| Name | qubit-discovery JSON |
| Version |
1.0.0
JSON |
| download |
| home_page | None |
| Summary | Tools to optimize superconducting circuits using SQcircuit. |
| upload_time | 2024-08-26 08:47:58 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.9 |
| license | BSD 3-Clause License Copyright (c) 2024, Taha Rajabzadeh, Alex Boulton-McKeehan, Sam Bonkowsky, and Amir Safavi-Naeini Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: 1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. 2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. 3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
superconducting circuits
superconducting qubits
machine learning
pytorch
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
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<div align="center">
<img alt="Logo image" src="https://raw.githubusercontent.com/stanfordLINQS/Qubit-Discovery/main/pics/light_logo_qd.png" width="150" height="auto">
</div>
# qubit-discovery
qubit-discovery is an open-source Python library for optimizing superconducting circuits, built on top of [SQcircuit](https://sqcircuit.org/) and [PyTorch](https://pytorch.org/). It provides:
* Composable loss functions with a special focus on qubit design, and straightforward methods to add new custom ones.
* Fine-tuned BFGS and SGD algorithms to optimize circuits, along with an interface to use other PyTorch optimizers.
* Utility features including random circuit sampling and functions to automatically choose circuit truncation numbers.
With these capabilities, you can easily optimize any superconducting circuit for decoherence time, anharmonicity, charge sensitivity, or other desired targets.
A description of the theory involved and example application is provided in the following paper:
> Taha Rajabzadeh, Alex Boulton-McKeehan, Sam Bonkowsky, David I. Schuster, Amir H. Safavi-Naeini, "A General Framework for Gradient-Based Optimization of Superconducting Quantum Circuits using Qubit Discovery as a Case Study", arXiv:2408.12704 (2024), https://arxiv.org/abs/2408.12704.
If qubit-discovery is useful to you, we welcome contributions to its development and maintenance! Use of the package in publications may be acknowledged by citing the above paper.
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