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bytes doc/_static/tape_mpl/rcparams.png | Bin 22952 -> 23102 bytes doc/_static/tape_mpl/show_all_wires.png | Bin 23962 -> 24036 bytes doc/_static/tape_mpl/sketch_style.png | Bin 51155 -> 51110 bytes doc/_static/tape_mpl/wire_order.png | Bin 22397 -> 22427 bytes doc/_static/tape_mpl/wires_labels.png | Bin 23090 -> 23173 bytes doc/releases/changelog-0.34.0.md | 330 +++++++++++++----------- pennylane/drawer/mpldrawer.py | 5 +- tests/drawer/test_mpldrawer.py | 16 +- 13 files changed, 202 insertions(+), 149 deletions(-) diff --git a/doc/_static/draw_mpl/mid_measure.png b/doc/_static/draw_mpl/mid_measure.png index 6e6079ba9e7be03a0bf82af705a869d85c64ae4e..bdf652b5fa2e4cb06da1085d520b635ed3c97b81 100644 GIT binary patch literal 9596 zcmeHtXH-*dx9tvsiYSN(2ng5!m8MdqqaY$p1qA7e^xhJw#)1_@K|s2Q^p3O;nn+PY zl@dDAYv2V!3+=AF-*?Y_&;57C8RL%ovGeTxw6*4(Yd-tcO>MP92RIKP2y#gM zy2@?8@fkiYd#W0G>bh8aV(z(HAzJr5T^(IK9qsO)KwG(c*ts~1 ziAalF5ISM&>FMerBP#0j9}PrY+-*c9@{2OzA@r`-?|L8z(>>}hDoruf4nfXLtE(vM 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Statistics and drawings for mid-circuit measurements 🎨

+

Statistics and drawing for mid-circuit measurements 🎨

+ +* It is now possible to return statistics of composite mid-circuit measurements. + [(#4888)](https://github.com/PennyLaneAI/pennylane/pull/4888) + + Mid-circuit measurement results can be composed using basic arithmetic operations and then + statistics can be calculated by putting the result within a PennyLane + [measurement](https://docs.pennylane.ai/en/stable/introduction/measurements.html) like + `qml.expval()`. For example: + + ```python + import pennylane as qml + + dev = qml.device("default.qubit") + + @qml.qnode(dev) + def circuit(phi, theta): + qml.RX(phi, wires=0) + m0 = qml.measure(wires=0) + qml.RY(theta, wires=1) + m1 = qml.measure(wires=1) + return qml.expval(~m0 + m1) + + print(circuit(1.23, 4.56)) + ``` + ``` + 1.2430187928114291 + ``` + + Another option, for ease-of-use when using `qml.sample()`, `qml.probs()`, or `qml.counts()`, is to + provide a simple list of mid-circuit measurement results: + + ```python + dev = qml.device("default.qubit") + + @qml.qnode(dev) + def circuit(phi, theta): + qml.RX(phi, wires=0) + m0 = qml.measure(wires=0) + qml.RY(theta, wires=1) + m1 = qml.measure(wires=1) + return qml.sample(op=[m0, m1]) + + print(circuit(1.23, 4.56, shots=5)) + ``` + + ``` + [[0 1] + [0 1] + [0 0] + [1 0] + [0 1]] + ``` + + Composite mid-circuit measurement statistics are supported on `default.qubit` and `default.mixed`. + To learn more about which measurements and arithmetic operators are supported, + [refer to the measurements page](https://docs.pennylane.ai/en/stable/introduction/measurements.html) and the + [documentation for qml.measure](https://docs.pennylane.ai/en/stable/code/api/pennylane.measure.html). * Mid-circuit measurements can now be visualized with the text-based `qml.draw()` and the - graphical `qml.draw_mpl()`. + graphical `qml.draw_mpl()` methods. [(#4775)](https://github.com/PennyLaneAI/pennylane/pull/4775) [(#4803)](https://github.com/PennyLaneAI/pennylane/pull/4803) [(#4832)](https://github.com/PennyLaneAI/pennylane/pull/4832) @@ -18,11 +75,10 @@ [(#4957)](https://github.com/PennyLaneAI/pennylane/pull/4957) Drawing of mid-circuit measurement capabilities including qubit reuse and reset, - postselection, conditioning, and collecting statistics is supported. + postselection, conditioning, and collecting statistics is now supported. Here + is an all-encompassing example: ```python - import pennylane as qml - def circuit(): m0 = qml.measure(0, reset=True) m1 = qml.measure(1, postselect=1) @@ -49,63 +105,7 @@ >>> print(qml.draw_mpl(circuit)()) ``` - - -* Users can now return statistics for multiple mid-circuit measurements. - [(#4888)](https://github.com/PennyLaneAI/pennylane/pull/4888) - - There are two ways in which mid-circuit measurement statistics can be collected: - - * By using arithmetic/binary operators. This can be through unary or binary operators as such: - - ```python - import pennylane as qml - - dev = qml.device("default.qubit") - - @qml.qnode(dev) - def circuit(phi, theta): - qml.RX(phi, wires=0) - m0 = qml.measure(wires=0) - qml.RY(theta, wires=1) - m1 = qml.measure(wires=1) - return qml.expval(~m0 + m1) - - print(circuit(1.23, 4.56)) - ``` - ``` - 1.2430187928114291 - ``` - - * By using a list of mid-circuit measurement values: - - ```python - import pennylane as qml - - dev = qml.device("default.qubit") - - @qml.qnode(dev) - def circuit(phi, theta): - qml.RX(phi, wires=0) - m0 = qml.measure(wires=0) - qml.RY(theta, wires=1) - m1 = qml.measure(wires=1) - return qml.sample([m0, m1]) - - print(circuit(1.23, 4.56, shots=5)) - ``` - ``` - [[0 1] - [0 1] - [0 0] - [1 0] - [0 1]] - ``` - - This feature is supported on `default.qubit`, `default.qubit.legacy`, and `default.mixed`. To - learn more about which measurements and arithmetic operators are supported, refer to the - [Measurements](https://docs.pennylane.ai/en/stable/introduction/measurements.html) page and the - documentation for [`qml.measure`](https://docs.pennylane.ai/en/stable/code/api/pennylane.measure.html). +

Catalyst is seamlessly integrated with PennyLane βš—οΈ

@@ -118,8 +118,8 @@ pip install pennylane-catalyst ``` - The [`qml.compiler`](https://docs.pennylane.ai/en/latest/code/qml_compiler.html) - module provides support for hybrid quantum-classical compilation. + The [qml.compiler](https://docs.pennylane.ai/en/latest/code/qml_compiler.html) + module provides support for hybrid quantum-classical compilation. [(#4692)](https://github.com/PennyLaneAI/pennylane/pull/4692) Through the use of the `qml.qjit` decorator, entire workflows can be JIT @@ -149,12 +149,11 @@ ``` Currently, PennyLane supports the [Catalyst hybrid compiler](https://github.com/pennylaneai/catalyst) - hybrid compiler with the `qml.qjit` decorator. A significant benefit of Catalyst + with the `qml.qjit` decorator. A significant benefit of Catalyst is the ability to preserve complex control flow around quantum operations β€” such as - if statements and for loops, and including measurement feedback β€” during compilation, + `if` statements and `for` loops, and including measurement feedback β€” during compilation, while continuing to support end-to-end autodifferentiation. - * The following functions can now be used with the `qml.qjit` decorator: `qml.grad`, `qml.jacobian`, `qml.vjp`, `qml.jvp`, and `qml.adjoint`. [(#4709)](https://github.com/PennyLaneAI/pennylane/pull/4709) @@ -194,6 +193,19 @@ `qml.while_loop`, and `qml.cond`. [(#4698)](https://github.com/PennyLaneAI/pennylane/pull/4698) + `qml.for_loop` and `qml.while_loop` can be deployed as decorators on functions that are the + body of the loop. The arguments to both follow typical conventions: + + ``` + @qml.for_loop(lower_bound, upper_bound, step) + ``` + + ``` + @qml.while_loop(cond_function) + ``` + + Here is a concrete example with `qml.for_loop`: + ``` python dev = qml.device("lightning.qubit", wires=1) @@ -223,62 +235,73 @@

Decompose circuits into the Clifford+T gateset 🧩

* The new `qml.clifford_t_decomposition()` transform provides an approximate breakdown - of an input circuit into the [Clifford+T](https://en.wikipedia.org/wiki/Clifford_gates) - gateset. Behind the scenes, this decomposition is enacted via the `sk_decomposition()` + of an input circuit into the [Clifford+T gateset](https://en.wikipedia.org/wiki/Clifford_gates). + Behind the scenes, this decomposition is enacted via the `sk_decomposition()` function using the Solovay-Kitaev algorithm. [(#4801)](https://github.com/PennyLaneAI/pennylane/pull/4801) [(#4802)](https://github.com/PennyLaneAI/pennylane/pull/4802) - Given a total circuit error `epsilon=0.001`, the following circuit can be decomposed: + The Solovay-Kitaev algorithm *approximately* decomposes a quantum circuit into the Clifford+T + gateset. To account for this, a desired total circuit decomposition error, `epsilon`, must be + specified when using `qml.clifford_t_decomposition`: ```python - import pennylane as qml + dev = qml.device("default.qubit") - with qml.tape.QuantumTape() as circuit: + @qml.qnode(dev) + def circuit(): qml.RX(1.1, 0) - qml.CNOT([0, 1]) - qml.RY(2.2, 0) + return qml.state() - (circuit,), _ = qml.clifford_t_decomposition(circuit, 0.001) + circuit = qml.clifford_t_decomposition(circuit, epsilon=0.1) ``` + ```pycon + >>> print(qml.draw(circuit)()) + 0: ──T†──H──T†──H──T──H──T──H──T──H──T──H──T†──H──T†──T†──H──T†──H──T──H──T──H──T──H──T──H──T†──H - The resource requirements of this circuit can also be evaluated. + ───T†──H──T──H──GlobalPhase(0.39)── + ``` + + The resource requirements of this circuit can also be evaluated: ```pycon - >>> circuit.specs["resources"] - wires: 2 - gates: 49770 - depth: 49770 + >>> with qml.Tracker(dev) as tracker: + ... circuit() + >>> resources_lst = tracker.history["resources"] + >>> resources_lst[0] + wires: 1 + gates: 34 + depth: 34 shots: Shots(total=None) gate_types: - {'Adjoint(T)': 13647, 'Hadamard': 22468, 'T': 13651, 'CNOT': 1, 'Adjoint(S)': 1, 'S': 1, 'GlobalPhase': 1} + {'Adjoint(T)': 8, 'Hadamard': 16, 'T': 9, 'GlobalPhase': 1} gate_sizes: - {1: 49768, 2: 1, 0: 1} + {1: 33, 0: 1} ```

Use an iterative approach for quantum phase estimation πŸ”„

-* Iterative Quantum Phase Estimation is now available from `qml.iterative_qpe`. +* [Iterative Quantum Phase Estimation](https://arxiv.org/pdf/quant-ph/0610214.pdf) + is now available with `qml.iterative_qpe`. [(#4804)](https://github.com/PennyLaneAI/pennylane/pull/4804) The subroutine can be used similarly to mid-circuit measurements: ```python - import pennylane as qml - dev = qml.device("default.qubit", shots = 5) + dev = qml.device("default.qubit", shots=5) @qml.qnode(dev) def circuit(): # Initial state - qml.PauliX(wires = [0]) + qml.PauliX(wires=[0]) # Iterative QPE - measurements = qml.iterative_qpe(qml.RZ(2., wires = [0]), ancilla = [1], iters = 3) + measurements = qml.iterative_qpe(qml.RZ(2., wires=[0]), ancilla=[1], iters=3) - return [qml.sample(op = meas) for meas in measurements] + return [qml.sample(op=meas) for meas in measurements] ``` ```pycon @@ -286,24 +309,13 @@ [array([0, 0, 0, 0, 0]), array([1, 0, 0, 0, 0]), array([0, 1, 1, 1, 1])] ``` - The i-th element in the list refers to the 5 samples generated by the i-th measurement of the algorithm. - -* `devices.qutrit_mixed.initialize_state` module added with functionality to initialize qutrit mixed states. - [(#4861)](https://github.com/PennyLaneAI/pennylane/pull/4861) + The :math:`i`-th element in the list refers to the 5 samples generated by the :math:`i`-th measurement of the algorithm.

Improvements πŸ› 

-* Implemented the method `process_counts` in the `ProbabilityMP` class. - [(#4952)](https://github.com/PennyLaneAI/pennylane/pull/4952) - -* `ClassicalShadow.entropy` now uses the algorithm outlined in - [1106.5458](https://arxiv.org/abs/1106.5458) to project the approximate density matrix - (with potentially negative eigenvalues) onto the closest valid density matrix. - [(#4959)](https://github.com/PennyLaneAI/pennylane/pull/4959) -

Community contributions πŸ₯³

-* The `+=` operand can now be used with a `PauliSentence`, which has also provided +* The `+=` operand can now be used with a `PauliSentence`, which has also provides a performance boost. [(#4662)](https://github.com/PennyLaneAI/pennylane/pull/4662) @@ -313,11 +325,20 @@ * `qml.draw` and `qml.draw_mpl` now render operator IDs. [(#4749)](https://github.com/PennyLaneAI/pennylane/pull/4749) -* Non-parametric operators such as `Barrier`, `Snapshot` and `Wirecut` have been grouped together and moved to `pennylane/ops/meta.py`. + The ID can be specified as a keyword argument when instantiating an operator: + + ```pycon + >>> def circuit(): + ... qml.RX(0.123, id="data", wires=0) + >>> print(qml.draw(circuit)()) + 0: ──RX(0.12,"data")── + ``` + +* Non-parametric operators such as `Barrier`, `Snapshot`, and `Wirecut` have been grouped together and moved to `pennylane/ops/meta.py`. Additionally, the relevant tests have been organized and placed in a new file, `tests/ops/test_meta.py`. [(#4789)](https://github.com/PennyLaneAI/pennylane/pull/4789) -* `TRX`, `TRY`, and `TRZ` operators are now differentiable via backpropagation on `default.qutrit`. +* The `TRX`, `TRY`, and `TRZ` operators are now differentiable via backpropagation on `default.qutrit`. [(#4790)](https://github.com/PennyLaneAI/pennylane/pull/4790) * The function `qml.equal` now supports `ControlledSequence` operators. @@ -329,35 +350,43 @@ * `==` and `!=` operands can now be used with `TransformProgram` and `TransformContainers` instances. [(#4858)](https://github.com/PennyLaneAI/pennylane/pull/4858) -* `qml.equal` now supports comparison of `QuantumScript` and `BasisRotation` objects +* A `qutrit_mixed` module has been added to `qml.devices` to store helper functions for a future qutrit + mixed-state device. A function called `create_initial_state` has been added to this module that creates + device-compatible initial states. + [(#4861)](https://github.com/PennyLaneAI/pennylane/pull/4861) + +* The function `qml.Snapshot` now supports arbitrary state-based measurements (i.e., measurements of type `StateMeasurement`). + [(#4876)](https://github.com/PennyLaneAI/pennylane/pull/4908) + +* `qml.equal` now supports the comparison of `QuantumScript` and `BasisRotation` objects. [(#4902)](https://github.com/PennyLaneAI/pennylane/pull/4902) [(#4919)](https://github.com/PennyLaneAI/pennylane/pull/4919) -* The function ``qml.Snapshot`` now supports arbitrary measurements of type ``StateMeasurement``. - [(#4876)](https://github.com/PennyLaneAI/pennylane/pull/4908) +* The function `qml.draw_mpl` now accept a keyword argument `fig` to specify the output figure window. + [(#4956)](https://github.com/PennyLaneAI/pennylane/pull/4956) * The function ``qml.draw_mpl`` now accept a keyword argument ``fig`` to specify the output figure window. [(#4956)](https://github.com/PennyLaneAI/pennylane/pull/4956)

Better support for batching

-* `default.qubit` now can evolve already batched states with `ParametrizedEvolution` - [(#4863)](https://github.com/PennyLaneAI/pennylane/pull/4863) - -* `AmplitudeEmbedding` now supports batching when used with Tensorflow. +* `qml.AmplitudeEmbedding` now supports batching when used with Tensorflow. [(#4818)](https://github.com/PennyLaneAI/pennylane/pull/4818) +* `default.qubit` can now evolve already batched states with `qml.pulse.ParametrizedEvolution`. + [(#4863)](https://github.com/PennyLaneAI/pennylane/pull/4863) + * `qml.ArbitraryUnitary` now supports batching. [(#4745)](https://github.com/PennyLaneAI/pennylane/pull/4745) -* Operator and tape batch sizes are evaluated lazily. +* Operator and tape batch sizes are evaluated lazily, helping run expensive computations less frequently + and an issue with Tensorflow pre-computing batch sizes. [(#4911)](https://github.com/PennyLaneAI/pennylane/pull/4911)

Performance improvements and benchmarking

-* Autograd, PyTorch, and JAX can now use VJPs provided by the device from the new device API. If a device provides - a vector-Jacobian product, this can be selected by providing `device_vjp=True` to - `qml.QNode` or `qml.execute`. +* Autograd, PyTorch, and JAX can now use vector-Jacobian products (VJPs) provided by the device from the new device API. If a device provides + a VJP, this can be selected by providing `device_vjp=True` to a QNode or `qml.execute`. [(#4935)](https://github.com/PennyLaneAI/pennylane/pull/4935) [(#4557)](https://github.com/PennyLaneAI/pennylane/pull/4557) [(#4654)](https://github.com/PennyLaneAI/pennylane/pull/4654) @@ -394,27 +423,27 @@ * A new pipeline to run benchmarks and plot graphs comparing with a fixed reference has been added. This pipeline will run on a schedule and can be activated on a PR with the label `ci:run_benchmarks`. [(#4741)](https://github.com/PennyLaneAI/pennylane/pull/4741) +* `default.qubit` now supports adjoint differentiation for arbitrary diagonal state-based measurements. + [(#4865)](https://github.com/PennyLaneAI/pennylane/pull/4865) + * The benchmarks pipeline has been expanded to export all benchmark data to a single JSON file and a CSV file with runtimes. This includes all references and local benchmarks. [(#4873)](https://github.com/PennyLaneAI/pennylane/pull/4873) -

Other improvements

+

Final phase of updates to transforms

-* `default.qubit` now supports adjoint differentiation for arbitrary diagonal state-based measurements. - [(#4865)](https://github.com/PennyLaneAI/pennylane/pull/4865) - -* `qml.quantum_monte_carlo` now uses the new transform system. +* `qml.quantum_monte_carlo` and `qml.simplify` now use the new transform system. [(#4708)](https://github.com/PennyLaneAI/pennylane/pull/4708/) - -* `qml.simplify` now uses the new transforms API. [(#4949)](https://github.com/PennyLaneAI/pennylane/pull/4949) -* The formal requirement that type hinting be providing when using +* The formal requirement that type hinting be provided when using the `qml.transform` decorator has been removed. Type hinting can still be used, but is now optional. Please use a type checker such as [mypy](https://github.com/python/mypy) if you wish to ensure types are being passed correctly. [(#4942)](https://github.com/PennyLaneAI/pennylane/pull/4942/) +

Other improvements

+ * `SampleMeasurement` now has an optional method `process_counts` for computing the measurement results from a counts dictionary. [(#4941)](https://github.com/PennyLaneAI/pennylane/pull/4941/) @@ -422,7 +451,7 @@ * A new function called `ops.functions.assert_valid` has been added for checking if an `Operator` class is defined correctly. [(#4764)](https://github.com/PennyLaneAI/pennylane/pull/4764) -* `Shots` can now be scaled with `*` via the `__mul__` and `__rmul__` dunders. +* `Shots` objects can now be multiplied by scalar values. [(#4913)](https://github.com/PennyLaneAI/pennylane/pull/4913) * `GlobalPhase` now decomposes to nothing in case devices do not support global phases. @@ -441,7 +470,7 @@ `SparseHamiltonian`. [(#4828)](https://github.com/PennyLaneAI/pennylane/pull/4828) -* `trainable_params` can now be set on initialization of `QuantumScript` instead of having to set the +* `trainable_params` can now be set upon initialization of a `QuantumScript` instead of having to set the parameter after initialization. [(#4877)](https://github.com/PennyLaneAI/pennylane/pull/4877) @@ -455,11 +484,8 @@ style, but with sketch-style lines. [(#4880)](https://github.com/PennyLaneAI/pennylane/pull/4880) -* `Conditional` and `MeasurementValue` objects now implement `map_wires`. - [(#4884)](https://github.com/PennyLaneAI/pennylane/pull/4884) - * Operators now define a `pauli_rep` property, an instance of `PauliSentence`, defaulting - to `None` if the operator has not defined it (or has no definition in the pauli basis). + to `None` if the operator has not defined it (or has no definition in the Pauli basis). [(#4915)](https://github.com/PennyLaneAI/pennylane/pull/4915) * `qml.ShotAdaptiveOptimizer` can now use a multinomial distribution for spreading shots across @@ -479,11 +505,20 @@ that they follow PennyLane `Operator` standards. [(#4922)](https://github.com/PennyLaneAI/pennylane/pull/4922) +* Probability measurements can now be calculated from a `counts` dictionary with the addition of a + `process_counts` method in the `ProbabilityMP` class. + [(#4952)](https://github.com/PennyLaneAI/pennylane/pull/4952) + +* `ClassicalShadow.entropy` now uses the algorithm outlined in + [1106.5458](https://arxiv.org/abs/1106.5458) to project the approximate density matrix + (with potentially negative eigenvalues) onto the closest valid density matrix. + [(#4959)](https://github.com/PennyLaneAI/pennylane/pull/4959) +

Breaking changes πŸ’”

-* The functions `qml.transforms.one_qubit_decomposition`, `qml.transforms.two_qubit_decomposition`, - `qml.transforms.sk_decomposition` were moved to respectively, `qml.ops.one_qubit_decomposition`, `qml.ops.two_qubit_decomposition`, - `qml.ops.sk_decomposition`. +* The functions `qml.transforms.one_qubit_decomposition`, `qml.transforms.two_qubit_decomposition`, and + `qml.transforms.sk_decomposition` were moved to `qml.ops.one_qubit_decomposition`, `qml.ops.two_qubit_decomposition`, and + `qml.ops.sk_decomposition`, respectively. [(#4906)](https://github.com/PennyLaneAI/pennylane/pull/4906) * The function `qml.transforms.classical_jacobian` has been moved to the gradients module @@ -548,36 +583,39 @@

Documentation πŸ“

-* Documentation for unitaries and operations decompositions was moved from `qml.transforms` to `qml.ops.ops_math`. +* Documentation for unitaries and operations' decompositions has been moved from `qml.transforms` to `qml.ops.ops_math`. [(#4906)](https://github.com/PennyLaneAI/pennylane/pull/4906) * Documentation for `qml.metric_tensor` and `qml.adjoint_metric_tensor` and `qml.transforms.classical_jacobian` - are now accessible via the gradients API page `qml.gradients` in the documentation. + is now accessible via the gradients API page `qml.gradients` in the documentation. [(#4900)](https://github.com/PennyLaneAI/pennylane/pull/4900) -* Documentation for `qml.specs` was moved to the resource module. +* Documentation for `qml.specs` has been moved to the `resource` module. [(#4904)](https://github.com/PennyLaneAI/pennylane/pull/4904) -* Documentation for QCut has moved to its own API page `qml.qcut`. +* Documentation for QCut has been moved to its own API page: `qml.qcut`. [(#4819)](https://github.com/PennyLaneAI/pennylane/pull/4819) * The documentation page for `qml.measurements` now links top-level accessible functions (e.g., `qml.expval`) to their top-level pages rather than their module-level pages (e.g., `qml.measurements.expval`). [(#4750)](https://github.com/PennyLaneAI/pennylane/pull/4750) -* Information to the documentation for `qml.matrix` about wire ordering has been added for using `qml.matrix` on a - `QNode` which uses a device with `device.wires=None`. +* Information for the documentation of `qml.matrix` about wire ordering has been added for using `qml.matrix` on a + QNode which uses a device with `device.wires=None`. [(#4874)](https://github.com/PennyLaneAI/pennylane/pull/4874)

Bug fixes πŸ›

+* `qml.map_wires` now works properly with `qml.cond` and `qml.measure`. + [(#4884)](https://github.com/PennyLaneAI/pennylane/pull/4884) + * `Pow` operators are now picklable. [(#4966)](https://github.com/PennyLaneAI/pennylane/pull/4966) * Finite differences and SPSA can now be used with tensorflow-autograph on setups that were seeing a bus error. [(#4961)](https://github.com/PennyLaneAI/pennylane/pull/4961) -* `qml.cond` no longer incorrectly queues operators used as qfunc arguments. +* `qml.cond` no longer incorrectly queues operators used arguments. [(#4948)](https://github.com/PennyLaneAI/pennylane/pull/4948) * `Attribute` objects now return `False` instead of raising a `TypeError` when checking if an object is inside @@ -616,13 +654,10 @@ an operation that has a parameter with `grad_method=None` is present. [(#4820)](https://github.com/PennyLaneAI/pennylane/pull/4820) -* `MottonenStatePreparation` now raises an error if decomposing a broadcasted state vector. +* `MottonenStatePreparation` and `BasisStatePreparation` now raise an error when decomposing a broadcasted state vector. [(#4767)](https://github.com/PennyLaneAI/pennylane/pull/4767) -* `BasisStatePreparation` now raises an error if decomposing a broadcasted state vector. - [(#4767)](https://github.com/PennyLaneAI/pennylane/pull/4767) - -* Gradient transforms now work with overridden shot vectors and default qubit. +* Gradient transforms now work with overridden shot vectors and `default.qubit`. [(#4795)](https://github.com/PennyLaneAI/pennylane/pull/4795) * Any `ScalarSymbolicOp`, like `Evolution`, now states that it has a matrix if the target @@ -633,7 +668,7 @@ wire order. [(#4781)](https://github.com/PennyLaneAI/pennylane/pull/4781) -* `transpile` can now handle measurements that are broadcasted onto all wires. +* `qml.transforms.transpile` can now handle measurements that are broadcasted onto all wires. [(#4793)](https://github.com/PennyLaneAI/pennylane/pull/4793) * Parametrized circuits whose operators do not act on all wires return PennyLane tensors instead of NumPy arrays, as @@ -641,7 +676,7 @@ [(#4811)](https://github.com/PennyLaneAI/pennylane/pull/4811) [(#4817)](https://github.com/PennyLaneAI/pennylane/pull/4817) -* `merge_amplitude_embeddings` no longer depends on queuing, allowing it to work as expected +* `qml.transforms.merge_amplitude_embedding` no longer depends on queuing, allowing it to work as expected with QNodes. [(#4831)](https://github.com/PennyLaneAI/pennylane/pull/4831) @@ -649,7 +684,7 @@ integer power. [(#4827)](https://github.com/PennyLaneAI/pennylane/pull/4827) -* The text drawer has been fixed to correctly label `qinfo` measurements, as well as `qml.classical_shadow` +* The text drawer has been fixed to correctly label `qml.qinfo` measurements, as well as `qml.classical_shadow` `qml.shadow_expval`. [(#4803)](https://github.com/PennyLaneAI/pennylane/pull/4803) @@ -667,6 +702,9 @@ * Conversion of circuits to openqasm now decomposes to a depth of 10, allowing support for operators requiring more than 2 iterations of decomposition, such as the `ApproxTimeEvolution` gate. [(#4951)](https://github.com/PennyLaneAI/pennylane/pull/4951) + +* `MPLDrawer` does not add the bonus space for classical wires when no classical wires are present. + [(#5987)](https://github.com/PennyLaneAI/pennylane/pull/4987)

Contributors ✍️

diff --git a/pennylane/drawer/mpldrawer.py b/pennylane/drawer/mpldrawer.py index f5f9894c9af..c932be2287b 100644 --- a/pennylane/drawer/mpldrawer.py +++ b/pennylane/drawer/mpldrawer.py @@ -269,7 +269,8 @@ def __init__(self, n_layers, n_wires, c_wires=0, wire_options=None, figsize=None ## Creating figure and ax if figsize is None: - figsize = (self.n_layers + 3, self.n_wires + self._cwire_scaling * c_wires + 1.5) + figheight = self.n_wires + self._cwire_scaling * c_wires + 1 + 0.5 * (c_wires > 0) + figsize = (self.n_layers + 3, figheight) if fig is None: self._fig = plt.figure(figsize=figsize) @@ -282,7 +283,7 @@ def __init__(self, n_layers, n_wires, c_wires=0, wire_options=None, figsize=None self._ax = self._fig.add_axes( [0, 0, 1, 1], xlim=(-2, self.n_layers + 1), - ylim=(-1, self.n_wires + self._cwire_scaling * c_wires + 0.5), + ylim=(-1, self.n_wires + self._cwire_scaling * c_wires + 0.5 * (c_wires > 0)), xticks=[], yticks=[], ) diff --git a/tests/drawer/test_mpldrawer.py b/tests/drawer/test_mpldrawer.py index cf2da7d6077..a4f8e8a5399 100644 --- a/tests/drawer/test_mpldrawer.py +++ b/tests/drawer/test_mpldrawer.py @@ -42,7 +42,7 @@ def test_figsize_wires(self, n_wires, n_layers): drawer = MPLDrawer(n_wires=n_wires, n_layers=n_layers) assert drawer.fig.get_figwidth() == (n_layers + 3) - assert drawer.fig.get_figheight() == (n_wires + 1.5) + assert drawer.fig.get_figheight() == (n_wires + 1) drawer = MPLDrawer(n_wires=n_wires, n_layers=n_layers) @@ -55,6 +55,20 @@ def test_figsize_wires(self, n_wires, n_layers): assert line.get_ydata() == (wire, wire) plt.close() + def test_figsize_classical_wires(self): + """Test the figsize is correct if classical wires are present.""" + n_wires = 4 + c_wires = 4 + n_layers = 1 + + drawer = MPLDrawer(n_wires=n_wires, n_layers=n_layers, c_wires=c_wires) + + assert drawer.fig.get_figheight() == n_wires + 1 + 0.25 * 4 + 0.5 + assert drawer.fig.get_figwidth() == n_layers + 3 + + assert drawer.ax.get_xlim() == (-2, 2) + assert drawer.ax.get_ylim() == (n_wires + 0.25 * 4 + 0.5, -1) + def test_customfigsize(self): """Tests a custom figsize alters the size"""