Daniel Silver, Devesh Tiwari, Tirthak Patel, Aditya Ranjan, Harshitta Gandhi, William Cutler
We lifted 3 functions out of this paper's own repositories and ran 2 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| SilverEngineered/SliQ | canonical | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| filter_labels | Ran | SilverEngineered/SliQ/triplet_generator.py code served (permissive licence) · get_code("1daefae3a05a9f01") |
| load_data | Ran | SilverEngineered/SliQ/triplet_generator.py code served (permissive licence) · get_code("51182d9edfc11e95") |
| generate_pca_triplets | Not yet run | SilverEngineered/SliQ/triplet_generator.py code served (permissive licence) · get_code("84a2da0a111f6c95") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Exploration into quantum machine learning has grown tremendously in recent years due to the ability of quantum computers to speed up classical programs. However, these efforts have yet to solve unsupervised similarity detection tasks due to the challenge of porting them to run on quantum computers. To overcome this challenge, we propose SLIQ, the first open-sourced work for resource-efficient quantum similarity detection networks, built with practical and effective quantum learning and variance-reducing algorithms.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2309.15259")
get_code_for_paper("2309.15259")
have("2309.15259")
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