Merge pull request #255 from singular-value/patch-1

Fix minor typo

Author: 01110011011101010110010001101111

README.md

@@ -55,7 +55,7 @@ Simulate quantum computations on classical hardware using PyTorch. It supports s
 Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, quantum neural networks.
 #### Differences from Qiskit/Pennylane
 
-Dynamic computation graph, automatic gradient computation, fast GPU support, batch model tersorized processing.
+Dynamic computation graph, automatic gradient computation, fast GPU support, batch model tensorized processing.
 
 ## News
 - v0.1.8 Available!

examples/README.md

@@ -1,6 +1,6 @@
 ## TorchQuantum Examples
 
-For **beginning level**, you may check [QNN for MNIST](simple_mnist), [Quantum Convolution (Quanvolution)](quanvolution) and [Quantum Kernel Method](quantum_kernel_method), and [Quantum Regression](regression).
+For **beginning level**, you may check [QNN for MNIST](mnist), [Quantum Convolution (Quanvolution)](quanvolution) and [Quantum Kernel Method](quantum_kernel_method), and [Quantum Regression](regression).
 
 For **intermediate level**, you may check [Amplitude Encoding for MNIST](amplitude_encoding_mnist), [Clifford gate QNN](clifford_qnn), [Save and Load QNN models](save_load_example), [PauliSum Operation](PauliSumOp), [How to convert tq to Qiskit](converter_tq_qiskit).