diff --git a/CITATION.bib b/CITATION.bib index bebebaa..6d90fd4 100644 --- a/CITATION.bib +++ b/CITATION.bib @@ -1,10 +1,15 @@ -@misc{https://doi.org/10.48550/arxiv.2209.03965, - doi = {10.48550/ARXIV.2209.03965}, - url = {https://arxiv.org/abs/2209.03965}, - author = {Nguyen, Minh-Thi and Liu, Jin-Guo and Wurtz, Jonathan and Lukin, Mikhail D. and Wang, Sheng-Tao and Pichler, Hannes}, - keywords = {Quantum Physics (quant-ph), Disordered Systems and Neural Networks (cond-mat.dis-nn), Quantum Gases (cond-mat.quant-gas), Computational Complexity (cs.CC), Atomic Physics (physics.atom-ph), FOS: Physical sciences, FOS: Physical sciences, FOS: Computer and information sciences, FOS: Computer and information sciences}, - title = {Quantum optimization with arbitrary connectivity using Rydberg atom arrays}, - publisher = {arXiv}, - year = {2022}, - copyright = {arXiv.org perpetual, non-exclusive license} -} +@ARTICLE{Nguyen2022Quantum, + author = {{Nguyen}, Minh-Thi and {Liu}, Jin-Guo and {Wurtz}, Jonathan and {Lukin}, Mikhail D. and {Wang}, Sheng-Tao and {Pichler}, Hannes}, + title = "{Quantum optimization with arbitrary connectivity using Rydberg atom arrays}", + journal = {arXiv e-prints}, + keywords = {Quantum Physics, Condensed Matter - Disordered Systems and Neural Networks, Condensed Matter - Quantum Gases, Computer Science - Computational Complexity, Physics - Atomic Physics}, + year = 2022, + month = sep, + eid = {arXiv:2209.03965}, + pages = {arXiv:2209.03965}, +archivePrefix = {arXiv}, + eprint = {2209.03965}, + primaryClass = {quant-ph}, + adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv220903965N}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} +} \ No newline at end of file diff --git a/LICENSE b/LICENSE index a3cf8c4..8f3c377 100644 --- a/LICENSE +++ b/LICENSE @@ -1,21 +1,180 @@ -MIT License +The UnitDiskMapping.jl package is licensed under the Apache License, Version 2.0: -Copyright (c) 2021 QuEra Computing Inc. - -Permission is hereby granted, free of charge, to any person obtaining a copy -of this software and associated documentation files (the "Software"), to deal -in the Software without restriction, including without limitation the rights -to use, copy, modify, merge, publish, distribute, sublicense, and/or sell -copies of the Software, and to permit persons to whom the Software is -furnished to do so, subject to the following conditions: - -The above copyright notice and this permission notice shall be included in all -copies or substantial portions of the Software. - -THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR -IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, -FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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UnitDiskMapping is a   @@ -10,18 +10,18 @@ Julia Language -   package for reducing a generic (weighted) maximum independent set problem, QUBO problem or integer factoring problem to a maximum independent set problem on a unit disk grid graph (or hardcore lattice gas in physics). To install UnitDiskMapping, +   package for reducing a generic maximum (weighted) independent set problem, Quadratic unconstrained binary optimization (QUBO) problem or integer factorization problem to a maximum independent set problem on a unit disk grid graph (or hardcore lattice gas in physics). To install UnitDiskMapping, please open - Julia's interactive session (known as REPL) and press ] key in the REPL to use the package mode, then type the following command + Julia's interactive session (known as REPL) and press the ] key in the REPL to use the package mode, and then type the following command:

-For installing the current master branch, please type +For installing the current master branch, please type: ```julia pkg> add https://github.com/QuEraComputing/UnitDiskMapping.jl.git ``` -For stable release (not yet ready) +For stable release (not yet ready): ```julia pkg> add UnitDiskMapping @@ -29,7 +29,7 @@ pkg> add UnitDiskMapping ## Examples -Please check this [notebook](https://refactored-spork-2b31d0c0.pages.github.io/notebooks/tutorial.html), which contains the following examples +Please check this [notebook](https://github.com/QuEraComputing/UnitDiskMapping.jl/blob/main/notebooks/tutorial.jl), which contains the following examples: * Generic Unweighted Mapping * Generic Weighted Mapping @@ -38,20 +38,20 @@ Please check this [notebook](https://refactored-spork-2b31d0c0.pages.github.io/n * QUBO problem on a square lattice * Factoring -To run the notebook locally you will need the [Pluto](https://github.com/fonsp/Pluto.jl) and [GenericTensorNetworks](https://github.com/QuEraComputing/GenericTensorNetworks.jl) Julia packages installed. You can run the following after entering Package mode: +To run the notebook locally, you will need the [Pluto](https://github.com/fonsp/Pluto.jl) and [GenericTensorNetworks](https://github.com/QuEraComputing/GenericTensorNetworks.jl) Julia packages installed. You can run the following after entering the Package mode: ``` pkg> add Pluto pkg> add GenericTensorNetworks ``` -and returning to the Julia REPL (you can do this by hitting Backspace in Package mode) run: +and returning to the Julia REPL (you can do this by hitting Backspace in the Package mode) to run: ``` julia> import Pluto; Pluto.run() ``` -in the `notebooks` directory of this project. At this point your browser should automatically launch and display a list of available notebooks you can run. You should just see `tutorial.jl` listed. +in the `notebooks` directory of this project. At this point, your browser should automatically launch and display a list of available notebooks you can run. You should just see `tutorial.jl` listed. ## Supporting and Citing -Much of the software in this ecosystem was developed as part of academic research. If you would like to help support it, please star the repository as such metrics may help us secure funding in the future. If you use our software as part of your research, teaching, or other activities, we would be grateful if you could cite our work. The [CITATION.bib](CITATION.bib) file in the root of this repository lists the relevant papers. +Much of the software in this ecosystem was developed as a part of an academic research project. If you would like to help support it, please star the repository. If you use our software as part of your research, teaching, or other activities, we would like to ask you to cite our work. The [CITATION.bib](CITATION.bib) file in the root of this repository lists the relevant papers. diff --git a/notebooks/tutorial.jl b/notebooks/tutorial.jl index cf0fe48..f364b9b 100644 --- a/notebooks/tutorial.jl +++ b/notebooks/tutorial.jl @@ -45,7 +45,7 @@ using UnitDiskMapping, Graphs, GenericTensorNetworks, LinearAlgebra # ╔═╡ 98459516-4833-4e4a-916f-d5ea3e657ceb # Visualization setup. -# To make the plots dark mode friendly, we use white background color. +# To make the plots dark-mode friendly, we use white-background color. using UnitDiskMapping.LuxorGraphPlot.Luxor, LuxorGraphPlot; LuxorGraphPlot.DEFAULT_UNIT[] = 25; LuxorGraphPlot.DEFAULT_BACKGROUND_COLOR[]="white"; # ╔═╡ eac6ceda-f5d4-11ec-23db-b7b4d00eaddf @@ -73,7 +73,7 @@ md"We can use the `map_graph` function to map the unweighted MIS problem on the unweighted_res = map_graph(graph; vertex_order=Branching()); # ╔═╡ 3f605eac-f587-40b2-8fac-8223777d3fad -md"Here, the key word argument `vertex_order` can be a vector of vertices in a specified order, or the method to compute the path decomposition that generates an order. The `Branching()` method is an exact path decomposition solver, which is suited for small graphs (where number of vertices <= 50). The `Greedy()` method finds the vertex order much faster and works in all cases, but is not optimal. +md"Here, the keyword argument `vertex_order` can be a vector of vertices in a specified order, or the method to compute the path decomposition that generates an order. The `Branching()` method is an exact path decomposition solver, which is suited for small graphs (where number of vertices <= 50). The `Greedy()` method finds the vertex order much faster and works in all cases, but may not be optimal. A good vertex order can reduce the depth of the mapped graph." # ╔═╡ e5382b61-6387-49b5-bae8-0389fbc92153 @@ -90,7 +90,7 @@ md"The field `grid_graph` is the mapped grid graph." show_graph(unweighted_res.grid_graph) # ╔═╡ 96ca41c0-ac77-404c-ada3-0cdc4a426e44 -md"The field `lines` is a vector of copy gadgets arranged in `⊢` shape. These copy gadgets form a *crossing lattice*, in which two copy lines cross each other whenever the their corresponding vertices in the source graph are connected by an edge. +md"The field `lines` is a vector of copy gadgets arranged in a `⊢` shape. These copy gadgets form a *crossing lattice*, in which two copy lines cross each other whenever their corresponding vertices in the source graph are connected by an edge. ``` vslot ↓ @@ -106,7 +106,7 @@ md"The field `lines` is a vector of copy gadgets arranged in `⊢` shape. These unweighted_res.lines # ╔═╡ a64c2094-9a51-4c45-b9d1-41693c89a212 -md"The field `mapping_history` contains the rewrite rules applied to the crossing lattice. They are important information for mapping a solution back." +md"The field `mapping_history` contains the rewrite rules applied to the crossing lattice. They contain important information for mapping a solution back." # ╔═╡ 52b904ad-6fb5-4a7e-a3db-ae7aff32be51 unweighted_res.mapping_history @@ -124,20 +124,20 @@ md"We can solve the mapped graph with [`GenericTensorNetworks`](https://queracom res = solve(IndependentSet(SimpleGraph(unweighted_res.grid_graph)), SingleConfigMax())[] # ╔═╡ 86457b4e-b83e-4bf5-9d82-b5e14c055b4b -md"You might want to read [the documentation page of `GenericTensorNetworks`](https://queracomputing.github.io/GenericTensorNetworks.jl/dev/) for a detailed explaination about this function. Here, we just visually check the solution configuration." +md"You might want to read [the documentation page of `GenericTensorNetworks`](https://queracomputing.github.io/GenericTensorNetworks.jl/dev/) for a detailed explanation on this function. Here, we just visually check the solution configuration." # ╔═╡ 4abb86dd-67e2-46f4-ae6c-e97952b23fdc show_config(unweighted_res.grid_graph, res.c.data) # ╔═╡ 5ec5e23a-6904-41cc-b2dc-659da9556d20 -md"By mapping the result back, we get a solution for the original Petersen graph. Its maximum independent set size is 4" +md"By mapping the result back, we get a solution for the original Petersen graph. Its maximum independent set size is 4." # ╔═╡ 773ce349-ba72-426c-849d-cfb511773756 # The solution obtained by solving the mapped graph original_configs = map_config_back(unweighted_res, res.c.data) # ╔═╡ 7d921205-5133-40c0-bfa6-f76713dd4972 -# Confirm the solution from the mapped graph gives us +# Confirm that the solution from the mapped graph gives us # a maximum independent set for the original graph UnitDiskMapping.is_independent_set(graph, original_configs) @@ -145,13 +145,13 @@ UnitDiskMapping.is_independent_set(graph, original_configs) md"## Generic Weighted Mapping" # ╔═╡ 5e4500f5-beb6-4ef9-bd42-41dc13b60bce -md"A Weighted Maximum Independent Set (WMIS) problem on a general graph can be mapped to one on the defected King's graph. The first step is do the same mapping as above but adding a new positional argument `Weighted()` as the first argument of `map_graph`. Let us still use the Petersen graph as an example." +md"A Maximum Weight Independent Set (MWIS) problem on a general graph can be mapped to one on the defected King's graph. The first step is to do the same mapping as above but adding a new positional argument `Weighted()` as the first argument of `map_graph`. Let us still use the Petersen graph as an example." # ╔═╡ 2fa704ee-d5c1-4205-9a6a-34ba0195fecf weighted_res = map_graph(Weighted(), graph; vertex_order=Branching()); # ╔═╡ 27acc8be-2db8-4322-85b4-230fdddac043 -md"The return value is similar to that for the unweighted mapping generated previously, except each node in the mapped graph can have a weight 1, 2 or 3. Note here, we haven't added the weights in the original graph." +md"The return value is similar to that for the unweighted mapping generated above, except each node in the mapped graph can have a weight 1, 2 or 3. Note here, we haven't added the weights in the original graph." # ╔═╡ b8879b2c-c6c2-47e2-a989-63a00c645676 show_grayscale(weighted_res.grid_graph) @@ -174,7 +174,7 @@ source_weights = rand(10) mapped_weights = map_weights(weighted_res, source_weights) # ╔═╡ f77293c4-e5c3-4f14-95a2-ac9688fa3ba1 -md"Now that we have both graph and weights, let us solve the mapped problem!" +md"Now that we have both the graph and the weights, let us solve the mapped problem!" # ╔═╡ cf910d3e-3e3c-42ef-acf3-d0990d6227ac wmap_config = let @@ -208,7 +208,7 @@ md"### Generic QUBO mapping" # ╔═╡ b5d95984-cf8d-4bce-a73a-8eb2a7c6b830 md""" -A QUBO problem can be specified as the following energy model +A QUBO problem can be specified as the following energy model: ```math E(z) = -\sum_{i