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The hidden complexity of QP visualization in link previews
When sharing content online, presentation matters. A good title and descriptionare important, but often what truly grabs attention is the preview image. Forour Quantum Program (QP) visualizations, we wanted link previews on social mediato display a dynamic, accurate visualization of the circuit associated with thelink, rather than just a generic static image. This seemingly small featureturned out to involve a surprising amount of technical complexity.
Technical background
Social media platforms rely on the Open Graph protocol (OG)to decide how links should be represented when shared. A web page declaresmetadata: title, description, preview image through <meta> tags in the HTML<head>. So it works like a contract - we provide well-known special tags, andother platforms seek them in HTML.
Problems we faced
Problem 1: Static SPA infrastructure
It is worth noting that platforms don't execute JavaScript or wait fordynamically injected tags to obtain a preview. They only analyze the static HTMLserved at page document request time.
Our front-end is a statically built single-page application (SPA). It means thatfor whichever page is requested, we serve the same static HTML with no <meta>tag variation. It's served straight from Amazon S3 behind CloudFront. Thissetup is fast and simple, but it also means we don't have Server-Side Rendering(SSR). Without SSR, injecting dynamic metadata into the HTML on the fly istricky.
Problem 2: Visualization only rendered in the browser
The visualization itself is rendered entirely in the front-end. That means thecircuit image only exists in the user's browser after the app loads. To generatea shareable preview image, we needed a way to reproduce that rendering in anautomated environment, open the same circuit, and capture a screenshot. In otherwords, we had to launch a headless browser, run the visualization, and save theresult.
We couldn't simply offload this task to users' sessions. Letting users triggerarbitrary image uploads from their browsers would introduce serious contentmoderation risks, potentially resulting in content that is inappropriate, orinaccurate. Automatic verification of every upload would become another complexsub-feature, being far beyond the scope of the initial goal, while still notgiving 100% confidence.
Problem 3: Unpredictable level of detail
Another challenge was deciding how the preview images should look. Thevisualization sizes vary significantly depending on the QP. A high-resolution,detail-heavy visualization isn't very helpful when social media shrinks it downto thumbnail size. We needed boundaries: clear enough to convey the circuit, butnot overwhelming or unreadable when scaled down.
Our approach
We tackled the problems one by one:
Problem 1 solution: Dynamic metadata injection
We used a CloudFront Function to inject the right <meta> tags dynamically,depending on the circuit ID in the URL. This allowed us to present each sharedlink with a unique preview image. The CloudFront Function acts as middleware,intercepting the request for a QP visualization page and modifying the HTMLresponse returned from S3 before it reaches the client (the renderer of thepreview).
Problem 2 solution: Automated screenshot service
We built an AWS Lambda function that launches a headless browser, loads thevisualization, and takes a screenshot. This function is triggered whenever a newvisualization is generated. The resulting image is stored and tied to thevisualization's ID in its filename.
Problem 3 solution: Balancing detail and readability
Our earlier workintroducing Variable View helped us a lot in getting compact visualization on its own. Having higher levelperspective, it's less cluttered with low-level details like qubit grid.
Still, some visualizations can be large, esp. in width. So for previews, wedecided to capture only a bounded top-left viewport, keeping the image compactand readable instead of exporting the full circuit and scaling it down.
As a side improvement, we also adjusted the handling of visualizations smallerthan the viewport: now we trim the empty space around the content to use theavailable space more effectively.
Solution architecture
In practice, the flow looks like this:
- Once a new visualization model is generated, our preview-rendering Lambda
function is being triggered. - The Lambda function renders it in a headless browser and saves the screenshot
as a file, named by QP ID. - When the visualization link is shared, the renderer requests the page HTML
for its metadata tags, and CloudFront injects OG tags pointing to the
corresponding stored image. - The link renderer (e.g., social media post card, or a messenger chat screen)
extracts the preview image URL and renders it as a regular static image, next
to the URL.
結論
At first glance, adding link previews for visualizations sounds like a small,almost cosmetic feature. But behind the scenes, it may require carefulcoordination of infrastructure, automations, security considerations, and takinginto account existing data flows.
The result is worth it: now, when someone shares a QP visualization, others seenot just a link or a generic splash image, but a real visualization of thisexact QP shared. It makes the content more engaging and understandable.
Even small user-facing improvements can demand surprisingly complex engineering,but that does not make them less impactful.
When sharing content online, presentation matters. A good title and descriptionare important, but often what truly grabs attention is the preview image. Forour Quantum Program (QP) visualizations, we wanted link previews on social mediato display a dynamic, accurate visualization of the circuit associated with thelink, rather than just a generic static image. This seemingly small featureturned out to involve a surprising amount of technical complexity.
Technical background
Social media platforms rely on the Open Graph protocol (OG)to decide how links should be represented when shared. A web page declaresmetadata: title, description, preview image through <meta> tags in the HTML<head>. So it works like a contract - we provide well-known special tags, andother platforms seek them in HTML.
Problems we faced
Problem 1: Static SPA infrastructure
It is worth noting that platforms don't execute JavaScript or wait fordynamically injected tags to obtain a preview. They only analyze the static HTMLserved at page document request time.
Our front-end is a statically built single-page application (SPA). It means thatfor whichever page is requested, we serve the same static HTML with no <meta>tag variation. It's served straight from Amazon S3 behind CloudFront. Thissetup is fast and simple, but it also means we don't have Server-Side Rendering(SSR). Without SSR, injecting dynamic metadata into the HTML on the fly istricky.
Problem 2: Visualization only rendered in the browser
The visualization itself is rendered entirely in the front-end. That means thecircuit image only exists in the user's browser after the app loads. To generatea shareable preview image, we needed a way to reproduce that rendering in anautomated environment, open the same circuit, and capture a screenshot. In otherwords, we had to launch a headless browser, run the visualization, and save theresult.
We couldn't simply offload this task to users' sessions. Letting users triggerarbitrary image uploads from their browsers would introduce serious contentmoderation risks, potentially resulting in content that is inappropriate, orinaccurate. Automatic verification of every upload would become another complexsub-feature, being far beyond the scope of the initial goal, while still notgiving 100% confidence.
Problem 3: Unpredictable level of detail
Another challenge was deciding how the preview images should look. Thevisualization sizes vary significantly depending on the QP. A high-resolution,detail-heavy visualization isn't very helpful when social media shrinks it downto thumbnail size. We needed boundaries: clear enough to convey the circuit, butnot overwhelming or unreadable when scaled down.
Our approach
We tackled the problems one by one:
Problem 1 solution: Dynamic metadata injection
We used a CloudFront Function to inject the right <meta> tags dynamically,depending on the circuit ID in the URL. This allowed us to present each sharedlink with a unique preview image. The CloudFront Function acts as middleware,intercepting the request for a QP visualization page and modifying the HTMLresponse returned from S3 before it reaches the client (the renderer of thepreview).
Problem 2 solution: Automated screenshot service
We built an AWS Lambda function that launches a headless browser, loads thevisualization, and takes a screenshot. This function is triggered whenever a newvisualization is generated. The resulting image is stored and tied to thevisualization's ID in its filename.
Problem 3 solution: Balancing detail and readability
Our earlier workintroducing Variable View helped us a lot in getting compact visualization on its own. Having higher levelperspective, it's less cluttered with low-level details like qubit grid.
Still, some visualizations can be large, esp. in width. So for previews, wedecided to capture only a bounded top-left viewport, keeping the image compactand readable instead of exporting the full circuit and scaling it down.
As a side improvement, we also adjusted the handling of visualizations smallerthan the viewport: now we trim the empty space around the content to use theavailable space more effectively.
Solution architecture
In practice, the flow looks like this:
- Once a new visualization model is generated, our preview-rendering Lambda
function is being triggered. - The Lambda function renders it in a headless browser and saves the screenshot
as a file, named by QP ID. - When the visualization link is shared, the renderer requests the page HTML
for its metadata tags, and CloudFront injects OG tags pointing to the
corresponding stored image. - The link renderer (e.g., social media post card, or a messenger chat screen)
extracts the preview image URL and renders it as a regular static image, next
to the URL.
結論
At first glance, adding link previews for visualizations sounds like a small,almost cosmetic feature. But behind the scenes, it may require carefulcoordination of infrastructure, automations, security considerations, and takinginto account existing data flows.
The result is worth it: now, when someone shares a QP visualization, others seenot just a link or a generic splash image, but a real visualization of thisexact QP shared. It makes the content more engaging and understandable.
Even small user-facing improvements can demand surprisingly complex engineering,but that does not make them less impactful.
