Merge branch 'master' into dev

Author: FelixXu35

.all-contributorsrc

@@ -247,7 +247,9 @@
       "avatar_url": "https://avatars.githubusercontent.com/u/61252303?v=4",
       "profile": "https://www.linkedin.com/in/felix-xu-16a153196/",
       "contributions": [
-        "tutorial"
+        "tutorial",
+        "code",
+        "test"
       ]
     },
     {
@@ -258,6 +260,28 @@
       "contributions": [
         "doc"
       ]
+    },
+    {
+      "login": "PeilinZHENG",
+      "name": "peilin",
+      "avatar_url": "https://avatars.githubusercontent.com/u/45784888?v=4",
+      "profile": "https://github.com/PeilinZHENG",
+      "contributions": [
+        "tutorial",
+        "code",
+        "test",
+        "doc"
+      ]
+    },
+    {
+      "login": "EmilianoG-byte",
+      "name": "Cristian Emiliano Godinez Ramirez",
+      "avatar_url": "https://avatars.githubusercontent.com/u/57567043?v=4",
+      "profile": "https://emilianog-byte.github.io",
+      "contributions": [
+        "code",
+        "test"
+      ]
     }
   ],
   "contributorsPerLine": 6,

.gitattributes

@@ -0,0 +1 @@
+* text=auto

CHANGELOG.md

@@ -2,10 +2,39 @@
 
 ## Unreleased
 
+## 0.11.0
+
 ### Added
 
 - Add multiple GPU VQE examples using jax pmap
 
+- Add `with_prob` option to `general_kraus` so that the probability of each option can be returned together
+
+- Add benchmark example showcasing new way of implementing matrix product using vmap
+
+- Add keras3 example showcasing integration with tc
+
+- Add circuit copy method that avoid shallow copy issue `Circuit.copy()`
+
+- Add end to end infrastructures and methods for classical shadow in `shadows.py`
+
+- Add classical shadow tutorial
+
+- Add NN-VQE tutorial
+
+### Fixed
+
+- improve the `adaptive_vmap` to support internal jit and pytree output
+
+- fix `pauli_gates` dtype unchange issue when set new dtype (not recommend to use this attr anymore)
+
+- fix rem `apply_correction` bug when non-numpy backend is set
+
+- fix tf warning for `cast` with higher version of tf
+### Changed
+
+- The static method `BaseCircuit.copy` is renamed as `BaseCircuit.copy_nodes` (breaking changes)
+
 ## 0.10.0
 
 ### Added

README.md

@@ -37,7 +37,7 @@ TensorCircuit is built on top of modern machine learning frameworks: Jax, Tensor
 
 Please begin with [Quick Start](/docs/source/quickstart.rst) in the [full documentation](https://tensorcircuit.readthedocs.io/).
 
-For more information on software usage, sota algorithm implementation and engineer paradigm demonstration, please refer to 60+ [example scripts](/examples) and 30+ [tutorial notebooks](https://tensorcircuit.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 70+ [example scripts](/examples) and 30+ [tutorial notebooks](https://tensorcircuit.readthedocs.io/en/latest/#tutorials). API docstrings and test cases in [tests](/tests) are also informative.
 
 The following are some minimal demos.
 
@@ -76,6 +76,43 @@ theta = tc.array_to_tensor(1.0)
 print(g(theta))
 ```
 
+<details>
+  <summary> More highlight features for TensorCircuit (click for details) </summary>
+
+- Sparse Hamiltonian generation and expectation evaluation:
+
+```python
+n = 6
+pauli_structures = []
+weights = []
+for i in range(n):
+    pauli_structures.append(tc.quantum.xyz2ps({"z": [i, (i + 1) % n]}, n=n))
+    weights.append(1.0)
+for i in range(n):
+    pauli_structures.append(tc.quantum.xyz2ps({"x": [i]}, n=n))
+    weights.append(-1.0)
+h = tc.quantum.PauliStringSum2COO(pauli_structures, weights)
+print(h)
+# BCOO(complex64[64, 64], nse=448)
+c = tc.Circuit(n)
+c.h(range(n))
+energy = tc.templates.measurements.operator_expectation(c, h)
+# -6
+```
+
+- Large-scale simulation with tensor network engine
+
+```python
+# tc.set_contractor("cotengra-30-10")
+n=500
+c = tc.Circuit(n)
+c.h(0)
+c.cx(range(n-1), range(1, n))
+c.expectation_ps(z=[0, n-1], reuse=False)
+```
+
+</details>
+
 ## Install
 
 The package is written in pure Python and can be obtained via pip as:
@@ -168,6 +205,8 @@ We also have [Docker support](/docker).
 
   - Reusable common circuit/measurement/problem templates and patterns.
 
+  - Jittable classical shadow infrastructures.
+
   - SOTA quantum algorithm and model implementations.
 
   - Support hybrid workflows and pipelines with CPU/GPU/QPU hardware from local/cloud/hpc resources using tf/torch/jax/cupy/numpy frameworks all at the same time.
@@ -182,12 +221,14 @@ This project is released by [Tencent Quantum Lab](https://quantum.tencent.com/)
 
 ### Citation
 
-If this project helps in your research, please cite our software whitepaper published in Quantum:
+If this project helps in your research, please cite our software whitepaper to acknowledge the work put into the development of TensorCircuit.
 
-[TensorCircuit: a Quantum Software Framework for the NISQ Era](https://quantum-journal.org/papers/q-2023-02-02-912/)
+[TensorCircuit: a Quantum Software Framework for the NISQ Era](https://quantum-journal.org/papers/q-2023-02-02-912/) (published in Quantum)
 
 which is also a good introduction to the software.
 
+Research works citing TensorCircuit can be highlighted in [Research and Applications section](https://github.com/tencent-quantum-lab/tensorcircuit#research-and-applications).
+
 ### Guidelines
 
 For contribution guidelines and notes, see [CONTRIBUTING](/CONTRIBUTING.md).
@@ -232,8 +273,10 @@ TensorCircuit is open source, released under the Apache License, Version 2.0.
     <tr>
       <td align="center" valign="top" width="16.66%"><a href="https://github.com/eurethia"><img src="https://avatars.githubusercontent.com/u/84611606?v=4?s=100" width="100px;" alt="隐公观鱼"/><br /><sub><b>隐公观鱼</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=eurethia" title="Code">💻</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=eurethia" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="16.66%"><a href="https://github.com/WiuYuan"><img src="https://avatars.githubusercontent.com/u/108848998?v=4?s=100" width="100px;" alt="WiuYuan"/><br /><sub><b>WiuYuan</b></sub></a><br /><a href="#example-WiuYuan" title="Examples">💡</a></td>
-      <td align="center" valign="top" width="16.66%"><a href="https://www.linkedin.com/in/felix-xu-16a153196/"><img src="https://avatars.githubusercontent.com/u/61252303?v=4?s=100" width="100px;" alt="Felix Xu"/><br /><sub><b>Felix Xu</b></sub></a><br /><a href="#tutorial-FelixXu35" title="Tutorials">✅</a></td>
+      <td align="center" valign="top" width="16.66%"><a href="https://www.linkedin.com/in/felix-xu-16a153196/"><img src="https://avatars.githubusercontent.com/u/61252303?v=4?s=100" width="100px;" alt="Felix Xu"/><br /><sub><b>Felix Xu</b></sub></a><br /><a href="#tutorial-FelixXu35" title="Tutorials">✅</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=FelixXu35" title="Code">💻</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=FelixXu35" title="Tests">⚠️</a></td>
       <td align="center" valign="top" width="16.66%"><a href="https://scholar.harvard.edu/hongyehu/home"><img src="https://avatars.githubusercontent.com/u/50563225?v=4?s=100" width="100px;" alt="Hong-Ye Hu"/><br /><sub><b>Hong-Ye Hu</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=hongyehu" title="Documentation">📖</a></td>
+      <td align="center" valign="top" width="16.66%"><a href="https://github.com/PeilinZHENG"><img src="https://avatars.githubusercontent.com/u/45784888?v=4?s=100" width="100px;" alt="peilin"/><br /><sub><b>peilin</b></sub></a><br /><a href="#tutorial-PeilinZHENG" title="Tutorials">✅</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=PeilinZHENG" title="Code">💻</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=PeilinZHENG" title="Tests">⚠️</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=PeilinZHENG" title="Documentation">📖</a></td>
+      <td align="center" valign="top" width="16.66%"><a href="https://emilianog-byte.github.io"><img src="https://avatars.githubusercontent.com/u/57567043?v=4?s=100" width="100px;" alt="Cristian Emiliano Godinez Ramirez"/><br /><sub><b>Cristian Emiliano Godinez Ramirez</b></sub></a><br /><a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=EmilianoG-byte" title="Code">💻</a> <a href="https://github.com/tencent-quantum-lab/tensorcircuit/commits?author=EmilianoG-byte" title="Tests">⚠️</a></td>
     </tr>
   </tbody>
 </table>
@@ -294,6 +337,12 @@ For the numerical simulation and hardware experiments with error mitigation on Q
 
 Reference paper: https://arxiv.org/abs/2303.14877.
 
+### NN-VQA
+
+For the setup and simulation code of neural network encoded variational quantum eigensolver, see the [demo](/docs/source/tutorials/nnvqe.ipynb).
+
+Reference paper: https://arxiv.org/abs/2308.01068.
+
 ### More works
 
  <details>
@@ -317,6 +366,8 @@ Reference paper: https://arxiv.org/abs/2303.14877.
 
 - GSQAS: Graph Self-supervised Quantum Architecture Search: https://arxiv.org/abs/2303.12381.
 
+- Practical advantage of quantum machine learning in ghost imaging: https://www.nature.com/articles/s42005-023-01290-1 (published in Communications Physics).
+
   </details>
 
 If you want to highlight your research work here, feel free to add by opening PR.

README_cn.md

@@ -35,7 +35,7 @@ TensorCircuit 现在支持真实量子硬件连接和实验，并提供优雅的
 
 请从 [完整文档](https://tensorcircuit.readthedocs.io/zh/latest/) 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 60+ [示例脚本](/examples) 和 30+ [案例教程](https://tensorcircuit.readthedocs.io/zh/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 70+ [示例脚本](/examples) 和 30+ [案例教程](https://tensorcircuit.readthedocs.io/zh/latest/#tutorials)。 [测试](/tests) 用例和 API docstring 也提供了丰富的使用信息。
 
 以下是一些最简易的演示。
 
@@ -127,7 +127,7 @@ pip install tensorcircuit-nightly
 
 ### 引用
 
-如果该软件对您的研究有帮助, 请引用我们发表在 Quantum 期刊的白皮书文章:
+如果该软件对您的研究有帮助, 请引用我们发表在 Quantum 期刊的白皮书文章来支持我们的研发付出。
 
 [TensorCircuit: a Quantum Software Framework for the NISQ Era](https://quantum-journal.org/papers/q-2023-02-02-912/).
 
@@ -184,3 +184,9 @@ VQEX 在 MBL 相位识别上的应用见 [教程](/docs/source/tutorials/vqex_mb
 数值模拟和带错误消除的真实量子硬件实验验证 QAOA 优化的代码请参考 [项目](https://github.com/sherrylixuecheng/EMQAOA-DARBO)。
 
 参考论文: https://arxiv.org/abs/2303.14877。
+
+### NN-VQA
+
+关于神经网络编码的变分量子算法的实现和工作流, 见 [教程](/docs/source/tutorials/nnvqe.ipynb)。
+
+参考论文: https://arxiv.org/abs/2308.01068。

check_all.sh

@@ -1,9 +1,13 @@
 tensorcircuit.applications
 ================================================================================
 .. toctree::
+    applications/ai.rst
     applications/dqas.rst
+    applications/finance.rst
     applications/graphdata.rst
     applications/layers.rst
+    applications/optimization.rst
+    applications/physics.rst
     applications/utils.rst
     applications/vags.rst
     applications/van.rst

docs/source/api/applications.rst

@@ -0,0 +1,4 @@
+tensorcircuit.applications.ai
+================================================================================
+.. toctree::
+    

docs/source/api/applications/ai.rst

@@ -0,0 +1,4 @@
+tensorcircuit.applications.finance
+================================================================================
+.. toctree::
+    finance/portfolio.rst

docs/source/api/applications/finance.rst

@@ -0,0 +1,7 @@
+tensorcircuit.applications.finance.portfolio
+================================================================================
+.. automodule:: tensorcircuit.applications.finance.portfolio
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:

docs/source/api/applications/finance/portfolio.rst

@@ -0,0 +1,7 @@
+tensorcircuit.applications.optimization
+================================================================================
+.. automodule:: tensorcircuit.applications.optimization
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:

docs/source/api/applications/optimization.rst

@@ -0,0 +1,5 @@
+tensorcircuit.applications.physics
+================================================================================
+.. toctree::
+    physics/baseline.rst
+    physics/fss.rst

docs/source/api/applications/physics.rst

@@ -0,0 +1,7 @@
+tensorcircuit.applications.physics.baseline
+================================================================================
+.. automodule:: tensorcircuit.applications.physics.baseline
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:

docs/source/api/applications/physics/baseline.rst

@@ -0,0 +1,7 @@
+tensorcircuit.applications.physics.fss
+================================================================================
+.. automodule:: tensorcircuit.applications.physics.fss
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:

docs/source/api/applications/physics/fss.rst

@@ -0,0 +1,7 @@
+tensorcircuit.shadows
+================================================================================
+.. automodule:: tensorcircuit.shadows
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:

docs/source/api/shadows.rst

@@ -1,8 +1,10 @@
 tensorcircuit.templates
 ================================================================================
 .. toctree::
+    templates/ansatz.rst
     templates/blocks.rst
     templates/chems.rst
+    templates/conversions.rst
     templates/dataset.rst
     templates/ensemble.rst
     templates/graphs.rst

docs/source/api/templates.rst

@@ -0,0 +1,7 @@
+tensorcircuit.templates.ansatz
+================================================================================
+.. automodule:: tensorcircuit.templates.ansatz
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members:

docs/source/api/templates/ansatz.rst

@@ -0,0 +1,7 @@
+tensorcircuit.templates.conversions
+================================================================================
+.. automodule:: tensorcircuit.templates.conversions
+    :members:
+    :undoc-members:
+    :show-inheritance:
+    :inherited-members: