Add transform for quantum Monte Carlo #1316

trbromley · 2021-05-14T18:44:20Z

Context:

Adds a quantum Monte Carlo transform. Given an input circuit, the output will be a transformed circuit to do the full QMC algorithm.

Description of the Change:

Adds the transform to the qml.transforms.qmc module, along with supporting functionality.

Benefits:

This transform can be used for resource estimation of the number of gates required to perform quantum Monte Carlo. It can also, in principle, be compatible with a hardware implementation (if a suitable low-noise, high-depth device were available).

Possible Drawbacks:

Potential confusion with the existing QuantumMonteCarlo template, which does the unitary-based version (not compatible with hardware or resource estimation).

The example uses pennylane.templates.state_preparations.mottonen._uniform_rotation_dagger for the R matrix. Should we consider pulling that out and giving a nicer name? Can we use an alternative in the example?

codecov · 2021-05-17T16:31:19Z

Codecov Report

Merging #1316 (ce2e393) into master (7bf2d17) will increase coverage by 0.00%.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##           master    #1316   +/-   ##
=======================================
  Coverage   98.17%   98.18%           
=======================================
  Files         155      156    +1     
  Lines       11631    11685   +54     
=======================================
+ Hits        11419    11473   +54     
  Misses        212      212

Impacted Files	Coverage Δ
pennylane/__init__.py	`98.57% <ø> (ø)`
pennylane/templates/subroutines/qmc.py	`100.00% <ø> (ø)`
pennylane/transforms/__init__.py	`100.00% <100.00%> (ø)`
pennylane/transforms/qmc.py	`100.00% <100.00%> (ø)`

Continue to review full report at Codecov.

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ixfoduap

Beautiful addition. I'm suggesting minor improvements to the docstrings, otherwise looks good to go from my side, I couldn't see any issues

.github/CHANGELOG.md

ixfoduap · 2021-06-03T15:55:59Z

pennylane/transforms/qmc.py

+    conventional multi-controlled-Z gate with an additional bit flip on each qubit before and after.
+
+    This function performs the multi-controlled-Z gate via a multi-controlled-X gate by picking an
+    arbitrary target wire to perform the X and adding a Hadamard on that wire either side of the


So this works for any target wire? I suppose, but just checking

Yes, the target wire can be any wire in wires. Here is a sanity check:

import pennylane as qml import numpy as np wires = 3 dev = qml.device("default.qubit", wires=wires) Z = np.zeros((2 ** wires, 2 ** wires)) np.fill_diagonal(Z, 1) Z[-1, -1] = -1 target_wire = 0 control_wires = set(range(wires)) - set([target_wire]) @qml.qnode(dev) def find_unitary(state): qml.QubitStateVector(state, wires=range(wires)) qml.Hadamard(wires=target_wire) qml.MultiControlledX(wires=target_wire, control_wires=control_wires) qml.Hadamard(wires=target_wire) qml.QubitUnitary(Z, wires=range(wires)) return qml.state() I = np.eye(2 ** wires) U = np.real_if_close(np.array([find_unitary(state) for state in I]).T) assert np.allclose(U, I)

pennylane/transforms/qmc.py

ixfoduap · 2021-06-03T16:03:44Z

pennylane/transforms/qmc.py

+    quantum phase estimation (see :class:`~.QuantumPhaseEstimation` for more details).
+
+    Args:
+        fn (Callable): a quantum function that applies quantum operations according to the


Any restrictions on this function? Can it have arbitrary inputs and outputs?

It can have any arguments and keyword arguments. The function itself should enact a quantum circuit, and it shouldn't return anything.

So far, we've been calling these things quantum functions, although I'm not sure how evolved or well-defined the concept is.

'quantum function' was originally defined on the glossary as a function that applies quantum operations, and returns a measurement statistic. I think it has since been removed, but mention of it remains in the QNode entry:

A quantum node consists of a quantum function (such as a variational circuit), and a device on which it executes.

We've since started using the term a bit more loosely; the original definition of a quantum function was strictly

def qfunc(params): quantum_operation() quantum_operation() quantum_operation() return measurement_statistic()

but we have since broadened it (in usage, not strictly in definition) to also include

def qfunc(params): quantum_operation() quantum_operation() quantum_operation()

This second usage is attractive because:

It still tends to make sense to users; they still have a Python function that applies quantum operations

It is more general than 'ansatz'; you often have a small quantum function that you use but wouldn't want to denote an 'ansatz'.

With templates now being Operation objects, it tends to work quite nicely. In the above example, quantum_operation() itself is a quantum function.

pennylane/transforms/qmc.py

ixfoduap · 2021-06-03T17:46:01Z

pennylane/transforms/qmc.py

+        algorithm
+
+        >>> qtape = qmc.qtape.expand(depth=1)
+        >>> qtape.get_resources()


Whaaat!?? 🤯 🤯 🤯 🤯
I had no idea we could do this. So cool!

Yes!! But don't advertise this yet as we don't want this to be user facing. Instead, we will be going with the following approaches:

Calculation of circuit depth #1293

https://github.com/PennyLaneAI/adrs/pull/13

In particular, the ability to do

>>> qml.specs(qmc)(*params) # get all specs >>> qml.depth(qml)(*params) # get a specific spec

Cool! @josh146 do you recommend removing this part then? On the other hand, it's a shame to hide the gate count.

Good question. I would say - leave it for now, but we need to remember to update it when the preferred UI changes 🙂

pennylane/transforms/qmc.py

ixfoduap · 2021-06-03T17:51:32Z

tests/transforms/test_qmc_transform.py

+    @qml.qnode(dev)
+    def unitary_z(basis_state):
+        qml.BasisState(basis_state, wires=range(n_wires))
+        circ()


Wait, what does circ() do?

circ() is the function passed into get_unitary(). It'll be the usual "quantum function" style of applying some PL ops and not returning anything. Then get_unitary() returns the unitary described by circ().

In practice in the tests below, circ() will be, for example the qmc circuit or just the controlled Z.

Got it!

Got confused, I thought this was a python function; didn't realize it was an argument to get_unitary

ixfoduap · 2021-06-03T17:54:17Z

tests/transforms/test_qmc_transform.py

+        A_wires = [0, "a", -1.1, -10, "bbb"]
+        target_wire = "Ancilla"
+        wires = A_wires + [target_wire]
+        estimation_wires = ["bob", -3, 42, "penny", "lane"]


🤣 Maybe this will be the way I remember to test unconventional wire naming: give them fun names!

Would be nice to get creative since I've used these names before!

Co-authored-by: ixfoduap <40441298+ixfoduap@users.noreply.github.com>

trbromley

Thanks for the review @ixfoduap!

trbromley · 2021-06-04T16:22:14Z

pennylane/transforms/qmc.py

+    conventional multi-controlled-Z gate with an additional bit flip on each qubit before and after.
+
+    This function performs the multi-controlled-Z gate via a multi-controlled-X gate by picking an
+    arbitrary target wire to perform the X and adding a Hadamard on that wire either side of the


Yes, the target wire can be any wire in wires. Here is a sanity check:

import pennylane as qml import numpy as np wires = 3 dev = qml.device("default.qubit", wires=wires) Z = np.zeros((2 ** wires, 2 ** wires)) np.fill_diagonal(Z, 1) Z[-1, -1] = -1 target_wire = 0 control_wires = set(range(wires)) - set([target_wire]) @qml.qnode(dev) def find_unitary(state): qml.QubitStateVector(state, wires=range(wires)) qml.Hadamard(wires=target_wire) qml.MultiControlledX(wires=target_wire, control_wires=control_wires) qml.Hadamard(wires=target_wire) qml.QubitUnitary(Z, wires=range(wires)) return qml.state() I = np.eye(2 ** wires) U = np.real_if_close(np.array([find_unitary(state) for state in I]).T) assert np.allclose(U, I)

trbromley · 2021-06-04T17:36:41Z

pennylane/transforms/qmc.py

+    quantum phase estimation (see :class:`~.QuantumPhaseEstimation` for more details).
+
+    Args:
+        fn (Callable): a quantum function that applies quantum operations according to the


It can have any arguments and keyword arguments. The function itself should enact a quantum circuit, and it shouldn't return anything.

So far, we've been calling these things quantum functions, although I'm not sure how evolved or well-defined the concept is.

trbromley · 2021-06-04T18:05:10Z

pennylane/transforms/qmc.py

+    if not wires.contains_wires(target_wire):
+        raise ValueError("The target wire must be contained within wires")
+
+    @wraps(fn)


It's defined here. The idea is to persist the name and docstring of the original function, rather than overwriting it with wrapper.

For example:

from functools import wraps def fn(): """Docstring""" ... def wrapper1(): """Wrong Docstring""" fn() return @wraps(fn) def wrapper2(): """Wrong Docstring""" fn() return

The output is:

>>> print("Name of wrapper1: ", wrapper1.__name__) Name of wrapper1: wrapper1 >>> print("Docstring of wrapper1:", wrapper1.__doc__) Docstring of wrapper1: Wrong Docstring >>> print("Name of wrapper2: ", wrapper2.__name__) Name of wrapper2: fn >>> print("Docstring of wrapper2:", wrapper2.__doc__) Docstring of wrapper2: Docstring

I'd never really used this before, so good chance for me to learn too!

trbromley · 2021-06-04T18:25:52Z

tests/transforms/test_qmc_transform.py

+    @qml.qnode(dev)
+    def unitary_z(basis_state):
+        qml.BasisState(basis_state, wires=range(n_wires))
+        circ()


circ() is the function passed into get_unitary(). It'll be the usual "quantum function" style of applying some PL ops and not returning anything. Then get_unitary() returns the unitary described by circ().

In practice in the tests below, circ() will be, for example the qmc circuit or just the controlled Z.

trbromley · 2021-06-04T18:26:47Z

tests/transforms/test_qmc_transform.py

+        A_wires = [0, "a", -1.1, -10, "bbb"]
+        target_wire = "Ancilla"
+        wires = A_wires + [target_wire]
+        estimation_wires = ["bob", -3, 42, "penny", "lane"]


Would be nice to get creative since I've used these names before!

pennylane/transforms/qmc.py

ixfoduap · 2021-06-04T19:51:08Z

tests/transforms/test_qmc_transform.py

+    @qml.qnode(dev)
+    def unitary_z(basis_state):
+        qml.BasisState(basis_state, wires=range(n_wires))
+        circ()


Got it!

Got confused, I thought this was a python function; didn't realize it was an argument to get_unitary

trbromley added 16 commits May 14, 2021 14:40

Update init

1c94a57

Add QMC

35cfc68

Add test

6243f96

Add to docstrings

9ff4f5a

Add test

8a49448

Remove test

bc08c1c

Tidy

7dc4cbe

Add qmc

61ebb08

Merge branch 'master' into add_qmc_transform

d8f6324

Rename test

fb452a3

Add test

c6489fa

No inheritance diagram and no classes heading

696d344

Be stricter with wires

cd93b64

Add integration test

51ff360

Apply black

a4891b6

Typo

d15e1ae

trbromley added this to the v0.16.0 milestone May 31, 2021

trbromley added 8 commits June 1, 2021 11:00

Merge branch 'master' into add_qmc_transform

86017e4

Fix

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Updates after read through

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Add to docstring

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Add to docstring

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Add example

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Add to docstrings

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Add to changelog

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trbromley changed the title ~~[WIP] Add transform for quantum Monte Carlo~~ Add transform for quantum Monte Carlo Jun 1, 2021

trbromley marked this pull request as ready for review June 1, 2021 18:10

trbromley requested review from ixfoduap and DSGuala June 1, 2021 18:11

trbromley and others added 2 commits June 1, 2021 14:28

Add note

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Merge branch 'master' into add_qmc_transform

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ixfoduap requested changes Jun 3, 2021

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trbromley and others added 4 commits June 4, 2021 12:01

Merge branch 'master' into add_qmc_transform

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Fix import

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Co-authored-by: ixfoduap <40441298+ixfoduap@users.noreply.github.com>

Updates

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trbromley commented Jun 4, 2021

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Fix space

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trbromley requested a review from ixfoduap June 4, 2021 18:39

ixfoduap approved these changes Jun 4, 2021

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trbromley merged commit 0dcbed5 into master Jun 4, 2021

trbromley deleted the add_qmc_transform branch June 4, 2021 21:10

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Add transform for quantum Monte Carlo #1316

Add transform for quantum Monte Carlo #1316

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Add transform for quantum Monte Carlo #1316

Add transform for quantum Monte Carlo #1316

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