Fix TRP #1
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Fix TRP #1
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Co-authored-by: Tom Bromley <[email protected]>
Co-authored-by: Olivia Di Matteo <[email protected]>
Co-authored-by: Olivia Di Matteo <[email protected]>
Co-authored-by: Olivia Di Matteo <[email protected]>
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Co-authored-by: Olivia Di Matteo <[email protected]>
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…5446) **Context:** When testing the new lightning device, we discovered that TensorFlow would error if the parameters were float64, the device returned float32, and classical operations modified the parameter: ``` dev = qml.device('lightning.qubit', wires=2, c_dtype=np.complex64) @qml.qnode(dev, diff_method="parameter-shift") def circuit(x): qml.RX(tf.cos(x), wires=0) return qml.expval(qml.Z(0)) x = tf.Variable(0.1, dtype=tf.float64) with tf.GradientTape() as tape: y = circuit(x) tape.gradient(y, x) ``` ``` InvalidArgumentError: cannot compute Mul as input #1(zero-based) was expected to be a float tensor but is a double tensor [Op:Mul] name: ``` The problem is that the output were results were `float32` precision, so the vjp would be `float32`. But `float32` vjp cannot be combined with `tf.cos`'s `float64` vjp. The reverse problem (`float64` results but `float32` data) seems to be fine. **Description of the Change:** If the input data are `float64` or `complex128`, we promote the results to the type of the parameters. [sc-58966] --------- Co-authored-by: Mudit Pandey <[email protected]>
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make test.(some tests failing in both master and branch, can not get an environment to work)
Context:
Currently the only method for finding gradients for qutrit circuits is through param-shift for qutrits this takes four evaluations and overall is very expensive. The TRP (TRX/TRY/TRZ) gates are not compatible with backwards propagation. With a planned default.qutrit.mixed it is important for these to be backprop differentiable to speed what will already be a very slow process.
Description of the Change:
qutrit parametric ops TRX, TRY, TRZ matrix construction is now done solely using qml.math and allow for inputs from TensorFlow, Torch, Autograd, and JAX backprop differentiation variables, also default.qutrit has been changed to use qml.math to allow for backwards propagation of qutrit circuits.
Benefits:
Backprop being implemented slows for much faster differentiation of qutrit circuits. This will be very helpful to differentiate larger circuits in a reasonable amount of time. The changes to TRP gates will also be useful for planned default.qutrit as the increased required resources for noisy simulation makes param-shift differentiation much less appealing
Possible Drawbacks:
Two small static methods copied from default_mixed, increase copied code, code smell
Makes interfaces useful for defualt_qutrit less tests than on other devices
Related GitHub Issues:
N/A