Add new differentiation method based on rewinding the tape [PR1] #1029
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trbromley
commented
Jan 22, 2021
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| with RewindTape() as tape: | ||
| qml.RX(a, wires=0) | ||
| qml.expval(qml.PauliZ(0)) | ||
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| circuit_output = tape.execute(dev) | ||
| expected_output = np.cos(a) | ||
| assert np.allclose(circuit_output, expected_output, atol=tol, rtol=0) | ||
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| # circuit jacobians | ||
| circuit_jacobian = tape.jacobian(dev, method="analytic") | ||
| expected_jacobian = -np.sin(a) | ||
| assert np.allclose(circuit_jacobian, expected_jacobian, atol=tol, rtol=0) | ||
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| def test_multiple_rx_gradient(self, tol): | ||
| """Tests that the gradient of multiple RX gates in a circuit yields the correct result.""" | ||
| dev = qml.device("default.qubit", wires=3) | ||
| params = np.array([np.pi, np.pi / 2, np.pi / 3]) | ||
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| with RewindTape() as tape: | ||
| qml.RX(params[0], wires=0) | ||
| qml.RX(params[1], wires=1) | ||
| qml.RX(params[2], wires=2) | ||
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| for idx in range(3): | ||
| qml.expval(qml.PauliZ(idx)) | ||
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| circuit_output = tape.execute(dev) | ||
| expected_output = np.cos(params) | ||
| assert np.allclose(circuit_output, expected_output, atol=tol, rtol=0) | ||
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| # circuit jacobians | ||
| circuit_jacobian = tape.jacobian(dev, method="analytic") | ||
| expected_jacobian = -np.diag(np.sin(params)) | ||
| assert np.allclose(circuit_jacobian, expected_jacobian, atol=tol, rtol=0) | ||
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| qubit_ops = [getattr(qml, name) for name in qml.ops._qubit__ops__] | ||
| analytic_qubit_ops = {cls for cls in qubit_ops if cls.grad_method == "A"} | ||
| analytic_qubit_ops -= { | ||
| qml.CRot, # not supported for RewindTape | ||
| qml.PauliRot, # not supported in test | ||
| qml.MultiRZ, # not supported in test | ||
| qml.U1, # not supported on device | ||
| qml.U2, # not supported on device | ||
| qml.U3, # not supported on device | ||
| } | ||
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| @pytest.mark.parametrize("obs", [qml.PauliX, qml.PauliY]) | ||
| @pytest.mark.parametrize("op", analytic_qubit_ops) | ||
| def test_gradients(self, op, obs, mocker, tol, dev): | ||
| """Tests that the gradients of circuits match between the | ||
| finite difference and analytic methods.""" | ||
| args = np.linspace(0.2, 0.5, op.num_params) | ||
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| with RewindTape() as tape: | ||
| qml.Hadamard(wires=0) | ||
| qml.RX(0.543, wires=0) | ||
| qml.CNOT(wires=[0, 1]) | ||
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| op(*args, wires=range(op.num_wires)) | ||
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| qml.Rot(1.3, -2.3, 0.5, wires=[0]) | ||
| qml.RZ(-0.5, wires=0) | ||
| qml.RY(0.5, wires=1).inv() | ||
| qml.CNOT(wires=[0, 1]) | ||
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| qml.expval(obs(wires=0)) | ||
| qml.expval(qml.PauliZ(wires=1)) | ||
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| tape.execute(dev) | ||
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| tape.trainable_params = set(range(1, 1 + op.num_params)) | ||
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| grad_F = tape.jacobian(dev, method="numeric") | ||
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| spy = mocker.spy(RewindTape, "_rewind_jacobian") | ||
| grad_A = tape.jacobian(dev, method="analytic") | ||
| spy.assert_called() | ||
| assert np.allclose(grad_A, grad_F, atol=tol, rtol=0) | ||
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| def test_gradient_gate_with_multiple_parameters(self, tol, dev): | ||
| """Tests that gates with multiple free parameters yield correct gradients.""" | ||
| x, y, z = [0.5, 0.3, -0.7] | ||
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| with RewindTape() as tape: | ||
| qml.RX(0.4, wires=[0]) | ||
| qml.Rot(x, y, z, wires=[0]) | ||
| qml.RY(-0.2, wires=[0]) | ||
| qml.expval(qml.PauliZ(0)) | ||
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| tape.trainable_params = {1, 2, 3} | ||
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| grad_A = tape.jacobian(dev, method="analytic") | ||
| grad_F = tape.jacobian(dev, method="numeric") | ||
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| # gradient has the correct shape and every element is nonzero | ||
| assert grad_A.shape == (1, 3) | ||
| assert np.count_nonzero(grad_A) == 3 | ||
| # the different methods agree | ||
| assert np.allclose(grad_A, grad_F, atol=tol, rtol=0) |
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Many of these are copied from the reversible tests, and edited accordingly.
Codecov Report
@@ Coverage Diff @@
## master #1029 +/- ##
==========================================
- Coverage 97.94% 97.91% -0.03%
==========================================
Files 153 154 +1
Lines 11384 11441 +57
==========================================
+ Hits 11150 11203 +53
- Misses 234 238 +4
Continue to review full report at Codecov.
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albi3ro
reviewed
Jan 25, 2021
| gradients of qubit operations using the rewind method of analytic differentiation. | ||
| This gradient method returns *exact* gradients, however requires use of a statevector simulator. | ||
| Simply create the tape, and then call the Jacobian method: | ||
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There was a problem hiding this comment.
Should we show how to create a tape as a rewind tape?
Suggested change
| >>> dev = qml.Device('default.qubit', wires=[0]) | |
| >>> with qml.tape.RewindTape() as tape: | |
| qml.RX(0.1, wires=0) |
albi3ro
reviewed
Jan 25, 2021
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| if not supported_device: | ||
| raise qml.QuantumFunctionError( | ||
| "The rewind gradient method is only supported on statevector-based devices" |
There was a problem hiding this comment.
The device could be a statevector device, yet just not use the same internal functions as default.qubit, the _apply_operation and _apply_unitary.
Maybe we should have one error for whether or not its a statevector device, and another for the internal functions. This would allow the error message to be more precise.
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To maintain small PRs, #1017 will be split into a few manageable PRs. This is the first one - it contains the main
RewindTapeand tests.Coming later: