diff --git a/.github/workflows/link-checker.yml b/.github/workflows/link-checker.yml index 18359b3d02..cf6340fc57 100644 --- a/.github/workflows/link-checker.yml +++ b/.github/workflows/link-checker.yml @@ -12,4 +12,4 @@ jobs: uses: ruzickap/action-my-broken-link-checker@v2 with: url: https://bloqade.quera.com - cmd_params: "--buffer-size=8192 --exclude='https://github.com/QuEraComputing/bloqade-python/edit/' --exclude='https://fonts.gstatic.com'" + cmd_params: "--buffer-size=8192 --exclude='https://github.com/QuEraComputing/bloqade-analog/edit/' --exclude='https://fonts.gstatic.com'" diff --git a/CITATION.cff b/CITATION.cff index a023694e68..89de1553fa 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -17,7 +17,7 @@ date-released: '2024-05-04' doi: 10.5281/zenodo.11114110 license: - cc-by-4.0 -repository-code: https://github.com/QuEraComputing/bloqade-python/tree/v0.15.11 -title: 'QuEraComputing/bloqade-python: v0.15.11' +repository-code: https://github.com/QuEraComputing/bloqade-analog/tree/v0.15.11 +title: 'QuEraComputing/bloqade-analog: v0.15.11' type: software version: v0.15.11 diff --git a/LICENSE b/LICENSE index 6e4da76908..62d5d3039a 100644 --- a/LICENSE +++ b/LICENSE @@ -1,4 +1,4 @@ -The bloqade-python package is licensed under the Apache License, Version 2.0: +The bloqade-analog package is licensed under the Apache License, Version 2.0: > Copyright (c) 2023 QuEra Computing Inc.. > diff --git a/README.md b/README.md index 5031d36a14..c70da3b8fd 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,24 @@
- - - + + + Bloqade Logo
-
- Bloqade Logo - -
- -[![Latest Version](https://img.shields.io/pypi/v/bloqade.svg)](https://pypi.python.org/pypi/bloqade) -[![Supported Python Versions](https://img.shields.io/pypi/pyversions/bloqade.svg)](https://pypi.python.org/pypi/bloqade) -[![codecov](https://codecov.io/github/QuEraComputing/bloqade-python/graph/badge.svg?token=4YJFc45Jyl)](https://codecov.io/github/QuEraComputing/bloqade-python) -![CI](https://github.com/QuEraComputing/bloqade-python/actions/workflows/ci.yml/badge.svg) -[![Documentation](https://img.shields.io/badge/Documentation-6437FF)](https://queracomputing.github.io/bloqade-python/latest/) +[![Latest Version](https://img.shields.io/pypi/v/bloqade.svg)](https://pypi.python.org/pypi/bloqade-analog) +[![Supported Python Versions](https://img.shields.io/pypi/pyversions/bloqade.svg)](https://pypi.python.org/pypi/bloqade-analog) +[![codecov](https://codecov.io/github/QuEraComputing/bloqade-analog/graph/badge.svg?token=4YJFc45Jyl)](https://codecov.io/github/QuEraComputing/bloqade-analog) +![CI](https://github.com/QuEraComputing/bloqade-analog/actions/workflows/ci.yml/badge.svg) +[![Documentation](https://img.shields.io/badge/Documentation-6437FF)](https://queracomputing.github.io/bloqade-analog/latest/) [![DOI](https://zenodo.org/badge/629628885.svg)](https://zenodo.org/doi/10.5281/zenodo.11114109) + +> [!IMPORTANT] +> +> Bloqade has been restructured to make room for new features and improvements. Please refer to the [migration guide](https://bloqade.quera.com/dev/home/migration/) for more information. + + # Welcome to Bloqade -- QuEra's Neutral Atom SDK ## What is Bloqade? @@ -47,12 +48,12 @@ In order to better understand more about Bloqade users we would kindly ask that You can install the package with `pip` in your Python environment of choice via: ```sh -pip install bloqade +pip install bloqade-analog ``` ## Documentation -If you're already convinced about what Bloqade brings to the table, feel free to take a look at our documentation with examples [here](https://queracomputing.github.io/bloqade-python/latest/). +If you're already convinced about what Bloqade brings to the table, feel free to take a look at our documentation with examples [here](https://queracomputing.github.io/bloqade-analog/latest/). If you aren't convinced, keep scrolling! @@ -80,8 +81,8 @@ Let Bloqade handle keeping track of all the variations while you focus on becomi Did we mention you can throw your program at hardware and emulation and still keep your parameter sweeps? ```python -from bloqade import var -from bloqade.atom_arrangement import Square +from bloqade.analog import var +from bloqade.analog.atom_arrangement import Square import numpy as np @@ -134,7 +135,7 @@ You can save any intermediate steps in your program construction, enabling you t Feel free to let your waveforms grow to your liking too!: ```python -from bloqade import start +from bloqade.analog import start # Save your intermediate steps any way you like initial_geometry = start.add_position((0, 0)) @@ -152,8 +153,8 @@ program_2 = target_rabi_wf.piecewise_linear( Want to focus on building one part of your program first before others (or, just want that same Bloqade.jl flavor?) We've got you covered: ```python -from bloqade import piecewise_linear, var -from bloqade.ir.location import Square +from bloqade.analog import piecewise_linear, var +from bloqade.analog.ir.location import Square import numpy as np # Create a geometry without worrying about pulses yet @@ -204,7 +205,7 @@ program_with_few_atoms.parallelize(24).braket.aquila(24).run_async(100) Bloqade wouldn't exist if we weren't fortunate enough to obtain feedback from awesome members of our community such as yourself (: -If you find a bug, have an idea, or find an issue with our documentation, please feel free to file an issue on the [Github repo itself](https://github.com/QuEraComputing/bloqade-python/issues/new/choose). +If you find a bug, have an idea, or find an issue with our documentation, please feel free to file an issue on the [Github repo itself](https://github.com/QuEraComputing/bloqade-analog/issues/new/choose). After using/experimenting/tinkering/hacking with Bloqade it would also be helpful to us for you to fill out [this form](https://share.hsforms.com/1FJjYan2VQC6NfrQ5IPAcewdrwvd) which allows us to get some more detailed feedback. diff --git a/docs/assets/analog-logo-dark.svg b/docs/assets/analog-logo-dark.svg new file mode 100644 index 0000000000..4ee3f81ae1 --- /dev/null +++ b/docs/assets/analog-logo-dark.svg @@ -0,0 +1,142 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/assets/analog-logo.svg b/docs/assets/analog-logo.svg new file mode 100644 index 0000000000..20b18d0f45 --- /dev/null +++ b/docs/assets/analog-logo.svg @@ -0,0 +1,135 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/docs/assets/logo-dark.png b/docs/assets/logo-dark.png deleted file mode 100644 index f51701bc43..0000000000 Binary files a/docs/assets/logo-dark.png and /dev/null differ diff --git a/docs/assets/logo.png b/docs/assets/logo.png deleted file mode 100644 index 1a4797cc0b..0000000000 Binary files a/docs/assets/logo.png and /dev/null differ diff --git a/docs/blog/2023/index.md b/docs/blog/2023/index.md deleted file mode 100644 index 85e285d534..0000000000 --- a/docs/blog/2023/index.md +++ /dev/null @@ -1,2 +0,0 @@ -# Bloqade Blog Posts 2023 -Sept. 21, 2023 - [Introducing Bloqade SDK for Python](posts/bloqade-release.md) diff --git a/docs/blog/2023/posts/bloqade-release.md b/docs/blog/2023/posts/bloqade-release.md deleted file mode 100644 index 864a55c12d..0000000000 --- a/docs/blog/2023/posts/bloqade-release.md +++ /dev/null @@ -1,122 +0,0 @@ - -# Introducing Bloqade SDK for Python - -![Bloqade Logo](../../../assets/logo.png#only-light) -![Bloqade Logo](../../../assets/logo-dark.png#only-dark) - -Greetings Neutral Atom QC experts, enthusiasts, and newcomers! - -We are ~~excited to the Rydberg state~~ thrilled to announce the Python version of our cutting-edge SDK, Bloqade. Originally developed in Julia, Bloqade has been a game-changer in the realm of Neutral Atom quantum computing. With the introduction of the Python version, we aim to make this revolutionary technology more accessible and user-friendly than ever before. - - -## Why Python? - -Python is one of the most widely used programming languages, especially in the quantum computing community and broader scientific communities. By extending Bloqade to Python, we are opening doors to a broader audience, enabling more developers, researchers, and organizations to harness the power of Neutral Atom quantum computing. - - -## Neutral Atom Quantum Computing - -Recently, the Neutral Atom platform has come on the QC scene in the form of Analog Hamiltonian Simulators that have a broad set of use cases beyond quantum circuits. Ranging from simulating unique quantum phases of matter, solving combinatorical optimization problems, and machine learning applications, the analog mode provides strong values in solving practical, interesting problems in the near term. - -These advances are crucial milestones on the way towards scalable digital gate-based architecture using atom shuttling. This new technology and its novel applications demand a paradigm shift in the way we not only think about quantum computing, but translate those ideas to real hardware. Enter Bloqade, a next-generation SDK designed to put the power of neutral atoms at your fingertips. - -## Why Bloqade? - -Bloqade is designed with the primary goal of making it easier to compose programs for QuEra’s hardware and analyze results. - -We've gained valuable insights into how users have used our neutral-atom hardware and with it, their struggles with existing tools. We took advantage of this knowledge to produce a tool that could take the "hard" out of "hardware". Bloqade is precision-balanced in both *flexibility* to empower novices to experiment with ease and *power* to let experts perform cutting-edge work without breaking a sweat. - -### Highlights - -#### Smart Documentation - -With our commitment to enabling more seamless program development, we've put the relevant documentation you need right *where* and *when* you need it. - -No more obnoxious switching between your favorite coding environment and documentation in a separate window. Let Bloqade guide you where you'd like to go: - -![](../../../assets/readme-gifs/smart-docs.gif) - -#### Fully Parameterized Analog Programs - -*Parameter sweeps* are a common theme of programs for analog quantum computers, where a user would like to observe differences in output results by varying a value or values in their program. - -You used to have to manually crank out variations of your program with different values and then keep track of all the individual submissions to the emulator and hardware, a mess to keep track of and process the results of afterwards. - -Bloqade eliminates this with its own support for variables that can later be assigned single values or a whole sequence of values for trivial parameter sweeping. This isn't some feature that's constrained to a certain backend, you can take your program with all its variables and submit it to your choice of emulator or our hardware directly. - -```python -from bloqade import var -from bloqade.atom_arrangement import Square - -import numpy as np - -adiabatic_durations = [0.4, 3.2, 0.4] - -# create variables explicitly... -max_detuning = var("max_detuning") -# ...or implicitly inside the program definition. -adiabatic_program = ( - Square(3, "lattice_spacing") - .rydberg.rabi.amplitude.uniform.piecewise_linear( - durations=adiabatic_durations, values=[0.0, "max_rabi", "max_rabi", 0.0] - ) - .detuning.uniform.piecewise_linear( - durations=adiabatic_durations, - values=[ - -max_detuning, # scalar variables support direct arithmetic operations - -max_detuning, - max_detuning, - max_detuning, - ], - ) - .assign(max_rabi=15.8, max_detuning=16.33) - .batch_assign(lattice_spacing=np.arange(4.0, 7.0, 0.5)) -) - -# Launch your program on your choice of Braket or in-house emulator... -emu_results = adiabatic_program.braket.local_emulator().run(10000) -faster_emu_results = adiabatic_program.bloqade.python().run(10000) -# ...as well as hardware without stress -hw_results = adiabatic_program.parallelize(24).braket.aquila().run_async(100) - -``` - -#### Integrated Visualization Tools - -Instantly understand what your programs are doing faster than you can say "neutral atoms rock!" with Bloqade's built-in visualization tools: - - -![](../../../assets/readme-gifs/locations-hover.gif) - -![](../../../assets/readme-gifs/graph-select.gif) - - -For your results, no more obnoxious manual compilation of results across different parameters or wrangling them into more useful forms. Get insights of experiment outcomes in the blink of an eye: - -![](../../../assets/readme-gifs/visualize-bitstrings.gif) - -![](../../../assets/readme-gifs/hover-bitstrings.gif) - -Now that's what we call having your cake AND eating it. - - -## Bloqade Roadmap - -### Bloqade Alpha Phase - -During the next year, we plan on continuing development of Bloqade's python interface. If you are as excited about Neutral Atom quantum computing as us, or heck, even just quantum physics in general, give Bloqade a try! This is your opportunity to influence the direction of Bloqade and get in on the ground floor of the next Quantum Computing revolution. - -### But what about Julia? - -***Don't you guys already HAVE an SDK in Julia? Why do you need two SDKs?*** - - -That's right! However, there's a key motivating factor for the reason we created Bloqade Python that's distinct for Bloqade.jl's existence. - -Bloqade.jl is primarily geared as a *high-performance emulator*. It allows you to design complex neutral-atom algorithms that may not necessarily run on our hardware BUT are excellent if you're exploring novel physical phenonema/algorithms or as a tool for pedagogical purposes. - -Bloqade.jl does have the ability to submit to [*Aquila*](https://www.quera.com/aquila), our flagship quantum computer, but for more complex tasks such as sweeping parameters (e.g. running the same program on hardware with slightly different parameters each time) or advanced post-processing, it becomes cumbersome quite quickly. - -There are no plans to drop support any time soon though. On the contrary, we plan on fully integrating Bloqade.jl into the Python package, which will enable you to program Neutral Atom quantum hardware without having to choose. - -We very much look forward to you trying out Bloqade! diff --git a/docs/contributing/asking-a-question.md b/docs/contributing/asking-a-question.md index 79ed6a27ed..35e28b0047 100644 --- a/docs/contributing/asking-a-question.md +++ b/docs/contributing/asking-a-question.md @@ -1,3 +1,3 @@ # Ask a Question -If you're interested in contributing to Bloqade, or just want to discuss the project, join the discussion on GitHub Discussions at https://github.com/QuEraComputing/bloqade-python/discussions +If you're interested in contributing to Bloqade, or just want to discuss the project, join the discussion on GitHub Discussions at https://github.com/QuEraComputing/bloqade-analog/discussions diff --git a/docs/contributing/documentation-issues.md b/docs/contributing/documentation-issues.md index 2d3dfffcac..9d59e6ab01 100644 --- a/docs/contributing/documentation-issues.md +++ b/docs/contributing/documentation-issues.md @@ -2,7 +2,7 @@ We are always looking to improve our documentation. If you find a typo or think something is unclear, please open an issue on -our GitHub page: https://github.com/QuEraComputing/bloqade-python/issues +our GitHub page: https://github.com/QuEraComputing/bloqade-analog/issues For typos or other minor problems, create an issue that contains a link to the specific page that includes the problem, along with diff --git a/docs/contributing/feature-requests.md b/docs/contributing/feature-requests.md index a70d89486d..20758764a9 100644 --- a/docs/contributing/feature-requests.md +++ b/docs/contributing/feature-requests.md @@ -4,7 +4,7 @@ Given that we are currently at the beginning of the development of the Bloqade python interface, we are open to suggestions about what features would be helpful to include in future package iterations. If you have a request for a new feature, please open an issue on our -GitHub page: https://github.com/QuEraComputing/bloqade-python/issues +GitHub page: https://github.com/QuEraComputing/bloqade-analog/issues We ask that the feature requests be as specific as possible. Please include the following information in your feature request: diff --git a/docs/contributing/reporting-a-bug.md b/docs/contributing/reporting-a-bug.md index a2363902e5..5d2a97380e 100644 --- a/docs/contributing/reporting-a-bug.md +++ b/docs/contributing/reporting-a-bug.md @@ -5,7 +5,7 @@ current implementation. We are continuously striving to improve the stability of Bloqade. As such, we encourage our users to report all bugs they find. To do this, we ask you to submit an issue to our GitHub page: -https://github.com/QuEraComputing/bloqade-python/issues +https://github.com/QuEraComputing/bloqade-analog/issues Please include the following information in your bug report: diff --git a/docs/drafts/aws_hybrid_execution.md b/docs/drafts/aws_hybrid_execution.md deleted file mode 100644 index 4e3f1bad21..0000000000 --- a/docs/drafts/aws_hybrid_execution.md +++ /dev/null @@ -1,711 +0,0 @@ -# Hybrid Execution with AWS Hybrid Jobs - -## Introduction - -Analog Hamiltonian Simulation (AHS) has proven itself to be a powerful method of performing quantum computation that is well-suited for solving optimization problems and performing computationally difficult simulations of other quantum systems. Unlike its counterpart digital/gate-based quantum computing where you think of your programs in terms of unitary operations that are akin to classicla gates, you think of programs in AHS in terms of the geometry of your qubits (individual atoms!) and the waveforms of the lasers that are applied to them. - -The team at QuEra Computing believes this is a useful step in the path to fault tolerant quantum computation but also realizes that such novel power and capabilities require a novel tool. - -That's why we're proud to announce the release of the Bloqade SDK for Python! We've had the opportunity to obtain valuable feedback from the community and leveraged our unique position as the only provider of publicly cloud-accessible Nuetral Atom hardware to produce a tool that puts the power of AHS hardware at your fingertips. - -## Installation - -Bloqade is a pure Python library so installation is as easy as `pip install bloqade`! Once installed you have a variety of options for building your Nuetral atom Analog program. We have worked very hard to provide a seamless user experience when venturing into unfamiliar territory of AHS with Nuetral Atoms! Let’s dig into some of the major features Bloqade has to offer! - -## Features of Bloqade - -### Just-in-time Documentation - -I know you have probably spent a pretty penny on your multi-monitor setup so that you do not have to switch between windows when writing code and looking at documentation. What if I told you there was a better way? What if your code contained all the documentation you needed to continue writing your program? That’s exactly what we have designed in the user experinence (UX) of Bloqade. - -Typically when building an AHS program one needs to construct many different objects and combine them in very particular ways which are not obvious without understanding the concept as a whole. Bloqade provides a unique experience programming AHS. Our interface eases the user into AHS programming by using Python’s rich type-hinting system. Most IDE's, as well as IPython and Jupyter, use the type hints to access to the methods and attributes that are availible to whatever object you currently have. We use this hinting to our advantage in Bloqade by building your AHS program with a chain of methods and attributes separated by `.`. In doing so you will be given hints as to what to do next at every step of the way when building your program. - -![](https://hackmd.io/_uploads/rJnd8rNMT.gif) - - -While it is conventional thinking that it is bad to chain `.` statements together, there are some well known libraries like [Pandas](https://medium.com/@ulriktpedersen/modern-pandas-streamlining-your-workflow-with-method-chaining-f65e75deb193) that can and do make heavy use of this pattern of programming. The dot-chaining syntax also integrate python linters like [black](https://github.com/psf/black) to make your code highly readable without having to do anything other can call the linter on your python file. - -Example, before linter some unstrctured code -![](https://hackmd.io/_uploads/Hk_t1BVfT.png) -After running black we get consistent formatting. -![](https://hackmd.io/_uploads/rkbckHNMp.png) -In this case properties are always chained which makes reading the code a lot like reading a sentence. - -On top of these nice features, If you’re in an IDE like VS code or PyCharm you can access the documentation of each method and attribute in which we provide even more hints for the next step after your current selection - -![](https://github.com/QuEraComputing/bloqade-python/blob/main/docs/assets/readme-gifs/smart-docs.gif?raw=True) - - -[Here](https://towardsdatascience.com/the-unreasonable-effectiveness-of-method-chaining-in-pandas-15c2109e3c69) is a blog post that goes into the advantages and disadvantages of chaining method calls/attributes like we have shown. It worth a read if you are still a bit skeptical! It's worth noting that you do not neccesarily have to chain method/attribute calls you can safely store a intermediate parts of program with intermediate objects because calling a method *does not* act in the object in-place. This means you can - -## Parameterized Programs -> keep title capitalization consistent [name=jzlong] - -Many near-term applications for QC as well as AHS require some notion of parameterized programs. We’ve made this a first-class feature in Bloqade enabling you to write a single AHS program precisely to define your experiment or algorithm symbolically enabling more readable and sharable code! - -You can also create stand-alone variables which have a basic symbolic representation that support some arithmetic operations that are useful for more advanced AHS applications. For example, say I have a piecewise linear pulse that has variable segments but I have another constant waveform running for the total time of the other waveform. I can sum the durations of each segment of the piecewise linear waveform to get the total duration and use that to construct the constant waveform. - -```python -from bloqade import var, start - -rabi_durations = [0.1, var("run_time"), 0.1] -total_time = sum(rabi_durations) - -program = ( - start.add_position((0, 0)) - .rydberg.detuning.uniform.constant(total_time, "detuning") - .amplitude.uniform.piecewise_linear( - rabi_durations, [0, "amplitude", "amplitude", 0] - ) -) -``` - -Once you have defined your parameterized program there are three different methods of specifying the run time values of the parameters: - -```python -program.assign(var1=value1, var2=value2) -``` - -which assigns the variables in a static way. The basic logic here is for segments of program in which you want to share without limiting other users to use the concrete values you decided works for you. - -```python -program.batch_assign(var1=[value1_1, value1_2, …],…) -``` -or - -```python -program.batch_assign([dict(var1=value1_1,…), dict(var1=value1_2,…),…]) -``` - -specify a batch of tasks via parameters assigned to lists or a list of dictionaries with the assigned parameters. Next, - -```python -args([“var1”, “var2”]) -``` - -will delay the assignment of the variable until the program is being executed. We will discuss this below in more detail. - -Note that none of these methods are required for non-parameterized programs. For parameterized programs you can mix and match all of these assignments together. However, they will always come in the order of `assign`, `batch_assign` then `args`. let's take the program above to give a concrete example. - -After specifying the program we can do the following assignment: - -```python -assigned_program = ( - program.assign(amplitude=15.7) - .batch_assign(run_time=np.linspace(0.05, 3.0, 101)) - .args(["detuning"]) -) -``` - -First we assign the amplitude to a value of 15.7 which is going to be non-changing regardless of the other changing parameters in execution. Because we assign ‘run_time’ using `batch_assign` that means every time we execute this program we will run 101 tasks with the parameter sweep defined by the list of values. Finally `args([“detuning”])` implies that you must provide the value of the detuning as an argument when calling the execution method of the device. This is primarily useful for hybrid work flows or if you are simply just wanting to split your experiments into chunks defined by a different set of parameters. In conclusion, whether it is a hybrid algorithm or a parameter scan for your next fancy experiment Bloqade has you covered! - -### Visualization Tools - -While having a clean, readable syntax is great, there is always going to be the need for more visual representations. This is especially true for AHS programming where you think about things in terms of waveforms and atom configurations. As such, we provide visualization of your programs using `Bokeh`. Bokeh allows for very clean responsive interactive plots which we have implemented not only for AHS programs but AHS results as well! - - -![](https://github.com/QuEraComputing/bloqade-python/blob/main/docs/assets/readme-gifs/locations-hover.gif?raw=true) - -![](https://github.com/QuEraComputing/bloqade-python/blob/main/docs/assets/readme-gifs/graph-select.gif?raw=true) - -We also provide some visualization of your AHS program in the terminal: - -```ipython -In [1]: from bloqade import start - ...: - ...: program = ( - ...: start.add_position((0, 0)) - ...: .add_position((0, "r")) - ...: .rydberg.detuning.uniform.piecewise_linear([0.1, 1.2, 0.1], [-20, -20, 20, 20]) - ...: .amplitude.uniform.piecewise_linear([0.1, 1.2, 0.1], [0, 15, 15, 0]) - ...: .args(["r"]) - ...: ) - -In [2]: program.parse_circuit() -Out[2]: -AnalogCircuit -├─ register -│ ⇒ AtomArrangement -│ ├─ Location: filled -│ │ ├─ x -│ │ │ ⇒ Literal: 0 -│ │ └─ y -│ │ ⇒ Literal: 0 -│ └─ Location: filled -│ ├─ x -│ │ ⇒ Literal: 0 -│ └─ y -│ ⇒ Variable: r -└─ sequence - ⇒ Sequence - └─ RydbergLevelCoupling - ⇒ Pulse - ├─ Detuning - │ ⇒ Field - │ └─ Drive - │ ├─ modulation - │ │ ⇒ UniformModulation - │ └─ waveform - │ ⇒ Append - │ ├─ Linear - │ │ ├─ start - │ │ │ ⇒ Literal: -20 - │ │ ├─ stop - │ │ │ ⇒ Literal: -20 - │ │ └─ duration - │ │ ⇒ Literal: 0.1 - │ ├─ Linear - │ │ ├─ start - │ │ │ ⇒ Literal: -20 - │ │ ├─ stop - │ │ │ ⇒ Literal: 20 - │ │ └─ duration - │ │ ⇒ Literal: 1.2 - │ └─ Linear - │ ├─ start - │ │ ⇒ Literal: 20 - │ ├─ stop - │ │ ⇒ Literal: 20 - │ └─ duration - │ ⇒ Literal: 0.1 - └─ RabiFrequencyAmplitude - ⇒ Field - └─ Drive - ├─ modulation - │ ⇒ UniformModulation - └─ waveform - ⇒ Append - ├─ Linear - │ ├─ start - │ │ ⇒ Literal: 0 - │ ├─ stop - │ │ ⇒ Literal: 15 - │ └─ duration - │ ⇒ Literal: 0.1 - ├─ Linear - │ ├─ start - │ │ ⇒ Literal: 15 - │ ├─ stop - │ │ ⇒ Literal: 15 - │ └─ duration - │ ⇒ Literal: 1.2 - └─ Linear - ├─ start - │ ⇒ Literal: 15 - ├─ stop - │ ⇒ Literal: 0 - └─ duration - ⇒ Literal: 0.1 -``` - - -### Bloqade Emulator - -Anyone familiar with QuEra’s Julia SDK [**Bloqade.jl**](https://github.com/QuEraComputing/Bloqade.jl) knows that we’re pretty obsessed with performance. We would also be remiss to advertise Bloqade’s pure python emulator. While not as fast as **Bloqade.jl**, we have have worked to optimize our the state-vector simulator to get the most out of python as possible. The emulator supports both two and three level atom configurations, along with global and local driving and support for the blockade subspace (for those who are more familiar with Rydberg atoms). The blockade subspace and matrix calculations are nearly optimal for both memory and time and are written in pure NumPy and SciPy. We also have basic Numba JIT compiled sparse operations that further optimize the memory when solving the time-dependent Schrödinger equation. We hope our python emulator will allow you to explore a wide variety of applications for neutral atoms and prototype some neat new algorithms with AHS. - -> Capitalize Bloqade across the entire article [name=jzlong] - -### Target Multiple Backends - -All Bloqade programs can be targeted to multiple emulation and hardware backends very easily, again using the chaining of `.`’s. Also note that the chaining syntax allows Bloqade to let you know exactly when a program should be able to be run. To select `braket` as your service simply select the `braket` attribute of your program. At this stage there will be two methods availible for you, `aquila` and `local_emulator`. - -> I personally prefer to distinguish between methods and attributes by putting the `()` after a method name and omitting them for attributes [name=jzlong] - -Each backend has different restrictions in terms of the types of AHS programs that can be run. During the alpha phase of Bloqade we will continue to improve the error messages that are given when targeting specific backends making it easy for you to diagnose any issues with your program execution. - -Depending on the backend there are two or three methods for executing your program. For Cloud devices Bloqade has an API for both asynchronous (`run_async`) and synchronous (`run`) method for executing the job. Local emulator backends only support the `run` API. - -Now let us revisit the meaning of `args` assignment. Every execution method has a `args` argument, this is where you can specify the values of the parameters defined in `args` when defining your program. The order of the arguments in the `args` tuple is the order of the variables specified in the `args` method. - -> a vs an? [name=jzlong] - -Finally the backend object that gets created is also callable where the such that `object(*args, shots=shots,...)` is equivalent to `object.run(shots, args=args, ...)`. While this is primarily a stylistic choice but this is an available interface for you if need be. - -> "where the such that" -> "such that" -> "While this is primarily a stylistic choice but this is an available" -> "While this is primarily a stylicstic choice, this is also an available interface for you if need be" [name=jzlong] - -### Job management Features - -If you use the `batch_assign` API combined with your parameterized program it is possible to submit entire batches of AWS tasks. It's not enough to make programming AHS easy though; you also need to manage all the data that gets generated. Fear not, we have you covered. We know it can be quite cumbersome to have to manage hundreds or thousands of tasks so we have provided some useful functionality inside Bloqade to make your life easier as well as making experiments more reproducible. - -> Fear not, we have you covered! (worth adding an exclamation mark just to go the full nine yards with the chippy/enthusiastic tone). [name=jzlong] - -One of the most common issues when running AHS (or just QC in general) is saving experimental results. When using a cloud device one also has the added difficultly of associating a task id with the parameter in the parameter scan as well as checking and fetching the task results as the tasks are completed. Bloqade provides a uniform format of saving this data which is useful for users to do sharable and reproducable work. To save your results simply invoke the `save` and `load` functions. We also provide `dumps` and `loads` if you want to work directly with strings instead of JSON files! It's as simple as: - -> capitalize ID [name=jzlong] - -```python -from bloqade import save, load - -# define program -... - -batch_task = my_program.braket.aquila().run_async(100) - -save(batch_task, “my_aquila_results.json”) - -# in some other file: - -loaded_batch_task = load(“my_aquila_results.json”) - -# continue with analysis -... - -``` - -These objects will contain all the necessary information to fetch results and obtain the values of parameters used in your program. - -Saving files isn’t all that Bloqade offers. When dealing with a Cloud device like Aquila it is important to be able to manage those asynchronous tasks. Bloqade offers different ways to do this: - -> "...isn't all that Bloqade offers." -> "..isn't all that Bloqade offers to make your life easier." - -- `batch_task.fetch()` queries the current task statuses and fetches the results of completed tasks without blocking the python interpreter. The results are stored inside the current object. - -- `batch_task.pull()` Like fetch but waits until all tasks have been completed before unblocking the interpreter. The tasks the results are stored inside the current object. - -- `batch_task.get_tasks(*status_codes)` returns a new Batch object that contains the tasks with the status given by the inputs to the function - -- `batch_task.remove_tasks(*status_codes)` return a new Batch object that contains tasks that did not match the status code that have been given. - -See the [documentation](https://bloqade.quera.com/latest/reference/bloqade/task/batch/#bloqade.task.batch.RemoteBatch) for more details on what the various status codes are and what they mean. - - -## Adaptive workloads - -As mentioned above, the ability to parameterize and assign values to your analog program means that there is a lot one can do in terms of running various kinds of experiments. Here we will discuss how to combine the parameterized pulses with braket’s hybrid jobs! - - -In this case we can pass our parameterized pulse program into classical optimizer in order to provide classical feedback for the next quantum program to run. The use case we will cover here is a pretty standard problem that maps very neatly onto the AHS architecture implemented with Neutral atoms. Because of the Rydberg blockade effect the ground state of a collection of atoms maps exactly to what is called the Maximum Independent Set (MIS) problem on geometric graphs. The MIS problem is a graph coloring problem where the goal is to find the largest set of nodes in a graph such that no two nodes are connected by an edge. This problem is NP-hard and has many applications in scheduling, resource allocation, and even quantum error correction. - - -> I also think it better to use the term "Combinatorial Optimization" over "Graph Coloring" considering CO might open a broader window in people's minds for what the machine is capable of than just "Graph Coloring". -> It would also be nice to have links to examples for each application (MIS for scheduling, resource allocation, QEC.) - -We refer the reader to [this](https://github.com/QuEraComputing/QuEra-braket-examples/blob/main/OptimizationTutorial/AWS_optimization_demo.ipynb) Notebook example for a more detailed explanation of the problem and how it maps onto AHS. For this blog post we will focus on the implementation of the hybrid algorithm using Bloqade and Braket Hybrid Jobs. - -Like most of the Bloqade programs we begin by importing the necessary components to build the program: - -```python -import numpy as np -from bloqade import RB_C6, save, start, var -from bloqade.atom_arrangement import Square -from braket.devices import Devices -from braket.aws import AwsDevice -``` - -using the `AwsDevice` we can get some information about the capabilities of the device. Note that Bloqade uses `rad/us` and `us` for energy and time units respectively while braket uses SI unites, e.g. `rad/s` and `s`, hence the conversion of units below. - -```python -# define the parameters of the experiment -device = AwsDevice(Devices.QuEra.Aquila) -rydberg_properties = device.properties.paradigm.rydberg.rydbergGlobal - -# Convert energy units to rad/us and time to us -detuning_max = float(rydberg_properties.detuningRange[1]) / 1e6 -rabi_max = float(rydberg_properties.rabiFrequencyRange[1]) / 1e6 -total_time = float(rydberg_properties.timeMax) * 1e6 -``` - -For the particular problem we are studying we need to map the problem graph onto the atom arrangement. For more information we refer the reader to the notebook [example](https://github.com/QuEraComputing/QuEra-braket-examples/blob/main/OptimizationTutorial/AWS_optimization_demo.ipynb). - -```python -# make sure next nearest neighbors are blockaded -Rmin = np.sqrt(2) # minimum unit disk radius -Rmax = 2 # maximum unit disk radius -Rudg = np.sqrt(Rmin * Rmax) # geometric mean of Rmin and Rmax - -detuning_end = detuning_max / 4 # detuning at the end of the pulse -blockade_radius = (RB_C6 / detuning_end) ** (1 / 6) -lattice_spacing = blockade_radius / Rudg -``` - -Now we can define the program. We will use the `var` function to define the parameters for the program. Also the cost function will involve calculating the final energy of tha atoms which can be obtained from the geometry of the program via the `rydberg_interaction` method. - -```python -# define the time step for the detuning waveform -dt = 0.4 -n_steps = int(total_time / dt) -# create variables for the detuning time steps -detuning_vars = [var(f"d{i}") for i in range(n_steps - 1)] - -# define the lattice size before defect insertion -L = 4 -# set seed for defect generation -rng = np.random.default_rng(1337) -# define the geometry of the program -geometry = ( - Square(L, lattice_spacing) - .apply_defect_count(L**2 // 2, rng=rng) - .remove_vacant_sites() -) -# define the program -program = ( - geometry.rydberg.detuning.uniform.piecewise_linear( - n_steps * [dt], [-detuning_end] + detuning_vars + [detuning_end] - ) - .amplitude.uniform.piecewise_linear( - [0.1, total_time - 0.2, 0.1], [0, rabi_max, rabi_max, 0] - ) -) -# get the interaction matrix -V_ij = geometry.interaction_matrix() -``` - -We need to build the infrastructure to do the hybrid job. There are many different tools availible in `braket.jobs` that allow you to log the progress of your hybrid algorithm. Here we set up a simple class that wraps the cost function and log the progress of the algorithm. - -```python -from braket.jobs import ( - InstanceConfig, - hybrid_job, - save_job_checkpoint, -) -from braket.jobs.metrics import log_metric -from braket.jobs_data import PersistedJobDataFormat -from braket.tracking import Tracker - -# define a wrapper for the cost function for reporting -class CostFuncWrapper: - def __init__(self, backend, shots=10, **options): - self.backend = backend - self.options = options - self.iterations = 0 - self.shots = shots - self.prev_calls = {} - self.task_tracker = Tracker().start() - - @staticmethod - def cost_func(report): - bitstrings = 1 - np.asarray(report.bitstrings(False)) - detuning_energies = -detuning_end * bitstrings.sum(axis=-1) - interaction_energies = np.einsum( - "ij, ...i, ...j-> ...", V_ij, bitstrings, bitstrings - ) - - total_energy = detuning_energies + interaction_energies - # minimize the energy mean and standard deviation - return total_energy.mean() + total_energy.std() - - def __call__(self, x): - args = tuple(x) - - batch_task = self.backend.run(self.shots, args=args, **self.options) - report = batch_task.report() - - save(batch_task, f"my-aquila_results-{self.iterations}.json") - - self.prev_calls[args] = report - return self.cost_func(report) - - def callback(self, state): - args = tuple(state.x) - self.iterations += 1 - bitstrings = 1 - np.asarray(self.prev_calls[args].bitstrings(False)) - detuning_energies = -detuning_end * bitstrings.sum(axis=-1) - - interaction_energies = np.einsum( - "ij, ...i, ...j-> ...", V_ij, bitstrings, bitstrings - ) - - total_energy = detuning_energies + interaction_energies - mean_energy = total_energy.mean() - std_energy = total_energy.std() - - # Log metrics to display in Braket Console - log_metric( - iteration_number=self.iterations, value=state.fun, metric_name="loss" - ) - log_metric( - iteration_number=self.iterations, - value=mean_energy, - metric_name="mean energy", - ) - log_metric( - iteration_number=self.iterations, - value=std_energy, - metric_name="std energy", - ) - - # Also track the quantum task cost for Braket devices - braket_task_cost = float( - self.task_tracker.qpu_tasks_cost() - + self.task_tracker.simulator_tasks_cost() - ) - log_metric( - metric_name="braket_cost", - value=braket_task_cost, - iteration_number=self.iterations, - ) - - # Save a checkpoint to resume the hybrid job from where you left off - checkpoint_data = {"i": self.iterations, "args": args} - save_job_checkpoint( - checkpoint_data, data_format=PersistedJobDataFormat.PICKLED_V4 - ) - # end the job if the std energy is less than 5% of the mean energy - # this indicates that the system is close to the ground state - return abs(std_energy / mean_energy) < 0.05 -``` - -While this is a lot to take in let us go through some important things. Firstly, you can generate a `Report` object from a Bloqade batch of tasks. This object provides some basic analysis methods common to Neutral atom AHS computing. The one we make the most use of here is the `bitstrings` method which return a list of arrays that contains the shot results after executing the program. It takes a boolean argument that specifies whether or not to return the bitstrings of shots where there were atoms that did not get put into a trap before the computation was executed. By default this filter is applied, but for this problem we want to include those shots in our analysis hence the `False` argument. - -> No need to capitalize "Batch" unless you want to refer to the object type -> You can just say AHS, AHS Computing seems redundant -> Do we assume users know what a trap is? Might be worth having a sentence or two, just say something like "It takes a boolean argument that specifies whether or not to return the bitstrings of shots where there were atoms that did not successfully get put into position..." - -Another thing to note is that our cost function not only contains the `mean` energy but also the standard deviation. The reason for this is because we are targeting an eigenstate of the final Hamiltonian which has no energy variance. This is a good way to check if the system is in the ground state. We use the ratio of the standard deviation to the mean energy as a stopping condition for the algorithm. - -Finally, we have a callback function that is called after each iteration of the optimizer. This is where we can log the progress of the algorithm. We use the `Tracker` object to track the cost of the quantum tasks that are being executed. We also use the `log_metric` function to log the mean and standard deviation of the energy as well as the cost function of the quantum tasks. - -Now we can define the function that will run the hybrid job. - - -```python -def run_algo(assigned_program, device_arn=None, n_calls=10, shots=10): - @hybrid_job( - device=device_arn, # Which device to get priority access to - dependencies="requirements.txt", # install bloqade - instance_config=InstanceConfig("ml.m5.large"), - ) - def _runner(backend, shots, n_calls, **options): - from skopt import gp_minimize - - # Braket task cost - wrapped_cost_func = CostFuncWrapper(backend, shots=shots, **options) - - n_params = len(backend.params.args_list) - bounds = n_params * [(-detuning_max, detuning_max)] - - result = gp_minimize( - wrapped_cost_func, - bounds, - callback=wrapped_cost_func.callback, - n_calls=n_calls, - ) - - detuning_values = {var.name: val for var, val in zip(detuning_vars, result.x)} - - return detuning_values - - if device_arn == Devices.QuEra.Aquila: # use Aquila - backend = assigned_program.braket.aquila() - options = dict() - else: # use bloqade emulator - backend = assigned_program.bloqade.python() - options = dict(atol=1e-8, rtol=1e-4, solver_name="dopri5") - - # Run the hybrid job - return _runner(backend, n_calls, shots, **options) -``` - -We use the `hybrid_job` decorator to define the hybrid job. This decorator takes a `device_arn` argument which is the Amazon Resource Name (ARN) of the device you want to run the hybrid job on. There are some other options associated with the EC2 instance that is used to run the hybrid job. We will use the `InstanceConfig` object to specify the instance type and number of instances to use. `dependencies` points to a text file that contains the python dependencies needed to run this hybrid job. In this case we need `bloqade` and `scikit-optimize` in this text file. - -The function `_runner` that is being decorated must take the `backend` as the first argument. This object is generated by the Bloqade API and must match the device arn that is specified in the decorator. `n_calls` is the total number of iterations of the optimizer and `shots` is the number of shots to use for each iteration. `options` is a dictionary of options that are passed to the `run` method of the backend. The return value of the function is the final value of the parameters that were optimized. We wrap the `_runner` inside `run_algo` that takes the `program` and the `device_arn` as arguments to make sure that the device requested by `hybrid_jobs` matches the device called by `bloqade` as well as setting up specialized `options` for the different `bloqade` backends. - -To run the hybrid job we simply call the `run_algo` function with the assigned program and the device arn. - -```python -optimal_parameters = run_algo(program.args(detuning_vars), device_arn=Devices.QuEra.Aquila) -``` - -Finally we can plot the results of the hybrid job. - -```python -assigned_program = program.assign(**optimal_params) -assigned_program.show() -``` - -To run the algorithm with Bloqade emulator we simply change the device arn to None. - -Full source code: - -```python= -import numpy as np -from bloqade import RB_C6, save, start, var -from bloqade.atom_arrangement import Square -from braket.devices import Devices -from braket.aws import AwsDevice - - -# define the parameters of the experiment -device = AwsDevice(Devices.QuEra.Aquila) -rydberg_properties = device.properties.paradigm.rydberg.rydbergGlobal - -# Convert energy units to rad/us and time to us -detuning_max = float(rydberg_properties.detuningRange[1]) / 1e6 -rabi_max = float(rydberg_properties.rabiFrequencyRange[1]) / 1e6 -total_time = float(rydberg_properties.timeMax) * 1e6 - - -# make sure next nearest neighbors are blockaded -# unit disk minimum and maximum radii -Rmin = np.sqrt(2) # minimum unit disk radius -Rmax = 2 # maximum unit disk radius -# choose geometric mean of Rmin and Rmax -Rudg = np.sqrt(Rmin * Rmax) - -detuning_end = detuning_max / 4 -blockade_radius = (RB_C6 / detuning_end) ** (1 / 6) -lattice_spacing = blockade_radius / Rudg - -# define the time step for the detuning waveform -dt = 0.4 -n_steps = int(total_time / dt) -# create variables for the detuning time steps -detuning_vars = [var(f"d{i}") for i in range(n_steps - 1)] - -# define the lattice size before defect insertion -L = 4 -# set seed for geometry generation -rng = np.random.default_rng(1337) -program = ( - Square(L, lattice_spacing) - .apply_defect_count(L**2 // 2, rng=rng) - .remove_vacant_sites() - .rydberg.detuning.uniform.piecewise_linear( - n_steps * [dt], [-detuning_end] + detuning_vars + [detuning_end] - ) - .amplitude.uniform.piecewise_linear( - [0.1, total_time - 0.2, 0.1], [0, rabi_max, rabi_max, 0] - ) -) -# get atom register and interaction matrix -V_ij = program.parse_register().rydberg_interaction() - - -from braket.jobs import ( - InstanceConfig, - hybrid_job, - save_job_checkpoint, -) -from braket.jobs.metrics import log_metric -from braket.jobs_data import PersistedJobDataFormat -from braket.tracking import Tracker - - -# define a wrapper for the cost function for reporting -class CostFuncWrapper: - def __init__(self, cost_func, backend, shots=10, **options): - self.backend = backend - self.options = options - self.iterations = 0 - self.shots = shots - self.prev_calls = {} - self.task_tracker = Tracker().start() - - @staticmethod - def cost_func(report): - bitstrings = 1 - np.asarray(report.bitstrings(False)) - detuning_energies = -detuning_end * bitstrings.sum(axis=-1) - interaction_energies = np.einsum( - "ij, ...i, ...j-> ...", V_ij, bitstrings, bitstrings - ) - - total_energy = detuning_energies + interaction_energies - # minimize the energy mean and standard deviation - return total_energy.mean() + total_energy.std() - - def __call__(self, x): - args = tuple(x) - - batch_task = self.backend.run(self.shots, args=args, **self.options) - report = batch_task.report() - - save(batch_task, f"my-aquila_results-{self.iterations}.json") - - self.prev_calls[args] = report - return self.cost_func(report) - - def callback(self, state): - args = tuple(state.x) - self.iterations += 1 - bitstrings = 1 - np.asarray(self.prev_calls[args].bitstrings(False)) - detuning_energies = -detuning_end * bitstrings.sum(axis=-1) - - interaction_energies = np.einsum( - "ij, ...i, ...j-> ...", V_ij, bitstrings, bitstrings - ) - - total_energy = detuning_energies + interaction_energies - mean_energy = total_energy.mean() - std_energy = total_energy.std() - - # Log metrics to display in Braket Console - log_metric( - iteration_number=self.iterations, value=state.fun, metric_name="loss" - ) - log_metric( - iteration_number=self.iterations, - value=mean_energy, - metric_name="mean energy", - ) - log_metric( - iteration_number=self.iterations, - value=std_energy, - metric_name="std energy", - ) - - # Also track the quantum task cost for Braket devices - braket_task_cost = float( - self.task_tracker.qpu_tasks_cost() - + self.task_tracker.simulator_tasks_cost() - ) - log_metric( - metric_name="braket_cost", - value=braket_task_cost, - iteration_number=self.iterations, - ) - - # Save a checkpoint to resume the hybrid job from where you left off - checkpoint_data = {"i": self.iterations, "args": args} - save_job_checkpoint( - checkpoint_data, data_format=PersistedJobDataFormat.PICKLED_V4 - ) - # end the job if the std energy is less than 5% of the mean energy - # this indicates that the system is close to the ground state - return abs(std_energy / mean_energy) < 0.05 - - -def run_algo(assigned_program, device_arn=None, n_calls=10, shots=10): - @hybrid_job( - device=device_arn, # Which device to get priority access to - dependencies="requirements.txt", # install bloqade - instance_config=InstanceConfig("ml.m5.large"), - ) - def _runner(backend, shots, n_calls, **options): - from skopt import gp_minimize - - # Braket task cost - wrapped_cost_func = CostFuncWrapper(backend, shots=shots, **options) - - n_params = len(backend.params.args_list) - bounds = n_params * [(-detuning_max, detuning_max)] - - result = gp_minimize( - wrapped_cost_func, - bounds, - callback=wrapped_cost_func.callback, - n_calls=n_calls, - ) - - detuning_values = {var.name: val for var, val in zip(detuning_vars, result.x)} - - return detuning_values - - if device_arn == Devices.QuEra.Aquila: # use Aquila - backend = assigned_program.braket.aquila() - options = dict() - else: # use bloqade emulator - backend = assigned_program.bloqade.python() - options = dict(atol=1e-8, rtol=1e-4, solver_name="dopri5") - - # Run the hybrid job - return _runner(backend, n_calls, shots, **options) - - -# optimal_params = run_algo(program.args(detuning_vars), Devices.QuEra.Aquila) -optimal_params = run_algo(program.args(detuning_vars), None, n_calls=10, shots=10) - -assigned_program = program.assign(**optimal_params) -assigned_program.show() - -``` diff --git a/docs/home/gotchas.md b/docs/home/gotchas.md index a1a34e4e8b..c7cca10405 100644 --- a/docs/home/gotchas.md +++ b/docs/home/gotchas.md @@ -21,7 +21,7 @@ This is quite redundant and verbose, especially considering Bloqade offers a lar In the code above, we're just defining a 4x4 square lattice of atoms with 4.0 micrometers of spacing between them. This can be expressed as follows ```python -from bloqade.atom_arrangement import Square +from bloqade.analog.atom_arrangement import Square spacing = 4.0 geometry = Square(4, lattice_spacing = spacing) diff --git a/docs/home/migration.md b/docs/home/migration.md new file mode 100644 index 0000000000..9fafa6f9ac --- /dev/null +++ b/docs/home/migration.md @@ -0,0 +1,38 @@ +# Migrating to Bloqade Analog + +## Introduction + +In order to make room for more features inside the Bloqade ecosystem, we have created a new package to take the place of the old `bloqade` package. The new package is called `bloqade-analog`. The old package `bloqade` will house a namespace package for other features such as our new Bloqade Digital package with support for circuit-based quantum computers! + +## Installation + +You can install the package with `pip` in your Python environment of choice via: + +```sh +pip install bloqade-analog +``` + +## Migration + +The new package is a drop-in replacement for the old one. You can simply replace `import bloqade` with `import bloqade.analog` or `from bloqade.analog import ...` in your code. Everything else should work as before. + +## Example + +lets say your header of your python script looks like this: + +```python +from bloqade import var +from bloqade.atom_arrangement import Square +... +``` +You can simply replace it with: + +```python +from bloqade.analog import var +from bloqade.analog.atom_arrangement import Square +... +``` + +## Having trouble, comments, or concerns? + +Please open an issue on our [GitHub](https://github.com/QuEraComputing/bloqade-analog/issues) diff --git a/docs/home/quick_start.md b/docs/home/quick_start.md index 5960c45771..11fee9b1df 100644 --- a/docs/home/quick_start.md +++ b/docs/home/quick_start.md @@ -35,7 +35,7 @@ The same goes for [IPython](https://ipython.readthedocs.io/en/stable/): You can import pre-defined geometries based on [Bravais lattices](https://en.wikipedia.org/wiki/Bravais_lattice) from `bloqade.atom_arrangement`. You may also specify a lattice spacing which dictates the spacing between the atoms as well as the number of atom sites in a certain direction. ```python -from bloqade.atom_arrangement import Square, Kagome +from bloqade.analog.atom_arrangement import Square, Kagome simple_geometry = Square(2, 4, lattice_spacing = 4.0) more_complex_geometry = Kagome(2, 2, lattice_spacing = 2.0) @@ -54,7 +54,7 @@ more_complex_geometry.show() You can also add positions to a pre-defined geometry: ```python -from bloqade.atom_arrangement import Square +from bloqade.analog.atom_arrangement import Square base_geometry = Square(2) geometry_with_my_positions = base_geometry.add_position([(10,10), (20,20)]) @@ -63,7 +63,7 @@ geometry_with_my_positions = base_geometry.add_position([(10,10), (20,20)]) As well as apply defects via `.apply_defect_density`. In the example below we apply a defect with a probability of 0.2: ```python -from bloqade.atom_arrangement import Square, Kagome +from bloqade.analog.atom_arrangement import Square, Kagome more_complex_geometry = Kagome(2, 2, lattice_spacing = 2.0) defective_geometry = more_complex_geometry.apply_defect_density(0.2) @@ -219,7 +219,7 @@ pulse_sequence.show() And when you're content with it you just `.apply()` it on the geometries of your choice: ```python -from bloqade.atom_arrangement import Honeycomb, Kagome +from bloqade.analog.atom_arrangement import Honeycomb, Kagome geometry_1 = Honeycomb(2, lattice_spacing = 6.0) geometry_2 = Kagome(2, lattice_spacing = 6.0) diff --git a/docs/index.md b/docs/index.md index a885176fb5..c6aea31dd6 100644 --- a/docs/index.md +++ b/docs/index.md @@ -1,14 +1,18 @@
- Bloqade Logo - Bloqade Logo + Bloqade Logo + Bloqade Logo
[![Latest Version](https://img.shields.io/pypi/v/bloqade.svg)](https://pypi.python.org/pypi/bloqade) -[![Supported Python Versions](https://img.shields.io/pypi/pyversions/bloqade.svg)](https://pypi.python.org/pypi/bloqade) -[![codecov](https://codecov.io/github/QuEraComputing/bloqade-python/graph/badge.svg?token=4YJFc45Jyl)](https://codecov.io/github/QuEraComputing/bloqade-python) -![CI](https://github.com/QuEraComputing/bloqade-python/actions/workflows/ci.yml/badge.svg) +[![Supported Python Versions](https://img.shields.io/pypi/pyversions/bloqade-analog.svg)](https://pypi.python.org/pypi/bloqade-analog) +[![codecov](https://codecov.io/github/QuEraComputing/bloqade-analog/graph/badge.svg?token=4YJFc45Jyl)](https://codecov.io/github/QuEraComputing/bloqade-analog) +![CI](https://github.com/QuEraComputing/bloqade-analog/actions/workflows/ci.yml/badge.svg) + +> [!IMPORTANT] +> +> Bloqade has been restructured to make room for new features and improvements. Please refer to the [migration guide](./home/migration/) for more information. # **Welcome to Bloqade**: QuEra's Neutral Atom SDK @@ -31,7 +35,7 @@ Let's try a simple example where we drive a [Rabi oscillation][rabi-oscillation- We start by defining where our atoms go, otherwise known as the *atom geometry*. In this particular example we will use a small Honeycomb lattice: ```python -from bloqade.atom_arrangement import Honeycomb +from bloqade.analog.atom_arrangement import Honeycomb geometry = Honeycomb(2, lattice_spacing = 10.0) ``` @@ -125,7 +129,7 @@ If you want to try the above at once, we collected the above steps into the snip ```python from math import pi -from bloqade.atom_arrangement import Honeycomb +from bloqade.analog.atom_arrangement import Honeycomb geometry = Honeycomb(2, lattice_spacing = 10.0) rabi_program = ( @@ -156,7 +160,7 @@ hardware_bitstring_counts = hardware_results.report().counts() You can easily explore a number of common geometric lattices with Bloqade's `atom_arrangement`'s: ```python -from bloqade.atom_arrangement import Lieb, Square, Chain, Kagome +from bloqade.analog.atom_arrangement import Lieb, Square, Chain, Kagome geometry_1 = Lieb(3) geometry_2 = Square(2) @@ -184,7 +188,7 @@ geometry = start.add_positions([(0,0), (6,0), (12,0)]) Define waveforms for pulse sequences any way you like by either building (and chaining!) them immediately as part of your program: ```python -from bloqade.atom_arrangement import Square +from bloqade.analog.atom_arrangement import Square geometry = Square(2) target_rabi_amplitude = geometry.rydberg.rabi.amplitude.uniform @@ -198,7 +202,7 @@ custom_rabi_amp_waveform = ( Or building them separately and applying them later: ```python -from bloqade.atom_arrangement import Square, Chain +from bloqade.analog.atom_arrangement import Square, Chain geometry_1 = Square(3) geometry_2 = Chain(5) @@ -215,7 +219,7 @@ program_2 = geometry_2.apply(pulse_sequence) Go from a fast and powerful emulator: ```python -from bloqade.atom_arrangement import Square +from bloqade.analog.atom_arrangement import Square from math import pi geometry = Square(3, lattice_spacing = 6.5) @@ -277,7 +281,7 @@ task_number Want to just see some plots of your results? `.show()` will show you the way! ```python -from bloqade.atom_arrangement import Square +from bloqade.analog.atom_arrangement import Square rabi_amplitude_values = [0.0, 15.8, 15.8, 0.0] rabi_detuning_values = [-16.33, -16.33, 42.66, 42.66] @@ -299,7 +303,7 @@ emulation_results.report().show() ## Contributing to Bloqade -Bloqade is released under the [Apache License, Version 2.0](https://github.com/QuEraComputing/bloqade-python/blob/main/LICENSE). If you'd like the chance to shape the future of [neutral atom][neutral-atom-qubits] quantum computation, see our [Contributing Guide](contributing/index.md) for more info! +Bloqade is released under the [Apache License, Version 2.0](https://github.com/QuEraComputing/bloqade-analog/blob/main/LICENSE). If you'd like the chance to shape the future of [neutral atom][neutral-atom-qubits] quantum computation, see our [Contributing Guide](contributing/index.md) for more info! [rabi-oscillation-wiki]: https://en.wikipedia.org/wiki/Rabi_cycle [neutral-atom-qubits]: home/background.md#neutral-atom-qubits diff --git a/docs/reference/overview.md b/docs/reference/overview.md index 04f8796553..a448199634 100644 --- a/docs/reference/overview.md +++ b/docs/reference/overview.md @@ -1,6 +1,6 @@ # Builder Overview -You may have noticed from the [Getting Started](../home/getting_started.md) and [Tutorials](https://queracomputing.github.io/bloqade-python-examples/latest/) +You may have noticed from the [Getting Started](../home/getting_started.md) and [Tutorials](https://queracomputing.github.io/bloqade-analog-examples/latest/) that Bloqade uses this interesting, dot-intensive syntax. ```python diff --git a/mkdocs.yml b/mkdocs.yml index 822ac58b74..1349ebfe1c 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -3,33 +3,27 @@ site_url: https://bloqade.quera.com site_description: >- The neutral atom computing software development kit. -repo_name: bloqade-python -repo_url: https://github.com/QuEraComputing/bloqade-python +repo_name: bloqade-analog +repo_url: https://github.com/QuEraComputing/bloqade-analog edit_uri: 'edit/main/docs/' # Page tree nav: - Home: - index.md + - Migration Guide to Bloqade Analog: home/migration.md - Quickstart: home/quick_start.md - Background: home/background.md - - Geometry: home/geometry.md - - Waveforms: home/waveforms.md - - Visualization: home/visualization.md - - Emulation: home/emulation.md - - Submission: home/submission.md - Gotchas: home/gotchas.md - - Tutorials: 'https://queracomputing.github.io/bloqade-python-examples/latest/' - - Reference: + - Analog Tutorials: 'https://queracomputing.github.io/bloqade-analog-examples/dev/' + - Digital Tutorials: 'https://queracomputing.github.io/bloqade/latest/digital/' + - Bloqade Digital: 'https://queracomputing.github.io/bloqade/latest/' + - Blog: https://queracomputing.github.io/bloqade/latest/blog/ + - Analog Reference: - Builder Overview: reference/overview.md - Builder Standard Diagram: reference/standard.md - Hardware Reference: reference/hardware-capabilities.md - API References : reference/bloqade/ - - Blog: - - 2023: - - blog/2023/index.md - - Introducing Bloqade SDK for Python: blog/2023/posts/bloqade-release.md - - License: https://github.com/QuEraComputing/bloqade-python/blob/main/LICENSE - Contributing: - contributing/index.md - Reporting Bugs: contributing/reporting-a-bug.md @@ -40,6 +34,7 @@ nav: - Community Slack: contributing/community-slack.md - Asking a Question: contributing/asking-a-question.md - Providing Feedback: contributing/providing-feedback.md + - QuEra Computing: 'https://quera.com' copyright: Copyright © 2023 - QuEra Computing Inc. theme: @@ -82,7 +77,7 @@ theme: icon: material/brightness-4 name: Switch to light mode favicon: assets/favicon.ico - logo: assets/logo-dark.png + logo: assets/analog-logo-dark.svg font: text: Lato extra_css: @@ -121,9 +116,9 @@ extra: property: !ENV GOOGLE_ANALYTICS_KEY social: - icon: fontawesome/brands/github - link: https://github.com/QuEraComputing/bloqade-python + link: https://github.com/QuEraComputing/bloqade-analog - icon: fontawesome/brands/python - link: https://pypi.org/project/bloqade/ + link: https://pypi.org/project/bloqade-analog/ # TODO: make a mastodon account! # - icon: fontawesome/brands/mastodon # link: https://fosstodon.org/@squidfunk diff --git a/uv.lock b/uv.lock index 04573ff6b5..7f12f81417 100644 --- a/uv.lock +++ b/uv.lock @@ -1,16 +1,20 @@ version = 1 requires-python = ">=3.9, <3.13" resolution-markers = [ - "python_full_version < '3.10'", - "python_full_version == '3.10.*'", - "python_full_version == '3.11.*'", - "python_full_version >= '3.12'", + "python_full_version < '3.10' and platform_python_implementation == 'PyPy'", + "python_full_version < '3.10' and platform_python_implementation != 'PyPy'", + "python_full_version == '3.10.*' and platform_python_implementation == 'PyPy'", + "python_full_version == '3.10.*' and 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