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Co-authored-by: ANT0N <39093564+AntonNI8@users.noreply.github.com> Co-authored-by: Guillermo Alonso-Linaje <65235481+KetpuntoG@users.noreply.github.com>
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drdren
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drdren
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Hi Jay, this is an excellent demo! We should be able to wrap this up quickly. We will brainstorm some ideas for images.
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| How to use PennyLane’s quantum resource estimator module for QSVT | |||
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Hi! I think we agreed to change the title to "How to estimate the resource cost of QSVT" for SEO reasons. Our posts that state "...PennyLane's resource estimation..." don't show up when people search for general resource estimation tools.
| How to use PennyLane’s quantum resource estimator module for QSVT | ||
| ================================================================= | ||
| .. meta:: | ||
| :property="og:description": Learn how to use PennyLane's estimator module to estimate the cost of QSVT |
| ================================================================= | ||
| .. meta:: | ||
| :property="og:description": Learn how to use PennyLane's estimator module to estimate the cost of QSVT | ||
| :property="og:image": https://pennylane.ai/qml/_static/demonstration_assets/resource_estimation.jpeg |
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We shall brainstorm a good and simple image here. How about the word QSVT and a resource scaling graph in the background going to the moon?
| - `QSVT in Practice <tutorial_apply_qsvt>`_ | ||
| - `How to implement QSVT on hardware <tutorial_qsvt_hardware>`_ | ||
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| However, simulations can only take us so far, and industrially relevant system sizes are often too large to |
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Instead, how about "It's important to estimate the resource cost of the QSVT algorithm, for any system size. Fortunately, PennyLane's resource :mod:~.pennylane.estimator module makes that easy, even if the QSVT problem you're interested in is too big to simulate right now. "
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| Estimating the cost of QSVT | ||
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| The logical cost of QSVT depends primarily on two factors, the **block encoding** operator and the **degree** of |
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I don't think this sentence is necessary! It's reasonable for what we are trying to achieve to just demonstrate an arbitrary polynomial transformation & block encoding.
| print(resources) | ||
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| ############################################################################## | ||
| # Representing the QSVT workflow like this allows us to easily upscale it to larger system sizes without |
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I absolutely love this paragraph.
Slight tweak:
Representing the QSVT workflow like this allows us to easily perform resource estimation of larger system sizes without any computational overheads.
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What does computational overhead mean here? Why is the cost to do RE the same for large systems as for small systems?
| encoding_wires = [f"e_{i}" for i in range(num_encoding_wires)] | ||
| lcu_wires = [f"t_{i}" for i in range(num_qubits)] | ||
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| Prep = qre.QROMStatePreparation( |
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People will copy-and-paste qre.QROMStatePreparation(...) for this larger example rather than qre.QubitUnitary(...) that you used before. Is this desirable? If so, should we explain it?
| print(resources) | ||
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| ############################################################################## | ||
| # With PennyLane's resource estimation functionality we can analyze the cost of QSVT workflows for large |
| # | ||
| # Suppose we already had a PennyLane circuit which used QSVT to apply the quintic polynomial transformation to | ||
| # :math:`A`. Here we can use `estimate() <https://docs.pennylane.ai/en/stable/code/api/pennylane.estimator.estimate.estimate.html>`_ | ||
| # on the circuit directly to obtain the resource estimate. |
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If that's the case, we can obtain the resource estimate with only a couple of lines of code with estimate() <https://docs.pennylane.ai/en/stable/code/api/pennylane.estimator.estimate.estimate.html>_.
| "executable_stable": true, | ||
| "executable_latest": true, | ||
| "dateOfPublication": "2026-01-30T10:00:00+00:00", | ||
| "dateOfLastModification": "2026-01-30T10:00:00+00:00", |
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We shall deal with the dates later. This comment is just to remind us.
2 participants
Title:
A simple demo on how to do resource estimation for
QSVTSummary:
Relevant references:
Possible Drawbacks:
Related GitHub Issues:
If you are writing a demonstration, please answer these questions to facilitate the marketing process.
GOALS — Why are we working on this now?
Eg. Promote a new PL feature or show a PL implementation of a recent paper.
AUDIENCE — Who is this for?
Eg. Chemistry researchers, PL educators, beginners in quantum computing.
KEYWORDS — What words should be included in the marketing post?
Which of the following types of documentation is most similar to your file?
(more details here)