using machine learning
### 🎯 Future Plans (v3.x+)
- [ ] **Multi-QPU Support** - Support for additional quantum processors
- [ ] **Quantum Networking** - Distributed quantum computing capabilities
- [ ] **Advanced QML Models** - Pre-trained quantum machine learning models
- [ ] **Natural Language Interface** - "English → Quantum Circuit" generation
- [ ] **Quantum Advantage Benchmarks** - Standardized performance metrics
- [ ] **Enterprise Cloud Platform** - Scalable quantum computing infrastructure
### 🧪 Experimental Features
- [ ] Quantum generative adversarial networks (QGANs)
- [ ] Quantum federated learning protocols
- [ ] Quantum-enhanced drug discovery pipelines
- [ ] Materials discovery acceleration frameworks
## Installation
The package now is written in pure Python and can be obtained via `pip` or
Install from source:
```bash
uv build
uv pip install dist/tyxonq-0.1.1-py3-none-any.whl
```
`uv` as:
```bash
pip install tyxonq
```
or
```bash
uv pip install tyxonq
```
or you can install it from github:
```bash
git clone https://github.com/QureGenAI-Biotech/TyxonQ.git
cd tyxonq
pip install --editable .
```
## Get Started Example
See examples/Get_Started_Demo.ipynb
## 🔑 Real Quantum Hardware Setup
### Getting API Access
1. **Apply for API Key**: Visit [TyxonQ Quantum AI Portal](https://www.tyxonq.com/) to register and obtain your API key
2. **Hardware Access**: Request access to **Homebrew_S2** quantum processor through API [TyxonQ QPU API](https://www.tyxonq.com)
### Configuration
Set up your API credentials:
```python
import tyxonq as tq
from tyxonq.cloud import apis
import getpass
# Configure quantum hardware access
API_KEY = getpass.getpass("Input your TyxonQ API_KEY:")
apis.set_token(API_KEY) # Get from https://www.tyxonq.com
```
### Real Hardware Example
See 'examples/simple_demo_1.py' , run:
```shell
python examples/simple_demo_1.py
```
Code:
```python
import sys
sys.path.append("./")
import tyxonq as tq
import getpass
from tyxonq.cloud import apis
import time
# Configure for real quantum hardware
apis.set_token(getpass.getpass("Input your TyxonQ API_KEY: "))
provider = "tyxonq"
device = "homebrew_s2"
# Create and execute quantum circuit on real hardware
def quantum_hello_world():
c = tq.Circuit(2)
c.H(0) # Hadamard gate on qubit 0
c.CNOT(0, 1) # CNOT gate between qubits 0 and 1
c.rx(1, theta=0.2) # Rotation around x-axis
# Execute on real quantum hardware
print("Submit task to TyxonQ")
task = apis.submit_task(provider = provider,
device = device,
circuit = c,
shots = 100)
print(f"Task submitted: {task}")
print("Wait 20 seconds to get task details")
time.sleep(20)
print(f"Real quantum hardware result: {task.details()}")
quantum_hello_world()
```
## Basic Usage and Guide
Considering that the features and documentation related to TyxonQ characteristics are currently under development, you can refer to the upstream library [Tensorcircuit](https://github.com/tencent-quantum-lab/tensorcircuit) for usage guidance in the interim: [Quick Start](https://github.com/tencent-quantum-lab/tensorcircuit/blob/master/docs/source/quickstart.rst) and [full documentation](https://tensorcircuit.readthedocs.io/). We will promptly update the TyxonQ documentation and tutorials in [English](), [Chinese]() and [Japanese]().
- Circuit manipulation:
```python
import tyxonq as tq
c = tq.Circuit(2)
c.H(0)
c.CNOT(0,1)
c.rx(1, theta=0.2)
print(c.wavefunction())
print(c.expectation_ps(z=[0, 1]))
print(c.sample(allow_state=True, batch=1024, format="count_dict_bin"))
```
- Runtime behavior customization:
```python
tq.set_backend("tensorflow")
tq.set_dtype("complex128")
tq.set_contractor("greedy")
```
- Automatic differentiations with jit:
```python
def forward(theta):
c = tq.Circuit(2)
c.R(0, theta=theta, alpha=0.5, phi=0.8)
return tq.backend.real(c.expectation((tq.gates.z(), [0])))
g = tq.backend.grad(forward)
g = tq.backend.jit(g)
theta = tq.array_to_tensor(1.0)
print(g(theta))
```
## Dependencies
- Python >= 3.7 (supports Python 3.7, 3.8, 3.9, 3.10, 3.11, 3.12+)
- PyTorch >= 1.8.0
## 📧 Contact & Support
- **Home**: [www.tyxonq.com](https://www.tyxonq.com)
- **Technical Support**: [code@quregenai.com](mailto:code@quregenai.com)
- **General Inquiries**: [bd@quregenai.com](mailto:bd@quregenai.com)
- **Documentation (beta version)**: [docs.tyxonq.com](https://tensorcircuit.readthedocs.io/)
- **Issue**:[github issue](https://github.com/QureGenAI-Biotech/TyxonQ/issues)
### Development Team
- **QureGenAI**: Quantum hardware infrastructure and services
- **TyxonQ Core Team**: Framework development and optimization
- **Community Contributors**: Open source development and testing
## License
TyxonQ is open source, released under the Apache License, Version 2.0.
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"description": "<h2><p align=\"center\">TyxonQ</p></h2>\n<h3><p align=\"center\">Full-stack Quantum Software Framework on Real Machine</p></h3>\n\n[](https://opensource.org/licenses/Apache-2.0)\n[](https://www.python.org/downloads/)\n[](https://www.tyxonq.com/)\n\nTyxonQ\u200b\u200b \u592a\u7384\u91cf\u5b50 is a full-stack quantum software framework for quantum simulation, optimization, and quantum machine learning. Forked from the open-source project \u200b[\u200bTensorCircuit](https://github.com/tencent-quantum-lab/tensorcircuit)\u200b\u200b and licensed under Apache License 2.0, it integrates modern quantum programming paradigms including automatic differentiation, just-in-time compilation, and hardware acceleration. \n\n**\ud83d\ude80 REAL QUANTUM HARDWARE READY**: TyxonQ supports **real quantum machine execution** through our quantum cloud services powered by **QureGenAI**. Currently featuring the **Homebrew_S2** quantum processor, enabling you to run your quantum algorithms on actual quantum hardware, not just simulators.\n\n***Try Real Quantum Computer Right Now\uff01***: [Getting a Key](https://www.tyxonq.com/) to register and obtain your API key\n\nInnovatively combining generative AI, heterogeneous computing architectures, TyxonQ delivers \u200b\u200bend-to-end solutions\u200b\u200b for quantum chemistry, drug discovery, and materials science.\n\n## \ud83c\udfd7\ufe0f Quantum-Classical Hybrid Architecture\n\nTyxonQ implements a comprehensive quantum-classical hybrid workflow that bridges high-level quantum algorithms to executable quantum programs:\n\n```mermaid\ngraph TB\n subgraph \"Real Problems\"\n A[Quantum Algorithm] --> B[Circuit Structure]\n end\n \n subgraph \"Quantum Circuit Design\"\n B --> C[Sampling<br/>Heuristic Algorithm<br/>RL<br/>Machine Learning]\n C --> D[Unitary Matrix]\n D --> E[Logic Circuit Synthesis<br/>Logic Circuit Optimization]\n E --> F[Logic Circuit]\n end\n \n subgraph \"Quantum Compilation\"\n F --> G[Gate Count<br/>Circuit Depth<br/>Execution Time<br/>Fidelity]\n G --> H[Dynamic Programming<br/>Heuristic Algorithm<br/>Reduction<br/>Machine Learning]\n end\n \n subgraph \"Hardware Design\"\n H --> I[Qubit Mapping<br/>Qubit Routing]\n I --> J[Executable Program<br/>Homebrew_S2]\n end\n \n style J fill:#e1f5fe\n style A fill:#f3e5f5\n```\n\n### Architecture Components:\n- **\ud83e\uddee Quantum Algorithm Layer**: High-level quantum algorithm specification\n- **\ud83d\udd04 Circuit Structure**: Parameterized quantum circuits with rotation parameters\n- **\u2699\ufe0f Logic Circuit Synthesis**: Automated circuit optimization and compilation\n- **\ud83c\udfaf Qubit Mapping**: Physical qubit topology-aware mapping and routing\n- **\ud83d\udcbb Hardware Execution**: Direct execution on **Homebrew_S2** quantum processor\n\n## Features\n\n### \ud83d\udd25 Real Quantum Hardware Integration\n- **Production-Ready Quantum Execution**: Direct integration with **QureGenAI's Homebrew_S2** quantum processor\n- **Pulse-Level Control**: Support for both gate-level operations and **pulse-level signals** for advanced quantum control\n- **Real-Time Quantum Computing**: Execute your quantum algorithms on actual quantum hardware with low latency\n- **Quantum-Classical Hybrid Workflows**: Seamlessly combine classical preprocessing with quantum execution\n\n### \ud83d\ude80 Upcoming API & MCP Services (Coming Soon)\n- **\ud83d\udd17 Quantum API Gateway**: RESTful APIs for direct quantum hardware access\n- **\ud83e\udd16 LLM Integration**: Model Control Protocol (MCP) services for large language model integration\n- **\u2601\ufe0f Quantum Cloud Services**: Scalable quantum computing as a service\n- **\ud83d\udcca Real-time Monitoring**: Quantum job monitoring and result analytics\n\n### Unified Quantum-Classical Hybrid Computing Paradigm\u200b\u200b\n- Supports efficient simulation and optimization of variational quantum algorithms (\u200b\u200bVQE, QAOA\u200b\u200b), featuring a built-in \u200b\u200bautomatic differentiation engine\u200b\u200b for seamless integration with PyTorch/TensorFlow gradient computation workflows.\n- Provides a \u200b\u200bhybrid task scheduler\u200b\u200b that dynamically allocates quantum hardware and classical computing resources (CPU/GPU) for acceleration\u200b\u200b.\n\n### Multi-Level Hardware Support\u200b\u200b\n\u200b\u200b- **Direct Quantum Hardware Integration\u200b\u200b**: Compatible with mainstream quantum processors (e.g., superconducting), supporting low-level control from \u200b\u200bgate-level operations\u200b\u200b to **\u200b\u200bpulse-level signals** :fire: :fire: :fire:\u200b.\n- \u200b\u200b**Heterogeneous Computing Optimization\u200b\u200b**: Enhances simulation throughput via \u200b\u200bGPU vectorization\u200b\u200b and quantum instruction compilation.\n\n### Generative AI Integration\u200b\u200b\n- Built-in [Generative \u200bQuantum Eigensolver (GQE)](https://arxiv.org/abs/2401.09253)\u200b\u200b and [\u200b\u200bQuantum Machine Learning (QML)](\u200b\u200bhttps://arxiv.org/abs/2502.01146) modules for direct pre-trained model deployment in tasks like molecular structure generation and protein folding computing.\n- Supports \u200b\u200blarge language model (LLM) interaction\u200b\u200b, enabling automated \u200b\u200b\"natural language \u2192 quantum circuit\"\u200b\u200b generation (experimental feature).\n\n### Domain-Specific Toolkits\u200b\u200b\n- **Quantum Chemistry Suite\u200b\u200b**: Includes molecular Hamiltonian builders and electronic structure analysis tools, compatible with classical quantum chemistry and drug discovery framework like [PySCF](https://pyscf.org/), [ByteQC](https://github.com/bytedance/byteqc) and [\u200b\u200bOpenMM](https://openmm.org/)\u200b\u200b.\n- \u200b\u200b**Materials Simulation Library\u200b\u200b**: Integrates \u200b\u200bquantum-accelerated density functional theory (DFT)\u200b\u200b modules for predicting novel material band structures.\n\n## \ud83d\ude80 Roadmap & Development Status\n\n### \u2705 Current Features (v1.x)\n- [x] Quantum circuit simulation and optimization\n- [x] **Real quantum hardware execution** (Homebrew_S2)\n- [x] Automatic differentiation engine\n- [x] Multi-backend support (NumPy, PyTorch, TensorFlow, JAX)\n- [ ] Variational quantum algorithms (VQE,GQE,QAOA)\n- [ ] Quantum chemistry toolkit integration\n\n### \ud83d\udd04 In Progress (v2.x)\n- [ ] **Quantum API Gateway** - RESTful APIs for quantum hardware access\n- [ ] **MCP Services** - Large language model integration protocols \n- [ ] Advanced quantum error correction protocols\n- [ ] Enhanced pulse-level control interface\n- [ ] Real-time quantum job monitoring dashboard\n- [ ] Quantum circuit optimization using machine learning\n\n### \ud83c\udfaf Future Plans (v3.x+)\n- [ ] **Multi-QPU Support** - Support for additional quantum processors\n- [ ] **Quantum Networking** - Distributed quantum computing capabilities\n- [ ] **Advanced QML Models** - Pre-trained quantum machine learning models\n- [ ] **Natural Language Interface** - \"English \u2192 Quantum Circuit\" generation\n- [ ] **Quantum Advantage Benchmarks** - Standardized performance metrics\n- [ ] **Enterprise Cloud Platform** - Scalable quantum computing infrastructure\n\n### \ud83e\uddea Experimental Features\n- [ ] Quantum generative adversarial networks (QGANs)\n- [ ] Quantum federated learning protocols\n- [ ] Quantum-enhanced drug discovery pipelines\n- [ ] Materials discovery acceleration frameworks\n\n## Installation\nThe package now is written in pure Python and can be obtained via `pip` or \n\nInstall from source:\n\n```bash\nuv build\nuv pip install dist/tyxonq-0.1.1-py3-none-any.whl\n```\n\n`uv` as:\n```bash\npip install tyxonq\n```\nor\n```bash\nuv pip install tyxonq\n```\nor you can install it from github:\n```bash\ngit clone https://github.com/QureGenAI-Biotech/TyxonQ.git\ncd tyxonq\npip install --editable .\n```\n\n## Get Started Example\n\nSee examples/Get_Started_Demo.ipynb\n\n## \ud83d\udd11 Real Quantum Hardware Setup\n\n### Getting API Access\n1. **Apply for API Key**: Visit [TyxonQ Quantum AI Portal](https://www.tyxonq.com/) to register and obtain your API key\n2. **Hardware Access**: Request access to **Homebrew_S2** quantum processor through API [TyxonQ QPU API](https://www.tyxonq.com)\n\n### Configuration\nSet up your API credentials:\n\n```python\nimport tyxonq as tq\nfrom tyxonq.cloud import apis\nimport getpass\n\n# Configure quantum hardware access\nAPI_KEY = getpass.getpass(\"Input your TyxonQ API_KEY:\")\napis.set_token(API_KEY) # Get from https://www.tyxonq.com\n```\n\n### Real Hardware Example\n\nSee 'examples/simple_demo_1.py' , run:\n```shell\npython examples/simple_demo_1.py\n```\n\nCode:\n\n```python\nimport sys\nsys.path.append(\"./\")\n\nimport tyxonq as tq\nimport getpass\nfrom tyxonq.cloud import apis\nimport time\n# Configure for real quantum hardware\napis.set_token(getpass.getpass(\"Input your TyxonQ API_KEY: \"))\n\nprovider = \"tyxonq\"\ndevice = \"homebrew_s2\"\n\n# Create and execute quantum circuit on real hardware\ndef quantum_hello_world():\n c = tq.Circuit(2)\n c.H(0) # Hadamard gate on qubit 0\n c.CNOT(0, 1) # CNOT gate between qubits 0 and 1\n c.rx(1, theta=0.2) # Rotation around x-axis\n \n # Execute on real quantum hardware\n\n print(\"Submit task to TyxonQ\")\n\n task = apis.submit_task(provider = provider,\n device = device,\n circuit = c,\n shots = 100)\n print(f\"Task submitted: {task}\")\n print(\"Wait 20 seconds to get task details\")\n time.sleep(20)\n print(f\"Real quantum hardware result: {task.details()}\")\n\nquantum_hello_world()\n\n```\n\n## Basic Usage and Guide\nConsidering that the features and documentation related to \u200b\u200bTyxonQ characteristics\u200b\u200b are currently under development, you can refer to the upstream library \u200b\u200b[Tensorcircuit](https://github.com/tencent-quantum-lab/tensorcircuit)\u200b\u200b for usage guidance in the interim: [Quick Start](https://github.com/tencent-quantum-lab/tensorcircuit/blob/master/docs/source/quickstart.rst) and [full documentation](https://tensorcircuit.readthedocs.io/). We will promptly update the \u200b\u200bTyxonQ documentation and tutorials in [English](), [Chinese]() and [Japanese]()\u200b\u200b.\n\n- Circuit manipulation:\n```python\nimport tyxonq as tq\nc = tq.Circuit(2)\nc.H(0)\nc.CNOT(0,1)\nc.rx(1, theta=0.2)\nprint(c.wavefunction())\nprint(c.expectation_ps(z=[0, 1]))\nprint(c.sample(allow_state=True, batch=1024, format=\"count_dict_bin\"))\n```\n\n- Runtime behavior customization:\n```python\ntq.set_backend(\"tensorflow\")\ntq.set_dtype(\"complex128\")\ntq.set_contractor(\"greedy\")\n```\n\n- Automatic differentiations with jit:\n```python\ndef forward(theta):\n c = tq.Circuit(2)\n c.R(0, theta=theta, alpha=0.5, phi=0.8)\n return tq.backend.real(c.expectation((tq.gates.z(), [0])))\n\ng = tq.backend.grad(forward)\ng = tq.backend.jit(g)\ntheta = tq.array_to_tensor(1.0)\nprint(g(theta))\n```\n\n## Dependencies\n- Python >= 3.7 (supports Python 3.7, 3.8, 3.9, 3.10, 3.11, 3.12+)\n- PyTorch >= 1.8.0\n\n## \ud83d\udce7 Contact & Support\n\n- **Home**: [www.tyxonq.com](https://www.tyxonq.com)\n- **Technical Support**: [code@quregenai.com](mailto:code@quregenai.com)\n\n- **General Inquiries**: [bd@quregenai.com](mailto:bd@quregenai.com)\n- **Documentation (beta version)**: [docs.tyxonq.com](https://tensorcircuit.readthedocs.io/)\n- **Issue**:[github issue](https://github.com/QureGenAI-Biotech/TyxonQ/issues)\n\n### Development Team\n- **QureGenAI**: Quantum hardware infrastructure and services\n- **TyxonQ Core Team**: Framework development and optimization\n- **Community Contributors**: Open source development and testing\n\n## License\nTyxonQ is open source, released under the Apache License, Version 2.0.\n",
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JSON
