Linux kernel release 2.6.xx These are the release notes for Linux version 2.6. Read them carefully, as they tell you what this is all about, explain how to install the kernel, and what to do if something goes wrong. WHAT IS LINUX? Linux is a Unix clone written from scratch by Linus Torvalds with assistance from a loosely-knit team of hackers across the Net. It aims towards POSIX compliance. It has all the features you would expect in a modern fully-fledged Unix, including true multitasking, virtual memory, shared libraries, demand loading, shared copy-on-write executables, proper memory management and TCP/IP networking. It is distributed under the GNU General Public License - see the accompanying COPYING file for more details. ON WHAT HARDWARE DOES IT RUN? Linux was first developed for 386/486-based PCs. These days it also runs on ARMs, DEC Alphas, SUN Sparcs, M68000 machines (like Atari and Amiga), MIPS and PowerPC, and others. DOCUMENTATION: - There is a lot of documentation available both in electronic form on the Internet and in books, both Linux-specific and pertaining to general UNIX questions. I'd recommend looking into the documentation subdirectories on any Linux FTP site for the LDP (Linux Documentation Project) books. This README is not meant to be documentation on the system: there are much better sources available. - There are various README files in the Documentation/ subdirectory: these typically contain kernel-specific installation notes for some drivers for example. See Documentation/00-INDEX for a list of what is contained in each file. Please read the Changes file, as it contains information about the problems, which may result by upgrading your kernel. - The Documentation/DocBook/ subdirectory contains several guides for kernel developers and users. These guides can be rendered in a number of formats: PostScript (.ps), PDF, and HTML, among others. After installation, "make psdocs", "make pdfdocs", or "make htmldocs" will render the documentation in the requested format. INSTALLING the kernel: - If you install the full sources, put the kernel tarball in a directory where you have permissions (eg. your home directory) and unpack it: gzip -cd linux-2.6.XX.tar.gz | tar xvf - Replace "XX" with the version number of the latest kernel. Do NOT use the /usr/src/linux area! This area has a (usually incomplete) set of kernel headers that are used by the library header files. They should match the library, and not get messed up by whatever the kernel-du-jour happens to be. - You can also upgrade between 2.6.xx releases by patching. Patches are distributed in the traditional gzip and the new bzip2 format. To install by patching, get all the newer patch files, enter the top level directory of the kernel source (linux-2.6.xx) and execute: gzip -cd ../patch-2.6.xx.gz | patch -p1 or bzip2 -dc ../patch-2.6.xx.bz2 | patch -p1 (repeat xx for all versions bigger than the version of your current source tree, _in_order_) and you should be ok. You may want to remove the backup files (xxx~ or xxx.orig), and make sure that there are no failed patches (xxx# or xxx.rej). If there are, either you or me has made a mistake. Alternatively, the script patch-kernel can be used to automate this process. It determines the current kernel version and applies any patches found. linux/scripts/patch-kernel linux The first argument in the command above is the location of the kernel source. Patches are applied from the current directory, but an alternative directory can be specified as the second argument. - If you are upgrading between releases using the stable series patches (for example, patch-2.6.xx.y), note that these "dot-releases" are not incremental and must be applied to the 2.6.xx base tree. For example, if your base kernel is 2.6.12 and you want to apply the 2.6.12.3 patch, you do not and indeed must not first apply the 2.6.12.1 and 2.6.12.2 patches. Similarly, if you are running kernel version 2.6.12.2 and want to jump to 2.6.12.3, you must first reverse the 2.6.12.2 patch (that is, patch -R) _before_ applying the 2.6.12.3 patch. - Make sure you have no stale .o files and dependencies lying around: cd linux make mrproper You should now have the sources correctly installed. SOFTWARE REQUIREMENTS Compiling and running the 2.6.xx kernels requires up-to-date versions of various software packages. Consult Documentation/Changes for the minimum version numbers required and how to get updates for these packages. Beware that using excessively old versions of these packages can cause indirect errors that are very difficult to track down, so don't assume that you can just update packages when obvious problems arise during build or operation. BUILD directory for the kernel: When compiling the kernel all output files will per default be stored together with the kernel source code. Using the option "make O=output/dir" allow you to specify an alternate place for the output files (including .config). Example: kernel source code: /usr/src/linux-2.6.N build directory: /home/name/build/kernel To configure and build the kernel use: cd /usr/src/linux-2.6.N make O=/home/name/build/kernel menuconfig make O=/home/name/build/kernel sudo make O=/home/name/build/kernel modules_install install Please note: If the 'O=output/dir' option is used then it must be used for all invocations of make. CONFIGURING the kernel: Do not skip this step even if you are only upgrading one minor version. New configuration options are added in each release, and odd problems will turn up if the configuration files are not set up as expected. If you want to carry your existing configuration to a new version with minimal work, use "make oldconfig", which will only ask you for the answers to new questions. - Alternate configuration commands are: "make menuconfig" Text based color menus, radiolists & dialogs. "make xconfig" X windows (Qt) based configuration tool. "make gconfig" X windows (Gtk) based configuration tool. "make oldconfig" Default all questions based on the contents of your existing ./.config file. "make silentoldconfig" Like above, but avoids cluttering the screen with questions already answered. NOTES on "make config": - having unnecessary drivers will make the kernel bigger, and can under some circumstances lead to problems: probing for a nonexistent controller card may confuse your other controllers - compiling the kernel with "Processor type" set higher than 386 will result in a kernel that does NOT work on a 386. The kernel will detect this on bootup, and give up. - A kernel with math-emulation compiled in will still use the coprocessor if one is present: the math emulation will just never get used in that case. The kernel will be slightly larger, but will work on different machines regardless of whether they have a math coprocessor or not. - the "kernel hacking" configuration details usually result in a bigger or slower kernel (or both), and can even make the kernel less stable by configuring some routines to actively try to break bad code to find kernel problems (kmalloc()). Thus you should probably answer 'n' to the questions for "development", "experimental", or "debugging" features. COMPILING the kernel: - Make sure you have gcc 2.95.3 available. gcc 2.91.66 (egcs-1.1.2), and gcc 2.7.2.3 are known to miscompile some parts of the kernel, and are *no longer supported*. Also remember to upgrade your binutils package (for as/ld/nm and company) if necessary. For more information, refer to Documentation/Changes. Please note that you can still run a.out user programs with this kernel. - Do a "make" to create a compressed kernel image. It is also possible to do "make install" if you have lilo installed to suit the kernel makefiles, but you may want to check your particular lilo setup first. To do the actual install you have to be root, but none of the normal build should require that. Don't take the name of root in vain. - If you configured any of the parts of the kernel as `modules', you will also have to do "make modules_install". - Keep a backup kernel handy in case something goes wrong. This is especially true for the development releases, since each new release contains new code which has not been debugged. Make sure you keep a backup of the modules corresponding to that kernel, as well. If you are installing a new kernel with the same version number as your working kernel, make a backup of your modules directory before you do a "make modules_install". Alternatively, before compiling, use the kernel config option "LOCALVERSION" to append a unique suffix to the regular kernel version. LOCALVERSION can be set in the "General Setup" menu. - In order to boot your new kernel, you'll need to copy the kernel image (e.g. .../linux/arch/i386/boot/bzImage after compilation) to the place where your regular bootable kernel is found. - Booting a kernel directly from a floppy without the assistance of a bootloader such as LILO, is no longer supported. If you boot Linux from the hard drive, chances are you use LILO which uses the kernel image as specified in the file /etc/lilo.conf. The kernel image file is usually /vmlinuz, /boot/vmlinuz, /bzImage or /boot/bzImage. To use the new kernel, save a copy of the old image and copy the new image over the old one. Then, you MUST RERUN LILO to update the loading map!! If you don't, you won't be able to boot the new kernel image. Reinstalling LILO is usually a matter of running /sbin/lilo. You may wish to edit /etc/lilo.conf to specify an entry for your old kernel image (say, /vmlinux.old) in case the new one does not work. See the LILO docs for more information. After reinstalling LILO, you should be all set. Shutdown the system, reboot, and enjoy! If you ever need to change the default root device, video mode, ramdisk size, etc. in the kernel image, use the 'rdev' program (or alternatively the LILO boot options when appropriate). No need to recompile the kernel to change these parameters. - Reboot with the new kernel and enjoy. IF SOMETHING GOES WRONG: - If you have problems that seem to be due to kernel bugs, please check the file MAINTAINERS to see if there is a particular person associated with the part of the kernel that you are having trouble with. If there isn't anyone listed there, then the second best thing is to mail them to me (torvalds@osdl.org), and possibly to any other relevant mailing-list or to the newsgroup. - In all bug-reports, *please* tell what kernel you are talking about, how to duplicate the problem, and what your setup is (use your common sense). If the problem is new, tell me so, and if the problem is old, please try to tell me when you first noticed it. - If the bug results in a message like unable to handle kernel paging request at address C0000010 Oops: 0002 EIP: 0010:XXXXXXXX eax: xxxxxxxx ebx: xxxxxxxx ecx: xxxxxxxx edx: xxxxxxxx esi: xxxxxxxx edi: xxxxxxxx ebp: xxxxxxxx ds: xxxx es: xxxx fs: xxxx gs: xxxx Pid: xx, process nr: xx xx xx xx xx xx xx xx xx xx xx or similar kernel debugging information on your screen or in your system log, please duplicate it *exactly*. The dump may look incomprehensible to you, but it does contain information that may help debugging the problem. The text above the dump is also important: it tells something about why the kernel dumped code (in the above example it's due to a bad kernel pointer). More information on making sense of the dump is in Documentation/oops-tracing.txt - If you compiled the kernel with CONFIG_KALLSYMS you can send the dump as is, otherwise you will have to use the "ksymoops" program to make sense of the dump. This utility can be downloaded from ftp://ftp.<country>.kernel.org/pub/linux/utils/kernel/ksymoops. Alternately you can do the dump lookup by hand: - In debugging dumps like the above, it helps enormously if you can look up what the EIP value means. The hex value as such doesn't help me or anybody else very much: it will depend on your particular kernel setup. What you should do is take the hex value from the EIP line (ignore the "0010:"), and look it up in the kernel namelist to see which kernel function contains the offending address. To find out the kernel function name, you'll need to find the system binary associated with the kernel that exhibited the symptom. This is the file 'linux/vmlinux'. To extract the namelist and match it against the EIP from the kernel crash, do: nm vmlinux | sort | less This will give you a list of kernel addresses sorted in ascending order, from which it is simple to find the function that contains the offending address. Note that the address given by the kernel debugging messages will not necessarily match exactly with the function addresses (in fact, that is very unlikely), so you can't just 'grep' the list: the list will, however, give you the starting point of each kernel function, so by looking for the function that has a starting address lower than the one you are searching for but is followed by a function with a higher address you will find the one you want. In fact, it may be a good idea to include a bit of "context" in your problem report, giving a few lines around the interesting one. If you for some reason cannot do the above (you have a pre-compiled kernel image or similar), telling me as much about your setup as possible will help. - Alternately, you can use gdb on a running kernel. (read-only; i.e. you cannot change values or set break points.) To do this, first compile the kernel with -g; edit arch/i386/Makefile appropriately, then do a "make clean". You'll also need to enable CONFIG_PROC_FS (via "make config"). After you've rebooted with the new kernel, do "gdb vmlinux /proc/kcore". You can now use all the usual gdb commands. The command to look up the point where your system crashed is "l *0xXXXXXXXX". (Replace the XXXes with the EIP value.) gdb'ing a non-running kernel currently fails because gdb (wrongly) disregards the starting offset for which the kernel is compiled.
English | 简体中文
Paddle Quantum (量桨)
- Features
- Install
- Introduction and developments
- Feedbacks
- Research with Paddle Quantum
- Frequently Asked Questions
- Copyright and License
- References
Paddle Quantum (量桨) is the world's first cloud-integrated quantum machine learning platform based on Baidu PaddlePaddle. It supports the building and training of quantum neural networks, making PaddlePaddle the first deep learning framework in China. Paddle Quantum is feature-rich and easy to use. It provides comprehensive API documentation and tutorials help users get started right away.
Paddle Quantum aims at establishing a bridge between artificial intelligence (AI) and quantum computing (QC). It has been utilized for developing several quantum machine learning applications. With the PaddlePaddle deep learning platform empowering QC, Paddle Quantum provides strong support for scientific research community and developers in the field to easily develop QML applications. Moreover, it provides a learning platform for quantum computing enthusiasts.
Features
- Easy-to-use
- Many online learning resources (Nearly 50 tutorials)
- High efficiency in building QNN with various QNN templates
- Automatic differentiation
- Versatile
- Multiple optimization tools and GPU mode
- Simulation with 25+ qubits
- Flexible noise models
- Featured Toolkits
- Toolboxes for Chemistry & Optimization
- LOCCNet for distributed quantum information processing
- Self-developed QML algorithms
Install
Install PaddlePaddle
This dependency will be automatically satisfied when users install Paddle Quantum. Please refer to PaddlePaddle's official installation and configuration page. This project requires PaddlePaddle 2.2.0 to 2.3.0.
Install Paddle Quantum
We recommend the following way of installing Paddle Quantum with pip
,
pip install paddle-quantum
or download all the files and finish the installation locally,
git clone https://github.com/PaddlePaddle/quantum
cd quantum
pip install -e .
Environment setup for Quantum Chemistry module
Currently, our qchem
module uses PySCF
as its backend to compute molecular integrals, so before executing quantum chemistry, we have to install this Python package.
It is recommended that
PySCF
is installed in a Python environment whose Python version >=3.6.
We highly recommend you to install PySCF
via conda. MacOS/Linux user can use the command:
conda install -c pyscf pyscf
NOTE: For Windows user, if your operating system is Windows10, you can install
PySCF
in Ubuntu subsystem provided by Windows 10's App Store.PySCF
can't run directly in Windows, so we are working hard to develop more quantum chemistry backends. Our support for Windows will be improved in the coming release of Paddle Quantum.
Note: Please refer to PySCF for more download options.
Run example
Now, you can try to run a program to verify whether Paddle Quantum has been installed successfully. Here we take quantum approximate optimization algorithm (QAOA) as an example.
cd paddle_quantum/QAOA/example
python main.py
For the introduction of QAOA, please refer to our QAOA tutorial.
Breaking Change
In version 2.2.0 of Paddle Quantum, we have made an incompatible upgrade to the code architecture, and the new version's structure and usage can be found in our tutorials, API documentation, and the source code. Also, we support connecting to a real quantum computer via QuLeaf, using paddle_quantum.set_backend('quleaf')
to select QuLeaf as the backend.
Introduction and developments
Quick start
Paddle Quantum Quick Start Manual is probably the best place to get started with Paddle Quantum. Currently, we support online reading and running the Jupyter Notebook locally. The manual includes the following contents:
- Detailed installation tutorials for Paddle Quantum
- Introduction to quantum computing and quantum neural networks (QNNs)
- Introduction to Variational Quantum Algorithms (VQAs)
- Introduction to Paddle Quantum
- PaddlePaddle optimizer tutorial
- Introduction to the quantum chemistry module in Paddle Quantum
- How to train QNN with GPU
Tutorials
We provide tutorials covering quantum simulation, machine learning, combinatorial optimization, local operations and classical communication (LOCC), and other popular QML research topics. Each tutorial currently supports reading on our website and running Jupyter Notebooks locally. For interested developers, we recommend them to download Jupyter Notebooks and play around with it. Here is the tutorial list,
-
- Building Molecular Hamiltonian
- Variational Quantum Eigensolver (VQE)
- Subspace Search-Quantum Variational Quantum Eigensolver (SSVQE)
- Variational Quantum State Diagonalization (VQSD)
- Gibbs State Preparation
- The Classical Shadow of Unknown Quantum States
- Estimation of Quantum State Properties Based on the Classical Shadow
- Hamiltonian Simulation with Product Formula
- Simulate the Spin Dynamics on a Heisenberg Chain
- Distributed Variational Quantum Eigensolver Based on Schmidt Decomposition
- Quantum Signal Processing and Quantum Singular Value Transformation
- Hamiltonian Simulation with qDRIFT
- Quantum Phase Processing
- Variational Quantum Metrology
-
- Encoding Classical Data into Quantum States
- Quantum Classifier
- Variational Shadow Quantum Learning (VSQL)
- Quantum Kernel Methods
- Quantum Autoencoder
- Quantum GAN
- Variational Quantum Singular Value Decomposition (VQSVD)
- Data Encoding Analysis
- Quantum Neural Network Approximating Functions
- Variational quantum amplitude estimation
-
- Quantum Approximation Optimization Algorithm (QAOA)
- Solving Max-Cut Problem with QAOA
- Large-scale QAOA via Divide-and-Conquer
- Travelling Salesman Problem
- Quantum Finance Application on Arbitrage Opportunity Optimization
- Quantum Finance Application on Portfolio Optimization
- Quantum Finance Application on Portfolio Diversification
With the latest LOCCNet module, Paddle Quantum can efficiently simulate distributed quantum information processing tasks. Interested readers can start with this tutorial on LOCCNet. In addition, Paddle Quantum supports QNN training on GPU. For users who want to get into more details, please check out the tutorial Use Paddle Quantum on GPU. Moreover, Paddle Quantum could design robust quantum algorithms under noise. For more information, please see Noise tutorial.
In a recent update, the measurement-based quantum computation (MBQC) module has been added to Paddle Quantum. Unlike the conventional quantum circuit model, MBQC has its unique way of computing. Interested readers are welcomed to read our tutorials on how to use the MBQC module and its use cases.
API documentation
For those who are looking for explanation on the python class and functions provided in Paddle Quantum, we refer to our API documentation page.
We, in particular, denote that the current docstring specified in source code is written in simplified Chinese, this will be updated in later versions.
Feedbacks
Users are encouraged to contact us through GitHub Issues or email quantum@baidu.com with general questions, unfixed bugs, and potential improvements. We hope to make Paddle Quantum better together with the community!
Research based on Paddle Quantum
We also highly encourage developers to use Paddle Quantum as a research tool to develop novel QML algorithms. If your work uses Paddle Quantum, feel free to send us a notice via qml@baidu.com. We are always excited to hear that! Cite us with the following BibTeX:
@misc{Paddlequantum, title = {{Paddle Quantum}}, year = {2020}, url = {https://github.com/PaddlePaddle/Quantum}, }
So far, we have done several projects with the help of Paddle Quantum as a powerful QML development platform.
[1] Wang, Youle, Guangxi Li, and Xin Wang. "Variational quantum Gibbs state preparation with a truncated Taylor series." Physical Review Applied 16.5 (2021): 054035. [pdf]
[2] Wang, Xin, Zhixin Song, and Youle Wang. "Variational quantum singular value decomposition." Quantum 5 (2021): 483. [pdf]
[3] Li, Guangxi, Zhixin Song, and Xin Wang. "VSQL: Variational Shadow Quantum Learning for Classification." Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 35. No. 9. 2021. [pdf]
[4] Chen, Ranyiliu, et al. "Variational quantum algorithms for trace distance and fidelity estimation." Quantum Science and Technology (2021). [pdf]
[5] Wang, Kun, et al. "Detecting and quantifying entanglement on near-term quantum devices." arXiv preprint arXiv:2012.14311 (2020). [pdf]
[6] Zhao, Xuanqiang, et al. "Practical distributed quantum information processing with LOCCNet." npj Quantum Information 7.1 (2021): 1-7. [pdf]
[7] Cao, Chenfeng, and Xin Wang. "Noise-Assisted Quantum Autoencoder." Physical Review Applied 15.5 (2021): 054012. [pdf]
Frequently Asked Questions
-
Question: What is quantum machine learning? What are the applications?
Answer: Quantum machine learning (QML) is an interdisciplinary subject that combines quantum computing (QC) and machine learning (ML). On the one hand, QML utilizes existing artificial intelligence technology to break through the bottleneck of quantum computing research. On the other hand, QML uses the information processing advantages of quantum computing to promote the development of traditional artificial intelligence. QML is not only suitable for quantum chemical simulations (with Variational Quantum Eigensolver) and other quantum problems. It also help researchers to solve classical optimization problems including knapsack problem, traveling salesman problem, and Max-Cut problem through the Quantum Approximate Optimization Algorithm.
-
Question: I want to study QML, but I don't know much about quantum computing. Where should I start?
Answer: Quantum Computation and Quantum Information by Nielsen & Chuang is the classic introductory textbook to QC. We recommend readers to study Chapter 1, 2, and 4 of this book first. These chapters introduce the basic concepts, provide solid mathematical and physical foundations, and discuss the quantum circuit model widely used in QC. Readers can also go through Paddle Quantum's quick start guide, which contains a brief introduction to QC and interactive examples. After building a general understanding of QC, readers can try some cutting-edge QML applications provided as tutorials in Paddle Quantum.
-
Question: Currently, there is no fault-tolerant large-scale quantum hardware. How can we develop quantum applications?
Answer: The development of useful algorithms does not necessarily require a perfect hardware. The latter is more of an engineering problem. With Paddle Quantum, one can develop, simulate, and verify the validity of self-innovated quantum algorithms. Then, researchers can choose to implement these tested quantum algorithms in a small scale hardware and see the actual performance of it. Following this line of reasoning, we can prepare ourselves with many candidates of useful quantum algorithms before the age of matured quantum hardware.
-
Question: What are the advantages of Paddle Quantum?
Answer: Paddle Quantum is an open-source QML toolkit based on Baidu PaddlePaddle. As the first open-source and industrial level deep learning platform in China, PaddlePaddle has the leading ML technology and rich functionality. With the support of PaddlePaddle, especially its dynamic computational graph mechanism, Paddle Quantum could easily train a QNN and with GPU acceleration. In addition, based on the high-performance quantum simulator developed by Institute for Quantum Computing (IQC) at Baidu, Paddle Quantum can simulate more than 20 qubits on personal laptops. Finally, Paddle Quantum provides many open-source QML tutorials for readers from different backgrounds.
Copyright and License
Paddle Quantum uses Apache-2.0 license.
References
[1] Quantum Computing - Wikipedia
[2] Nielsen, M. A. & Chuang, I. L. Quantum computation and quantum information. (2010).
[3] Phillip Kaye, Laflamme, R. & Mosca, M. An Introduction to Quantum Computing. (2007).
[4] Biamonte, J. et al. Quantum machine learning. Nature 549, 195–202 (2017).