update readme and add doc

Author: refraction-ray

README.md

@@ -27,15 +27,15 @@
 
 TensorCircuit-NG is an open-source high-performance quantum software framework, supporting for automatic differentiation, just-in-time compiling, hardware acceleration, and vectorized parallelism, providing unified infrastructures and interfaces for quantum programming. It can compose quantum circuits, neural networks and tensor networks seamlessly with high simulation efficiency and flexibility.
 
-TensorCircuit-NG is built on top of modern machine learning frameworks: Jax, TensorFlow, and PyTorch. It is specifically suitable for large-scale simulations of quantum-classical hybrid paradigm and variational quantum algorithms in ideal, noisy, approximate and analog cases. It also supports quantum hardware access and provides CPU/GPU/QPU hybrid deployment solutions.
+TensorCircuit-NG is built on top of modern machine learning frameworks: Jax, TensorFlow, and PyTorch. It is specifically suitable for large-scale simulations of quantum-classical hybrid paradigm and variational quantum algorithms in ideal, noisy, Clifford, approximate and analog cases. It also supports quantum hardware access and provides CPU/GPU/QPU hybrid deployment solutions.
 
 TensorCircuit-NG is [fully compatible](https://tensorcircuit-ng.readthedocs.io/en/latest/faq.html#what-is-the-relation-between-tensorcircuit-and-tensorcircuit-ng) with TensorCircuit with more new features and bug fixes (support latest `numpy>2` and `qiskit>1`).
 
 ## Getting Started
 
 Please begin with [Quick Start](/docs/source/quickstart.rst) in the [full documentation](https://tensorcircuit-ng.readthedocs.io/).
 
-For more information on software usage, sota algorithm implementation and engineer paradigm demonstration, please refer to 70+ [example scripts](/examples) and 30+ [tutorial notebooks](https://tensorcircuit-ng.readthedocs.io/en/latest/#tutorials). API docstrings and test cases in [tests](/tests) are also informative.
+For more information on software usage, sota algorithm implementation and engineer paradigm demonstration, please refer to 80+ [example scripts](/examples) and 30+ [tutorial notebooks](https://tensorcircuit-ng.readthedocs.io/en/latest/#tutorials). API docstrings and test cases in [tests](/tests) are also informative.
 
 For beginners, please refer to [quantum computing lectures with TC-NG](https://github.com/sxzgroup/qc_lecture) to learn both quantum computing basis and representative usage of TensorCircuit-NG.
 
@@ -159,6 +159,8 @@ We also have [Docker support](/docker).
 
 - GPU support, quantum device access support, hybrid deployment support
 
+- HPC native, distributed simulation enabled, multiple devices/hosts support
+
 - Efficiency
 
   - Time: 10 to 10^6+ times acceleration compared to TensorFlow Quantum, Pennylane or Qiskit
@@ -180,6 +182,8 @@ We also have [Docker support](/docker).
 
   - Support **noisy simulation** with both Monte Carlo and density matrix (tensor network powered) modes.
 
+  - Support **stabilizer circuit simulation** with stim backend
+
   - Support **approximate simulation** with MPS-TEBD modes.
 
   - Support **analog/digital hybrid simulation** (time dependent Hamiltonian evolution, **pulse** level simulation) with neural ode modes.
@@ -206,13 +210,13 @@ We also have [Docker support](/docker).
 
   - Gradients can be obtained with both **automatic differenation** and parameter shift (vmap accelerated) modes.
 
-  - **Machine learning interface/layer/model** abstraction in both TensorFlow and PyTorch for both numerical simulation and real QPU experiments.
+  - **Machine learning interface/layer/model** abstraction in both TensorFlow, PyTorch and Jax for both numerical simulation and real QPU experiments.
 
   - Circuit sampling supports both final state sampling and perfect sampling from tensor networks.
 
   - Light cone reduction support for local expectation calculation.
 
-  - Highly customizable tensor network contraction path finder with opteinsum interface.
+  - Highly customizable tensor network contraction path finder with opteinsum and cotengra interface.
 
   - Observables are supported in measurement, sparse matrix, dense matrix and MPO format.
 

README_cn.md

@@ -23,15 +23,15 @@
 
 TensorCircuit-NG 是下一代量子软件框架，完美支持自动微分、即时编译、硬件加速和向量并行化。
 
-TensorCircuit-NG 建立在现代机器学习框架 Jax, TensorFlow, PyTorch 之上，支持机器学习后端无关的统一界面。 其特别适用于理想情况、含噪声情况及可控近似情况下，大规模量子经典混合范式和变分量子算法的高效模拟。
+TensorCircuit-NG 建立在现代机器学习框架 Jax, TensorFlow, PyTorch 之上，支持机器学习后端无关的统一界面。 其特别适用于理想情况、含噪声情况、稳定子情况及可控近似情况下，大规模量子经典混合范式和变分量子算法的高效模拟。
 
 TensorCircuit-NG 现在支持真实量子硬件连接和实验，并提供优雅的 CPU/GPU/QPU 混合部署训练方案（v0.9+）。
 
 ## 入门
 
 请从 [完整文档](https://tensorcircuit-ng.readthedocs.io/) 中的 [快速上手](/docs/source/quickstart.rst) 开始。
 
-有关软件用法，算法实现和工程范式演示的更多信息和介绍，请参阅 70+ [示例脚本](/examples) 和 30+ [案例教程](https://tensorcircuit-ng.readthedocs.io/en/latest/#tutorials)。 [测试](/tests) 用例和 API docstring 也提供了丰富的使用信息。
+有关软件用法，算法实现和工程范式演示的更多信息和介绍，请参阅 80+ [示例脚本](/examples) 和 30+ [案例教程](https://tensorcircuit-ng.readthedocs.io/en/latest/#tutorials)。 [测试](/tests) 用例和 API docstring 也提供了丰富的使用信息。
 
 初学者也可以参考[量子计算教程](https://github.com/sxzgroup/qc_lecture)学习量子计算基础和 TensorCircuit-NG 的典型用法.
 
@@ -105,6 +105,8 @@ pip install tensorcircuit-nightly
 
 - GPU 支持、量子硬件支持、混合部署方案支持
 
+- 高性能原生，分布式多卡多节点支持
+
 - 效率
 
   - 时间：与 TFQ, Pennylane, 或 Qiskit 相比，加速 10 到 10^6+ 倍

docs/source/advance.rst

@@ -351,6 +351,76 @@ Quimb provides more flexible MPO construction options:
     h_tc = tc.quantum.quimb2qop(H)
 
 
+
+Stabilizer Circuit Simulator
+-----------------------------
+
+TensorCircuit-NG provides a Stabilizer Circuit simulator for efficient simulation of Clifford circuits. 
+This simulator is particularly useful for quantum error correction, measurement induced phase transition, etc.
+
+
+.. code-block:: python
+
+    import tensorcircuit as tc
+    
+    # Create a stabilizer circuit
+    c = tc.StabilizerCircuit(2)
+    
+    # Apply Clifford gates
+    c.h(0)
+    c.cnot(0, 1)
+    
+    # Measure qubits
+    results = c.measure(0, 1)  # Returns measurement outcomes
+    
+    # Sample multiple shots
+    samples = c.sample(batch=1000)  # Returns array of shape (1000, 2)
+
+**Supported Operations**
+
+The simulator supports common Clifford gates and operations:
+
+- Single-qubit gates: H, X, Y, Z, S, SDG (S dagger)
+- Two-qubit gates: CNOT, CZ, SWAP
+- Measurements: projective measurements (`c.measurement` doesn't affect the state while `c.cond_measure` collpases the state)
+- Post-selection (`c.post_select`)
+- Random Clifford gates (`c.random_gate`)
+- Gates defined by tableau (`c.tableau_gate`)
+- Entanglement calculation (`c.entanglement_entropy`)
+- Pauli string operator expectation (`c.expectation_ps`)
+- Openqasm and qir transformation as usual circuits
+- Initialization state provided by Pauli string stabilizer (`tc.StabCircuit(inputs=...)`) or inverse tableau (`tc.StabCircuit(tableau_inputs=)`)
+- Probabilistic noise (`c.depolarizing`)
+
+
+Example: Quantum Teleportation
+
+.. code-block:: python
+
+    c = tc.StabilizerCircuit(3)
+    
+    # Prepare Bell pair between qubits 1 and 2
+    c.h(1)
+    c.cnot(1, 2)
+    
+    # State to teleport on qubit 0 (must be Clifford)
+    c.x(0)
+    
+    # Teleportation circuit
+    c.cnot(0, 1)
+    c.h(0)
+    
+    # Measure and apply corrections
+    r0 = c.cond_measure(0)
+    r1 = c.cond_measure(1)
+    if r0 == 1:
+        c.z(2)
+    if r1 == 1:
+        c.x(2)
+
+
+
+
 Fermion Gaussian State Simulator
 --------------------------------
 

examples/slicing_auto_pmap_mpo.py

@@ -1,6 +1,6 @@
 """
 This script illustrates how to parallelize both the contraction path
-finding and sliced contraction computation
+finding and sliced contraction computation for MPO expectation
 """
 
 from functools import partial