Shared experiment cleanup
As a user, I want to clean up my experiments before sharing them publicly through the dashboard. For example, to remove failed or irrelevant jobs.
AI feature - Best backend and backend options for haiqu.run
As an application scientist, I want to run my algorithm on the most relevant backends. I want AI to suggest a backend based on my algorithm and help me build the correct backend_option dictionary to pass to haiqu.run()
Subtlety spotter
If I am recreating something from a paper it can be helpful if I can state this at the beginning and have the AI point out parts that may functionally (obv the code will differ semantically) subtly differ from what is stated elsewhere. Not as a ‘you must change this‘ but more as a hint/warning. It’s perfectly possible some subtle alteration is intended, but it’s also possible that it’s not. I’m thinking niche qubit grouping choices, specific optimiser choices in specific param regimes, all the nasty things that can get pushed into a corner of an appendix
Feature Request - AI for plots
As a user, I want to quickly generate beautiful plots with the context of the experiment and execution results
As a Haiqu SDK user, I want
to have the ability to access external resources about Haiqu SDK with examples/notebooks to help me write my own code without looking much into documentation.
Feature Request - Use AI to explore use cases
As an application scientist, I want to explore use cases quickly. I want to define a problem statement, have an AI agent ask guiding questions, and build for me a script that uses an optimized quantum algorithm to find the solution to my problem.
Feature request - Plots connected to executions
As an application developer, I want to be able to associate my plots in the experiments with particular circuits and hardware executions
Feature request - sharable experiments
As an application developer, I want to be able to share my experiments with all the circuits and results with other users
Feature Request - Hitting my usage quota. Want easy upgrade path.
As a user, I’m hitting my usage quota and I want an easy way to upgrade my account. Ask: Add upgrade account to the dashboard sidebar nav and show me the available options.
Feature Request - Getting Started and Interactive Demo for Dashboard
As a user, I want to be know how to use the dashboard and what features are accessible to me. Ask: Consider a guided interactive demo: https://www.storylane.io/tutorials/how-to-use-linears-timeline-view Consider creating a checklist of notebooks that automatically check off when the last cell is run.
Feature Request - Better interface to notebooks
As a user I want to be able to rapidly find notebooks I was working. Ask: Show last n recently run notebooks on dashboard board. Let me click and open them. Link the experiment to the notebook and cell where it was initialized. Link the jobs to the notebook and cell where it was run.
Show total elapsed time in STATE COMPRESSION job progress
Currently, the STATE COMPRESSION job progress output includes messages such as approximation quality, fine-tuning completion, and CNOT count reduction. However, it does not display how long the job took to run. Feature request Add a final line showing the total elapsed time (wall-clock time) for the compression job. For example: Total elapsed time: 32.4 seconds Why this matters Helps users estimate computational cost. Makes it easier to compare performance across circuits or approximation levels. Improves transparency and usability.
Drawer: support matplotlib-compatible marker symbols (e.g., 'o')
The Drawer.plot method currently requires custom marker names such as "circle", "square", etc., and does not accept the standard matplotlib marker symbols like 'o', '^', 's'. This makes it harder to reuse code or switch between matplotlib and Drawer plots.