Merge pull request #62 from CQCL/develop

0.3.2

README.md

@@ -48,14 +48,14 @@ param_to_st = qujax.get_params_to_statetensor_func(circuit_gates,
 We now have a pure JAX function that generates the statetensor for given parameters
 ```python
 param_to_st(jnp.array([0.1]))
-# DeviceArray([[0.58778524+0.j, 0.        +0.j],
-#              [0.80901706+0.j, 0.        +0.j]], dtype=complex64)
+# Array([[0.58778524+0.j, 0.        +0.j],
+#        [0.80901706+0.j, 0.        +0.j]], dtype=complex64)
 ```
 
 The statevector can be obtained from the statetensor via ```.flatten()```.
 ```python
 param_to_st(jnp.array([0.1])).flatten()
-# DeviceArray([0.58778524+0.j, 0.+0.j, 0.80901706+0.j, 0.+0.j], dtype=complex64)
+# Array([0.58778524+0.j, 0.+0.j, 0.80901706+0.j, 0.+0.j], dtype=complex64)
 ```
 
 We can also use qujax to map the statetensor to an expected value
@@ -70,8 +70,8 @@ from jax import value_and_grad
 param_to_expectation = lambda param: st_to_expectation(param_to_st(param))
 expectation_and_grad = value_and_grad(param_to_expectation)
 expectation_and_grad(jnp.array([0.1]))
-# (DeviceArray(-0.3090171, dtype=float32),
-#    DeviceArray([-2.987832], dtype=float32))
+# (Array(-0.3090171, dtype=float32),
+#    Array([-2.987832], dtype=float32))
 ```
 
 ## Densitytensor simulations with qujax
@@ -91,7 +91,7 @@ Expectations can also be evaluated through the densitytensor
 ```python
 dt_to_expectation = qujax.get_densitytensor_to_expectation_func([['Z']], [[0]], [1.])
 dt_to_expectation(dt)
-# DeviceArray(-0.3090171, dtype=float32)
+# Array(-0.3090171, dtype=float32)
 ```
 Again everything is differentiable, jit-able and can be composed with other JAX code.
 

examples/README.md

@@ -6,6 +6,7 @@ In this directory, you can find a selection of notebooks demonstrating some simp
 - [`generative_modelling.ipynb`](https://github.com/CQCL/qujax/blob/main/examples/generative_modelling.ipynb) - uses a parameterised quantum circuit as a generative model for a real life dataset. Trains via stochastic gradient Langevin dynamics on the maximum mean discrepancy between statetensor and dataset.
 - [`heisenberg_vqe.ipynb`](https://github.com/CQCL/qujax/blob/main/examples/heisenberg_vqe.ipynb) - an implementation of the variational quantum eigensolver to find the ground state of a quantum Hamiltonian.
 - [`maxcut_vqe.ipynb`](https://github.com/CQCL/qujax/blob/main/examples/maxcut_vqe.ipynb) - an implementation of the variational quantum eigensolver to solve a maxcut problem. Trains with Adam via [`optax`](https://github.com/deepmind/optax) and uses more realistic stochastic parameter shift gradients.
+- [`noise_channel.ipynb`](https://github.com/CQCL/qujax/blob/main/examples/noise_channel.ipynb) - uses the densitytensor simulator to fit the parameters of a depolarising noise channel.
 - [`qaoa.ipynb`](https://github.com/CQCL/qujax/blob/main/examples/qaoa.ipynb) - uses a problem inspired QAOA ansatz to find the ground state of a quantum Hamiltonian. Demonstrates how to encode more sophisticated parameters that control multiple gates.
 - [`variational_inference.ipynb`](https://github.com/CQCL/qujax/blob/main/examples/variational_inference.ipynb) - uses a parameterised quantum circuit as a variational distribution to fit to a target probability mass function. Uses Adam via [`optax`](https://github.com/deepmind/optax) to minimise the KL divergence between circuit and target distributions.