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The hidden complexity of QP visualization in link previews
When sharing content online, presentation matters. A good title and description are important, but often what truly grabs attention is the preview image. For our Quantum Program (QP) visualizations, we wanted link previews on social media to display a dynamic, accurate visualization of the circuit associated with the link, rather than just a generic static image. This seemingly small feature turned 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 declares metadata: title, description, preview image through <meta> tags in the HTML<head>. So it works like a contract - we provide well-known special tags, and other 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 for dynamically injected tags to obtain a preview. They only analyze the static HTML served at page document request time.
Our front-end is a statically built single-page application (SPA). It means that for whichever page is requested, we serve the same static HTML with no <meta>tag variation. It's served straight from Amazon S3 behind CloudFront. This setup 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 is tricky.
Problem 2: Visualization only rendered in the browser
The visualization itself is rendered entirely in the front-end. That means the circuit image only exists in the user's browser after the app loads. To generate a shareable preview image, we needed a way to reproduce that rendering in an automated environment, open the same circuit, and capture a screenshot. In other words, we had to launch a headless browser, run the visualization, and save the result.
We couldn't simply offload this task to users' sessions. Letting users trigger arbitrary image uploads from their browsers would introduce serious content moderation risks, potentially resulting in content that is inappropriate, or inaccurate. Automatic verification of every upload would become another complex sub-feature, being far beyond the scope of the initial goal, while still not giving 100% confidence.
Problem 3: Unpredictable level of detail
Another challenge was deciding how the preview images should look. The visualization sizes vary significantly depending on the QP. A high-resolution, detail-heavy visualization isn't very helpful when social media shrinks it down to thumbnail size. We needed boundaries: clear enough to convey the circuit, but not 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 shared link with a unique preview image. The CloudFront Function acts as middleware, intercepting the request for a QP visualization page and modifying the HTML response returned from S3 before it reaches the client (the renderer of the preview).
Problem 2 solution: Automated screenshot service
We built an AWS Lambda function that launches a headless browser, loads the visualization, and takes a screenshot. This function is triggered whenever a new visualization is generated. The resulting image is stored and tied to the visualization's ID in its filename.
Problem 3 solution: Balancing detail and readability
Our earlier work introducing Variable View helped us a lot in getting compact visualization on its own. Having higher level perspective, it's less cluttered with low-level details like qubit grid.
Still, some visualizations can be large, esp. in width. So for previews, we decided to capture only a bounded top-left viewport, keeping the image compact and readable instead of exporting the full circuit and scaling it down.
As a side improvement, we also adjusted the handling of visualizations smaller than the viewport: now we trim the empty space around the content to use the available 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 careful coordination of infrastructure, automations, security considerations, and taking into account existing data flows.
The result is worth it: now, when someone shares a QP visualization, others see not just a link or a generic splash image, but a real visualization of this exact 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 description are important, but often what truly grabs attention is the preview image. For our Quantum Program (QP) visualizations, we wanted link previews on social media to display a dynamic, accurate visualization of the circuit associated with the link, rather than just a generic static image. This seemingly small feature turned 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 declares metadata: title, description, preview image through <meta> tags in the HTML<head>. So it works like a contract - we provide well-known special tags, and other 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 for dynamically injected tags to obtain a preview. They only analyze the static HTML served at page document request time.
Our front-end is a statically built single-page application (SPA). It means that for whichever page is requested, we serve the same static HTML with no <meta>tag variation. It's served straight from Amazon S3 behind CloudFront. This setup 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 is tricky.
Problem 2: Visualization only rendered in the browser
The visualization itself is rendered entirely in the front-end. That means the circuit image only exists in the user's browser after the app loads. To generate a shareable preview image, we needed a way to reproduce that rendering in an automated environment, open the same circuit, and capture a screenshot. In other words, we had to launch a headless browser, run the visualization, and save the result.
We couldn't simply offload this task to users' sessions. Letting users trigger arbitrary image uploads from their browsers would introduce serious content moderation risks, potentially resulting in content that is inappropriate, or inaccurate. Automatic verification of every upload would become another complex sub-feature, being far beyond the scope of the initial goal, while still not giving 100% confidence.
Problem 3: Unpredictable level of detail
Another challenge was deciding how the preview images should look. The visualization sizes vary significantly depending on the QP. A high-resolution, detail-heavy visualization isn't very helpful when social media shrinks it down to thumbnail size. We needed boundaries: clear enough to convey the circuit, but not 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 shared link with a unique preview image. The CloudFront Function acts as middleware, intercepting the request for a QP visualization page and modifying the HTML response returned from S3 before it reaches the client (the renderer of the preview).
Problem 2 solution: Automated screenshot service
We built an AWS Lambda function that launches a headless browser, loads the visualization, and takes a screenshot. This function is triggered whenever a new visualization is generated. The resulting image is stored and tied to the visualization's ID in its filename.
Problem 3 solution: Balancing detail and readability
Our earlier work introducing Variable View helped us a lot in getting compact visualization on its own. Having higher level perspective, it's less cluttered with low-level details like qubit grid.
Still, some visualizations can be large, esp. in width. So for previews, we decided to capture only a bounded top-left viewport, keeping the image compact and readable instead of exporting the full circuit and scaling it down.
As a side improvement, we also adjusted the handling of visualizations smaller than the viewport: now we trim the empty space around the content to use the available 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 careful coordination of infrastructure, automations, security considerations, and taking into account existing data flows.
The result is worth it: now, when someone shares a QP visualization, others see not just a link or a generic splash image, but a real visualization of this exact 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.
