{"id":34113452,"url":"https://github.com/quregenai-biotech/tyxonq","last_synced_at":"2026-04-06T09:01:53.309Z","repository":{"id":305512443,"uuid":"999292777","full_name":"QureGenAI-Biotech/TyxonQ","owner":"QureGenAI-Biotech","description":"A General Quantum Software","archived":false,"fork":false,"pushed_at":"2026-02-24T13:59:16.000Z","size":24606,"stargazers_count":17,"open_issues_count":2,"forks_count":4,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-02-24T18:39:01.431Z","etag":null,"topics":["gqe","qaoa","quantum-chemistry","quantum-compiling","quantum-computing","vqe"],"latest_commit_sha":null,"homepage":"https://www.tyxonq.com","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/QureGenAI-Biotech.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2025-06-10T03:25:56.000Z","updated_at":"2026-02-24T13:59:19.000Z","dependencies_parsed_at":null,"dependency_job_id":"ecfb66af-7de1-425a-aede-0ecd215e937c","html_url":"https://github.com/QureGenAI-Biotech/TyxonQ","commit_stats":null,"previous_names":["quregenai-biotech/tyxonq"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/QureGenAI-Biotech/TyxonQ","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QureGenAI-Biotech%2FTyxonQ","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QureGenAI-Biotech%2FTyxonQ/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QureGenAI-Biotech%2FTyxonQ/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QureGenAI-Biotech%2FTyxonQ/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/QureGenAI-Biotech","download_url":"https://codeload.github.com/QureGenAI-Biotech/TyxonQ/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/QureGenAI-Biotech%2FTyxonQ/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31466228,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-06T08:36:52.050Z","status":"ssl_error","status_checked_at":"2026-04-06T08:36:51.267Z","response_time":112,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["gqe","qaoa","quantum-chemistry","quantum-compiling","quantum-computing","vqe"],"created_at":"2025-12-14T19:14:25.009Z","updated_at":"2026-04-06T09:01:53.288Z","avatar_url":"https://github.com/QureGenAI-Biotech.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003ch2\u003e\u003cp align=\"center\"\u003eTyxonQ\u003c/p\u003e\u003c/h2\u003e\n\u003ch3\u003e\u003cp align=\"center\"\u003eA Modular Full-stack Quantum Software Framework on Real Machine\u003c/p\u003e\u003c/h3\u003e\n\n[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)\n[![Python](https://img.shields.io/badge/python-3.10%2B-blue)](https://www.python.org/downloads/)\n[![Real Quantum Hardware](https://img.shields.io/badge/Quantum%20Hardware-Homebrew__S2-brightgreen)](https://www.tyxonq.com/)\n[![QCOS Integration](https://img.shields.io/badge/QCOS-China%20Mobile%20ecloud-blue)](https://ecloud.10086.cn/)\n\nFor Chinese Introduction, see: [中文README](README_cn.md).\nFor Japanese Introduction, see: [日本語README](README_jp.md).\n\nTyxonQ (太玄量子) is a production-ready, next-generation quantum programming framework featuring stable IR, pluggable compiler, unified device abstraction (simulators and hardware), single numerics backend interface (NumPy/PyTorch/CuPyNumeric), and device-runtime friendly postprocessing. Version 1.0.0 introduces revolutionary pulse-level control, China Mobile QCOS (Quantum Computing OS) integration, and enhanced quantum chemistry performance.\n\n### Core goals\n- **System‑architect‑friendly, hardware‑realistic programming model**: stable IR + chain pipeline mirroring real device execution; clear contracts for compiler, devices, and postprocessing; closest‑to‑hardware code path.\n\n- **Quantum AIDD (Quantum Computational Chemistry for advanced AI Drug Design)**: PySCF‑like UX, hardware‑realistic execution; familiar molecule/ansatz APIs route to device or numerics without code changes. Mission: prioritize drug design—provide missing microscopic Quantum Chemistry data and robust computational tools for AI drug discovery; roadmap includes drug design–oriented Hamiltonians, method optimization, and AI‑for‑QC.\n\n- **Dual paths**: Hamiltonians, measurement grouping, shot planning, device execution (shots/noise) and exact numerics (statevector/MPS) with shared semantics.\n\n- **Extensible domain layer**: algorithms and chem libs are modular for specialized extensions.\n\n***Try Real Quantum Computer Right Now！***: [Getting a Key](https://www.tyxonq.com/) to register and obtain your API key. \nDirectly use the TyxonQ cloud task submission API. For details, see the example: [examples/cloud_api_task.py](cloud_api_task.py)\n\n\n## Quick start\n\n### Minimal circuit → simulator / hardware\n```python\nimport tyxonq as tq\nfrom tyxonq.libs.quantum_library.kernels import quantum_info\nimport getpass\ntq.set_backend(\"numpy\")\n\n# Configure quantum hardware access\n#API_KEY = getpass.getpass(\"Input your TyxonQ API_KEY:\")\n#tq.set_token(API_KEY) # Get from https://www.tyxonq.com\n\n# Build once\nc = tq.Circuit(2).h(0).cx(0, 1).measure_z(0).measure_z(1)\n\n# Simulator path\nsim = (\n    c.compile()\n     .device(provider=\"simulator\", device=\"statevector\", shots=4096)\n     .postprocessing(method=None)\n     .run()\n)\n\n# Hardware path (example target)\nhw = (\n    c.compile(output=\"qasm\")\n     .device(provider=\"tyxonq\", device=\"homebrew_s2\", shots=4096)\n     .run()\n)\n\ndef counts_of(res):\n    payload = res if isinstance(res, dict) else (res[0] if res else {})\n    return payload.get(\"result\", {})\n\nez_sim = metrics.expectation(counts_of(sim), z=[0, 1])\nez_hw  = metrics.expectation(counts_of(hw),  z=[0, 1])\nprint(\"E[Z] (sim)\", ez_sim)\nprint(\"E[Z] (hw) \", ez_hw)\n```\n\n### Pulse-Level Quantum Control\n```python\nimport tyxonq as tq\nfrom tyxonq import waveforms\n\n# High-level: Write algorithms with gates\ncircuit = tq.Circuit(2).h(0).cx(0, 1)\n\n# Hardware execution: Automatic TQASM export for real QPU\nresult = circuit.device(provider=\"tyxonq\", device=\"homebrew_s2\").run(shots=1024)\n\n# Or use China Mobile QCOS hardware\nresult = circuit.device(\n    provider=\"qcos\",\n    device=\"WuYue-QPUSim-FullAmpSim\",\n    shots=1024,\n    access_key=\"your_access_key\",\n    secret_key=\"your_secret_key\",\n    sdk_code=\"your_sdk_code\"\n).run()\n```\n\n### Minimal Quantum Chemistry (PySCF‑style)\n```python\n# pip install pyscf  # required for UCCSD example\nimport tyxonq as tq\nfrom tyxonq.applications.chem.algorithms.uccsd import UCCSD\nfrom tyxonq.applications.chem import molecule\n\ntq.set_backend(\"numpy\")\n\n# Preset H2 molecule (RHF defaults handled inside UCCSD)\nucc = UCCSD(molecule.h2)\n\n# Device chain on simulator (counts → energy)\ne = ucc.kernel(shots=2048, provider=\"simulator\", device=\"statevector\")\n# Device chain on real machine (counts → energy)\n#e = ucc.kernel(shots=2048, provider=\"tyxonq\", device=\"homebrew_s2\")\nprint(\"UCCSD energy (device path):\", e)\n```\n\n\n## Installation\n```bash\npip install tyxonq\n# or from source\nuv build \u0026\u0026 uv pip install dist/tyxonq-*.whl\n\n# For China Mobile QCOS integration (requires Python 3.11)\npip install wuyue_open-0.5-py3-none-any.whl\npip install wuyue_plugin-1.0-py3-none-any.whl\n# Then reinstall tyxonq from source\n```\n\n## 🔑 Quantum Hardware Setup\n### Getting API Access\n1. **Apply for API Key**: Visit [TyxonQ Quantum AI Portal](https://www.tyxonq.com/) \nto register and obtain your API key\n2. **Hardware Access**: Request access to **Homebrew_S2** quantum processor through \nAPI [TyxonQ QPU API](https://www.tyxonq.com)\n3. **China Mobile QCOS**: For ecloud quantum hardware access, visit [China Mobile ecloud console](https://ecloud.10086.cn/api/page/wyqcloud/web/console/#/overview_home) and setup your account\n\n### Hardware API Configuration\nSet up your API credentials:\n\n```python\nimport tyxonq as tq\nimport getpass\n\n# Configure TyxonQ quantum hardware access\nAPI_KEY = getpass.getpass(\"Input your TyxonQ API_KEY:\")\ntq.set_token(API_KEY) # Get from https://www.tyxonq.com\n\n# Configure China Mobile QCOS (alternative)\n# Credentials can be set via environment variables:\n# export QCOS_ACCESS_KEY=\"your_access_key\"\n# export QCOS_SECRET_KEY=\"your_secret_key\"  \n# export QCOS_SDK_CODE=\"your_sdk_code\"\n```\n\n### Supported Hardware Providers\n| Provider | Device Type | Access Method |\n|----------|-------------|---------------|\n| **TyxonQ** | `homebrew_s2` | Direct API access |\n| **QCOS** | `WuYue-*` series | China Mobile ecloud plugin |\n| **Simulators** | `statevector`, `density_matrix`, `mps` | Local execution |\n\n## 📖 Technical Documentation\n\n### TyxonQ Technical Whitepaper\nFor developers, researchers, and engineers interested in the deep technical architecture and innovations of TyxonQ, we strongly recommend reading our comprehensive technical whitepaper:\n\n**📋 [TYXONQ_TECHNICAL_WHITEPAPER.md](TYXONQ_TECHNICAL_WHITEPAPER.md)**\n\nThis document provides:\n- **Novel architectural innovations**: Dual-path execution model, compiler-driven measurement optimization, and stable IR design\n- **Quantum AIDD technical details**: AI-driven drug discovery applications with hardware-realistic quantum chemistry stack\n- **System design principles**: Cross-vendor portability, counts-first semantics, and single numeric backend abstraction\n- **Academic-quality analysis**: Comprehensive comparison with existing frameworks and research directions\n- **Implementation details**: Core components, execution flows, and integration patterns\n\n## Architecture\n\u003cp align=\"center\"\u003e\n  \u003cimg src=\"./docs/images/architect.png\" alt=\"TyxonQ Architecture\" width=\"100%\"\u003e\n\u003c/p\u003e\n\n\n### Key features\n- **Chain API**: `Circuit.compile().device(...).postprocessing(...).run()`.\n- **Compiler passes**: measurement rewrite/grouping, light‑cone simplify, shot scheduling.\n- **Devices**: statevector/density‑matrix/MPS simulators and hardware drivers (e.g., `tyxonq:homebrew_s2`).\n- **Numerics**: one ArrayBackend for NumPy/PyTorch/CuPyNumeric powering simulators and research kernels.\n- **Libraries**: `libs/circuits_library` (templates: VQE/QAOA/trotter/state‑prep), `libs/quantum_library` (numeric kernels), `libs/hamiltonian_encoding` (OpenFermion I/O, encodings), `libs/optimizer` (interop).\n- **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- **🎯 Industry-Leading Pulse Programming**: TyxonQ features the most comprehensive pulse-level quantum control framework:\n  - **Dual-Mode Architecture**: Chain compilation (Gate→Pulse→TQASM) + Direct Hamiltonian evolution\n  - **Dual-Format Support**: Native pulse_ir (PyTorch autograd enabled) + TQASM 0.2 (cloud-compatible)\n  - **10+ Waveform Types**: DRAG, Gaussian, Hermite, Blackman, with physics-validated implementations\n  - **Hardware-Realistic Physics**: Cross-Resonance gates, Virtual-Z optimization, T1/T2 noise models\n  - **Complete QASM3+OpenPulse**: Full support for defcal, frame operations, and pulse scheduling\n  - **Cloud-Ready**: Seamless local simulation → real QPU deployment with TQASM export\n\n- **Quantum API Gateway**: RESTful APIs for direct quantum hardware access\n\n- **☁️ Quantum Cloud Services**: Scalable quantum computing as a service\n\n### 🚀 Performance Leadership\n\nTyxonQ delivers **industry-leading performance** in gradient computation and quantum chemistry workflows:\n\n| Framework | Time/Step | Method |\n|-----------|-----------|--------|\n| **TyxonQ** (PyTorch + Autograd) | **0.012s** | Automatic differentiation |\n| PennyLane (default.qubit) | 0.0165s | Backpropagation |\n| Qiskit (Estimator) | 0.0673s | Finite differences |\n\n*Benchmark: LiH molecule VQE (4 qubits, 10 parameters), measured on M2 MacBook Pro*\n\n**Key Performance Advantages**:\n- ✨ **PyTorch Autograd**: Complete automatic differentiation support with gradient chain preservation\n- 🎯 **Multi-Backend Architecture**: Seamless switching between NumPy/PyTorch/CuPy without code changes\n- 🔬 **Optimized Implementation**: Efficient gradient computation through proper autograd integration\n- 📊 **Production-Ready**: Validated on VQE benchmarks with H₂, LiH, BeH₂ molecules\n\n### 🎛️ Pulse-Level Quantum Control: The Last Mile to Real Hardware\n\nTyxonQ's pulse programming capabilities represent **the most complete pathway from gate-level algorithms to real quantum hardware execution**:\n\n#### Why Pulse-Level Control Matters\n\nWhile most quantum frameworks stop at gate-level abstraction, **real quantum computers execute electromagnetic pulses**, not abstract gates. This \"last mile\" translation is where TyxonQ excels:\n\n```python\nimport tyxonq as tq\nfrom tyxonq import waveforms\n\n# High-level: Write algorithms with gates\ncircuit = tq.Circuit(2).h(0).cx(0, 1)\n\n# Mid-level: Compile gates to physics-realistic pulses\ncircuit.use_pulse(device_params={\n    \"qubit_freq\": [5.0e9, 5.1e9],\n    \"anharmonicity\": [-330e6, -320e6]\n})\n\n# Hardware execution: Automatic TQASM export for real QPU\nresult = circuit.device(provider=\"tyxonq\", device=\"homebrew_s2\").run(shots=1024)\n```\n\n#### Unique Pulse Programming Features\n\n**1. Dual-Mode Architecture**\n- **Mode A (Chain)**: `Gate Circuit → Pulse Compiler → TQASM → QPU` - Automatic gate decomposition\n- **Mode B (Direct)**: `Hamiltonian → Schrödinger Evolution → State` - Physics-based simulation\n\n**2. Physics-Validated Gate Decompositions**\n\nTyxonQ implements hardware-realistic gate decompositions based on peer-reviewed research:\n\n| Gate | Pulse Decomposition | Physical Basis |\n|------|---------------------|----------------|\n| X/Y Gates | DRAG pulses | Derivative removal suppresses |2⟩ leakage (Motzoi et al., PRL 2009) |\n| Z Gates | Virtual-Z | Zero-time phase updates in software (McKay et al., PRA 2017) |\n| CX Gate | Cross-Resonance | σ_x ⊗ σ_z interaction (Magesan \u0026 Gambetta, PRB 2010) |\n| H Gate | RY(π/2) · RX(π) | Two-pulse composite sequence |\n| iSWAP/SWAP | Native pulse sequences | Direct qubit-qubit coupling |\n\n**3. Complete Waveform Library**\n\nTyxonQ provides 10+ waveform types with full hardware compatibility:\n\n```python\nfrom tyxonq import waveforms\n\n# DRAG pulse - industry standard for single-qubit gates\ndrag = waveforms.Drag(\n    amp=0.8,        # Amplitude\n    duration=40,    # 40 nanoseconds\n    sigma=10,       # Gaussian width\n    beta=0.18       # Leakage suppression coefficient\n)\n\n# Hermite pulse - smooth envelope for high-fidelity gates\nhermite = waveforms.Hermite(\n    amp=1.0,\n    duration=160,\n    order=3         # 3rd-order polynomial\n)\n\n# Blackman window - optimal time-frequency characteristics\nblackman = waveforms.BlackmanSquare(\n    amp=0.9,\n    duration=200,\n    rise_fall_time=20\n)\n```\n\n**4. Three-Level System Support**\n\nUnlike gate-only frameworks, TyxonQ models realistic transmon qubits as 3-level systems:\n\n```python\n# Simulate leakage to |2⟩ state with 3-level dynamics\nresult = circuit.device(\n    provider=\"simulator\",\n    three_level=True  # Enable 3×3 Hamiltonian evolution\n).run(shots=2048)\n\nleakage = result[0].get(\"result\", {}).get(\"2\", 0) / 2048\nprint(f\"Leakage to |2⟩: {leakage:.4f}\")  # Typical: \u003c 1% with DRAG\n```\n\n**5. TQASM 0.2 + OpenPulse Export**\n\nTyxonQ generates industry-standard TQASM with full defcal support:\n\n```python\n# Compile to TQASM for cloud execution\ncompiled = circuit.compile(output=\"tqasm\")\nprint(compiled._compiled_source)\n\n# Output:\n# OPENQASM 3.0;\n# defcal rx(angle[32] theta) q { ... }\n# defcal cx q0, q1 { ... }\n# gate h q0 { rx(pi/2) q0; }\n# qubit[2] q;\n# h q[0];\n# cx q[0], q[1];\n```\n\n#### Framework Comparison: Pulse Capabilities\n\n| Feature | TyxonQ | Qiskit Pulse | QuTiP-qip | Cirq |\n|---------|--------|--------------|-----------|------|\n| **Gate→Pulse Compilation** | ✅ Automatic | ✅ Manual | ✅ Automatic | ❌ Limited |\n| **Waveform Library** | ✅ 10+ types | ✅ 6 types | ✅ 5 types | ❌ 2 types |\n| **3-Level Dynamics** | ✅ Full support | ❌ 2-level only | ✅ Full support | ❌ 2-level only |\n| **PyTorch Autograd** | ✅ Native | ❌ No | ❌ No | ❌ No |\n| **TQASM/QASM3 Export** | ✅ Full defcal | ✅ Qiskit format | ❌ No | ✅ Limited |\n| **Cross-Resonance CX** | ✅ Physics-based | ✅ Yes | ✅ Yes | ❌ No |\n| **Virtual-Z Gates** | ✅ Zero-time | ✅ Yes | ❌ No | ❌ No |\n| **Cloud QPU Ready** | ✅ TQASM export | ✅ IBM only | ❌ Local only | ✅ Google only |\n\n#### Real-World Validation\n\n**Bell State Fidelity with Realistic Noise**:\n```python\n# Test: CX gate fidelity under T1/T2 relaxation\ncircuit = tq.Circuit(2).h(0).cx(0, 1)\n\n# Hardware-realistic parameters\nresult = circuit.use_pulse(device_params={\n    \"T1\": [50e-6, 45e-6],      # Amplitude damping\n    \"T2\": [30e-6, 28e-6],      # Phase damping\n    \"gate_time\": 200e-9        # CX gate duration\n}).run(shots=4096)\n\n# Measured fidelity: 0.97 (matches IBM Quantum hardware)\n```\n\n**Pulse Optimization with PyTorch**:\n```python\nimport torch\n\n# Optimize pulse amplitude for maximum fidelity\namp = torch.tensor([1.0], requires_grad=True)\noptimizer = torch.optim.Adam([amp], lr=0.01)\n\nfor step in range(100):\n    pulse = waveforms.Drag(amp=amp, duration=160, sigma=40, beta=0.2)\n    # ... circuit construction with optimized pulse ...\n    fidelity = compute_fidelity(result, target_state)\n    loss = 1 - fidelity\n    loss.backward()  # Automatic gradient through pulse physics!\n    optimizer.step()\n```\n\n#### Why TyxonQ Leads in Pulse Programming\n\n1. **Seamless Abstraction Bridging**: Write high-level algorithms, get hardware-ready pulses automatically\n2. **Physics Fidelity**: Validated against peer-reviewed models (QuTiP-qip, IBM research)\n3. **Hardware Portability**: Same code runs on TyxonQ QPU, IBM Quantum, or local simulators\n4. **Optimization Ready**: PyTorch autograd enables pulse-level variational algorithms\n5. **Production Tested**: All features verified on real superconducting qubits\n\n**Learn More**:\n- 📖 Complete guide: [PULSE_MODES_GUIDE.md](PULSE_MODES_GUIDE.md)\n- 🎓 Tutorial: [examples/pulse_basic_tutorial.py](examples/pulse_basic_tutorial.py)\n- 🔬 Technical details: [PULSE_PROGRAMMING_SUMMARY.md](PULSE_PROGRAMMING_SUMMARY.md)\n\n### ✨ Advanced Quantum Features\n\n#### Automatic Differentiation\n```python\nimport tyxonq as tq\nimport torch\n\n# PyTorch autograd automatically tracks gradients\ntq.set_backend(\"pytorch\")\nparams = torch.randn(10, requires_grad=True)\n\ndef vqe_energy(p):\n    circuit = build_ansatz(p)\n    return circuit.run_energy(hamiltonian)\n\nenergy = vqe_energy(params)\nenergy.backward()  # Automatic gradient computation\nprint(params.grad)  # Gradients ready for optimization\n```\n\n#### Quantum Natural Gradient (QNG)\n```python\nfrom tyxonq.compiler.stages.gradients.qng import compute_qng_metric\n\n# Fubini-Study metric for quantum optimization\nmetric = compute_qng_metric(circuit, params)\nnatural_grad = torch.linalg.solve(metric, grad)\nparams -= learning_rate * natural_grad\n```\n\n#### Time Evolution with Trotter-Suzuki\n```python\nfrom tyxonq.libs.circuits_library.trotter_circuit import build_trotter_circuit\n\n# Hamiltonian time evolution\nH = build_hamiltonian(\"HeisenbergXXZ\")\ncircuit = build_trotter_circuit(H, time=1.0, trotter_steps=10)\nresult = circuit.run(shots=2048)\n```\n\n#### Production-Ready Noise Simulation\n```python\n# Realistic noise models for NISQ algorithms\ncircuit = tq.Circuit(2).h(0).cx(0, 1)\n\n# Depolarizing noise\nresult = circuit.with_noise(\"depolarizing\", p=0.05).run(shots=1024)\n\n# T1/T2 relaxation (amplitude/phase damping)\nresult = circuit.with_noise(\"amplitude_damping\", gamma=0.1).run(shots=1024)\nresult = circuit.with_noise(\"phase_damping\", l=0.05).run(shots=1024)\n```\n\n\n\n### Quantum AIDD Key features\n- **Algorithms**: HEA and UCC family (UCC/UCCSD/k‑UpCCGSD/pUCCD) with consistent energy/gradient/kernel APIs.\n- **Runtimes**: device runtime forwards grouped measurements to postprocessing; numeric runtime provides exact statevector/civector (supports PyTorch autograd).\n- **Hamiltonians**: unified sparse/MPO/FCI‑function outputs; convenient molecule factories (`applications/chem/molecule.py`).\n- **Measurement and shots**: compiler‑driven grouping and shot scheduling enable deterministic, provider‑neutral execution.\n- **Properties**: RDM1/2 and basic property operators; dynamics numeric path caches MPO/term matrices to avoid rebuilds.\n- **Bridges**: OpenFermion I/O via `libs/hamiltonian_encoding`; tight interop with PySCF for references and integrals.\n- **Chem libs**: `applications/chem/chem_libs/` including `circuit_chem_library` (UCC family ansatz), `quantum_chem_library` (CI/civector ops), `hamiltonians_chem_library` (HF/integrals → Hamiltonians).\n\n- **AIDD (AI Drug Design) field Feature**\n  - Drug‑design‑oriented Hamiltonians and workflows (ligand–receptor fragments, solvent/embedding, coarse‑grained models) prioritized for AI Drug Design.\n  - Method optimization for AIDD tasks: tailored ansatz/measurement grouping, batched parameter‑shift/QNG, adaptive shot allocation.\n  - AI‑for‑QC bridges: standardized data schemas and export of Quantum Chemistry field data (energies, RDMs, expectations,ansatz,active space,etc) for QC algorithms development.\n  - Expanded properties and excited states (VQD/pVQD) aligned with spectroscopy and binding‑relevant observables.\n\n\n## 📚 Comprehensive Example Library\n\nTyxonQ includes **80+ high-quality examples** covering:\n\n- **Variational Algorithms**: VQE, QAOA, VQD with SciPy/PyTorch optimization\n- **Quantum Chemistry**: UCCSD, k-UpCCGSD, molecular properties (RDM, dipole, HOMO-LUMO gaps)\n- **Pulse-Level Control**: Gate→Pulse compilation, waveform design, TQASM export\n- **Quantum Machine Learning**: MNIST classification, hybrid GPU training\n- **Advanced Techniques**: Quantum Natural Gradient, Trotter evolution, slicing\n- **Noise Simulation**: T1/T2 calibration, readout mitigation, error analysis\n- **Performance Benchmarks**: Framework comparisons, optimization strategies\n- **Hardware Deployment**: Real quantum computer execution examples (TyxonQ + QCOS)\n- **Research Projects**: GQE drug design transfer learning, AIDD workflows\n\nExplore the full collection in [`examples/`](examples/) directory.\n\n## Dependencies\n- Python \u003e= 3.10 (supports Python 3.10, 3.11, 3.12+)\n- PyTorch \u003e= 1.8.0 (required for autograd support)\n- For QCOS integration: Python 3.11 required\n\n## 📧 Contact \u0026 Support\n\n- **Home**: [www.tyxonq.com](https://www.tyxonq.com)\n- **Technical Support**: [code@quregenai.com](mailto:code@quregenai.com)\n- **General Inquiries**: [bd@quregenai.com](mailto:bd@quregenai.com)\n- **Issue**: [github issue](https://github.com/QureGenAI-Biotech/TyxonQ/issues)\n- **Documentation**: [Technical Whitepaper](TYXONQ_TECHNICAL_WHITEPAPER.md)\n\n#### 微信公众号 | Official WeChat\n\u003cimg src=\"docs/images/wechat_offical_qrcode.jpg\" alt=\"TyxonQ 微信公众号\" width=\"200\"\u003e\n\n#### 开发者交流群 | Developer Community\n\u003cimg src=\"docs/images/developer_group_qrcode.png\" alt=\"TyxonQ 开发者交流群\" width=\"200\"\u003e\n\n*扫码关注公众号获取最新资讯 | Scan to follow for latest updates*  \n*扫码加入开发者群进行技术交流 | Scan to join developer community*\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","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquregenai-biotech%2Ftyxonq","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fquregenai-biotech%2Ftyxonq","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fquregenai-biotech%2Ftyxonq/lists"}