version0.12.0

Author: refraction-ray

CHANGELOG.md

@@ -2,6 +2,8 @@
 
 ## Unreleased
 
+## 0.12.0
+
 ### Added
 
 - Add translation of r gate from qiskit
@@ -28,6 +30,8 @@
 
 - Fixed jax refactoring (0.4.24) where SVD and QR return a namedtuple instead of a tuple
 
+- Fix qiskit<1.0 and tf<2.16
+
 ## 0.11.0
 
 ### Added

README.md

@@ -356,13 +356,13 @@ Reference paper: https://arxiv.org/abs/2303.10825 (published in JCTC).
 
 For the numerical simulation and hardware experiments with error mitigation on QAOA, see the [project repo](https://github.com/sherrylixuecheng/EMQAOA-DARBO).
 
-Reference paper: https://arxiv.org/abs/2303.14877.
+Reference paper: https://arxiv.org/abs/2303.14877 (published in Communications Physics).
 
 ### 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.
+Reference paper: https://arxiv.org/abs/2308.01068 (published in PRApplied).
 
 ### More works
 
@@ -377,31 +377,31 @@ Reference paper: https://arxiv.org/abs/2308.01068.
 
 - Variational Quantum Simulations of Finite-Temperature Dynamical Properties via Thermofield Dynamics: https://arxiv.org/abs/2206.05571.
 
-- Understanding quantum machine learning also requires rethinking generalization: https://arxiv.org/abs/2306.13461.
+- Understanding quantum machine learning also requires rethinking generalization: https://arxiv.org/abs/2306.13461 (published in Nature Communications).
 
 - Decentralized Quantum Federated Learning for Metaverse: Analysis, Design and Implementation: https://arxiv.org/abs/2306.11297. Code: https://github.com/s222416822/BQFL.
 
 - Non-IID quantum federated learning with one-shot communication complexity: https://arxiv.org/abs/2209.00768 (published in Quantum Machine Intelligence). Code: https://github.com/JasonZHM/quantum-fed-infer.
 
 - Quantum generative adversarial imitation learning: https://doi.org/10.1088/1367-2630/acc605 (published in New Journal of Physics).
 
-- GSQAS: Graph Self-supervised Quantum Architecture Search: https://arxiv.org/abs/2303.12381.
+- GSQAS: Graph Self-supervised Quantum Architecture Search: https://arxiv.org/abs/2303.12381 (published in Physica A: Statistical Mechanics and its Applications).
 
 - Practical advantage of quantum machine learning in ghost imaging: https://www.nature.com/articles/s42005-023-01290-1 (published in Communications Physics).
 
 - Zero and Finite Temperature Quantum Simulations Powered by Quantum Magic: https://arxiv.org/abs/2308.11616.
 
 - Comparison of Quantum Simulators for Variational Quantum Search: A Benchmark Study: https://arxiv.org/abs/2309.05924.
 
-- Statistical analysis of quantum state learning process in quantum neural networks: https://arxiv.org/abs/2309.14980 (Pubilshed in NeurIPS).
+- Statistical analysis of quantum state learning process in quantum neural networks: https://arxiv.org/abs/2309.14980 (published in NeurIPS).
 
-- Generative quantum machine learning via denoising diffusion probabilistic models: https://arxiv.org/abs/2310.05866.
+- Generative quantum machine learning via denoising diffusion probabilistic models: https://arxiv.org/abs/2310.05866 (published in PRL).
 
 - Quantum imaginary time evolution and quantum annealing meet topological sector optimization: https://arxiv.org/abs/2310.04291.
 
 - Google Summer of Code 2023 Projects (QML4HEP): https://github.com/ML4SCI/QMLHEP, https://github.com/Gopal-Dahale/qgnn-hep, https://github.com/salcc/QuantumTransformers.
 
-- Absence of barren plateaus in finite local-depth circuits with long-range entanglement: https://arxiv.org/abs/2311.01393.
+- Absence of barren plateaus in finite local-depth circuits with long-range entanglement: https://arxiv.org/abs/2311.01393 (published in PRL).
 
 - Non-Markovianity benefits quantum dynamics simulation: https://arxiv.org/abs/2311.17622.
 

tensorcircuit/__init__.py

@@ -1,4 +1,4 @@
-__version__ = "0.11.0"
+__version__ = "0.12.0"
 __author__ = "TensorCircuit Authors"
 __creator__ = "refraction-ray"