quimb | python library for quantum information

 by   jcmgray Jupyter Notebook Version: v1.5.0 License: Non-SPDX

kandi X-RAY | quimb Summary

kandi X-RAY | quimb Summary

quimb is a Jupyter Notebook library typically used in Quantum Computing applications. quimb has no vulnerabilities and it has low support. However quimb has 18 bugs and it has a Non-SPDX License. You can download it from GitHub, GitLab.

A python library for quantum information and many-body calculations including tensor networks.
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              quimb has a low active ecosystem.
              It has 338 star(s) with 90 fork(s). There are 20 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 40 open issues and 79 have been closed. On average issues are closed in 57 days. There are 5 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of quimb is v1.5.0

            kandi-Quality Quality

              quimb has 18 bugs (0 blocker, 3 critical, 15 major, 0 minor) and 600 code smells.

            kandi-Security Security

              quimb has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              quimb code analysis shows 0 unresolved vulnerabilities.
              There are 5 security hotspots that need review.

            kandi-License License

              quimb has a Non-SPDX License.
              Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

            kandi-Reuse Reuse

              quimb releases are available to install and integrate.
              quimb saves you 9414 person hours of effort in developing the same functionality from scratch.
              It has 19225 lines of code, 1841 functions and 67 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed quimb and discovered the below as its top functions. This is intended to give you an instant insight into quimb implemented functionality, and help decide if they suit your requirements.
            • Multiply a gate
            • Factorize a gate into a tensor
            • Convert an numpy array into a numpy array
            • Return the number of dimensions of an array
            • Return a setuptools cmdclass
            • Extract the version information
            • Get the project root directory
            • Create a ConfigParser instance from root
            • R Compute local expectation cluster
            • Compute Plaquette environments
            • Draws the plot
            • Specify bond boundary
            • R Generates a classification partition function
            • R Kraus operator
            • Computes plaquette envs
            • Calculates the local state of the local state 1 site
            • R Solve eigenvalue decomposition
            • Generate a 2D hamming
            • Partial trace
            • Return the contract boundary of a molecule
            • Return the bounding boundary of the molecule
            • Plot the graph
            • Compute the local expectations for a molecule
            • Perform full update
            • Create a contract boundary bounding box
            • Compute the local expectation using local expectations
            Get all kandi verified functions for this library.

            quimb Key Features

            No Key Features are available at this moment for quimb.

            quimb Examples and Code Snippets

            No Code Snippets are available at this moment for quimb.

            Community Discussions

            QUESTION

            Why Pauli Z can be used to measure a single qubit ?
            Asked 2022-Mar-30 at 09:21

            According to the Q# documentation, a single qubit can be measured by M.The method uses Pauli-Z. But why Pauli Z can be used to measure a single qubit? I have known the matrix of Pauli-Z like below:

            and the output result is given by the distribution:

            But what's the relationship between the matrix and the formula? What's happened with method M? I really need your help.

            ...

            ANSWER

            Answered 2022-Mar-25 at 18:18

            Pauli Z matrix defines the basis in which the measurement is performed. A measurement in the Pauli Z basis is the same as the computational basis measurement, projecting the state onto one of the states |0⟩ or |1⟩ (the eigenstates of Pauli Z matrix).

            I'm not up for spelling the math here, since classical StackOverflow doesn't support LaTeX. You can find a good tutorial on single-qubit measurements in Q# in the Quantum Katas project.

            Source https://stackoverflow.com/questions/71617443

            QUESTION

            where can I get the detailed tutorial or document for Q# machine learning
            Asked 2022-Mar-25 at 17:34

            Recently, I'm learning the Q# language for machine learning. The sample of half-moons has been run correctly. Now I want to learn the detail of the code. But there is too little explanation to find. There are too many methods I can't understand and there are no introductions in detail. For example, it only explains the name, parameters for the method, but no further information. I really can't understand it. So is there an exits detailed document for machine learning for beginners? Thank u very much.

            how to get the detained document

            ...

            ANSWER

            Answered 2022-Mar-25 at 17:34

            Q# machine learning library implements one specific approach, circuit-centric quantum classifiers. You can find the documentation for this approach at https://docs.microsoft.com/en-us/azure/quantum/user-guide/libraries/machine-learning/intro and the subsequent pages in that section. The paper it's based on is 'Circuit-centric quantum classifiers', Maria Schuld, Alex Bocharov, Krysta Svore and Nathan Wiebe.

            Source https://stackoverflow.com/questions/71596588

            QUESTION

            Accessing Qubit With DAGCircuit
            Asked 2022-Mar-22 at 11:46

            I'm currently trying to make my own TransformationPass to use when compiling a QuantumCircuit for specific hardware, but I'm struggling to get things to work with the DAGCircuit that gets passed to the run(self, dag) method that gets overridden. My main issue at the moment is trying to figure out which qubits each node in the graph actually operates on. I can access the wire for each node, but accessing the qubit index from there raises a DeprecationWarning.

            I can simply ignore the warning, but it gives me the impression that I should be going about this another way.

            Is there a formal method for accessing the qubit (either object or simply its index) given the DAG?

            ...

            ANSWER

            Answered 2022-Mar-22 at 11:46

            For DAGCircuit right now there isn't a great answer for this. The .index attribute is deprecated as in the case of standalone bit objects on the circuit if they're in a register it might not yield the result you expect (it'll be the register index not the index on the circuit necessarily).

            I typically do this by having something like:

            Source https://stackoverflow.com/questions/71571268

            QUESTION

            Missing types, namespaces, directives, and assembly references
            Asked 2022-Feb-27 at 10:24

            I use VS Code for C# and Unity3D and TypeScript and Angular and Python programming, so I have pretty much every required extension, including the .NET Framework and Core as well as the Quantum Development Kit (QDK) plus the Q# Interoperability Tools and also C# and Python extensions for VS Code.

            I have devised the following steps to create my first quantum Hello World based on a few tutorials:

            ...

            ANSWER

            Answered 2022-Feb-27 at 10:24

            With help from a user on another forum, it turns out the problem was the command:

            Source https://stackoverflow.com/questions/71100198

            QUESTION

            Deutsch algorithm with NOT gate as oracle
            Asked 2021-Aug-01 at 05:36

            I tried to implement Deutsch algorithm using qiskit. The following is a code.

            ...

            ANSWER

            Answered 2021-Aug-01 at 05:36

            Deutsch algorithm applies the X gate to the qubit you use for phase kickback trick, to prepare it in the |-⟩ state before applying the oracle. Your implementation applies it to the "data" qubit instead, so that the combined effect of the algorithm (after H gates cancel out) is just preparing the data qubit in |1⟩ state.

            Source https://stackoverflow.com/questions/68600069

            QUESTION

            Why does drawing a qiskit quantum circuit look different when I run a jupyter notebook locally
            Asked 2021-Jun-05 at 17:40

            I'm using the qiskit textbook, and it creates a QuantumCircuit and then draws the circuit, and it looks like this:

            I see the same result when running the textbook as a jupyter notebook in IBM's quantum lab.

            However, when I download the textbook as a jupyter notebook and run it myself locally, it looks like this:

            I don't like this very much, and I think I am missing something simple. The code that is running is exactly the same. I am using MacOS 11.4 (Big Sur). The following code is sufficient to show a difference when I run it online vs. locally:

            ...

            ANSWER

            Answered 2021-Jun-05 at 17:40

            Because Qiskit has multiple drawers. Those are:

            • text
            • mpl
            • latex
            • latex_source.

            The drawer you see in the IBM Quantum Lab is the one based on Matplotlib. You can get the same output by qc.draw('mpl').

            To set a default, you can change (or create if does not exist) the file ~/.qiskit/settings.conf) with the entry circuit_drawer = mpl.

            Source https://stackoverflow.com/questions/67852048

            QUESTION

            How to solve TSP problem with more than 3 nodes in the tutorial of Max-Cut and Traveling Salesman Problem Qiskit 0.24.0?
            Asked 2021-Jun-05 at 12:02

            I had to try the example of qiskit’s Traveling Salesman Problem with 3 nodes and executing it at IBM backend called simulator_statevector.Can execute and get the result normally.

            But when trying to solve the TSP problem with more than 3 nodes,I changed n = 3 to n = 4.

            ...

            ANSWER

            Answered 2021-Jun-05 at 12:02

            I found the answer, my method is to increase the Ansat number of reps from 5 to 7.

            from solving TSP 4 node problem

            Source https://stackoverflow.com/questions/66920772

            QUESTION

            How to decide bias in Hamiltonian Ising model? python
            Asked 2021-May-12 at 03:26

            I am trying to code finance portfolio optimisation problem into a quantum annealer, using the Hamiltonian Ising model. I am using the dwave module

            ...

            ANSWER

            Answered 2021-May-12 at 03:26

            If you are familiar with the physics of the Ising model (e.g. just look it up on wikipedia), you will find out that the term "linear bias" h is used instead of the physics term external constant magnetic field and the term "quadratic bias" J is used instead of the physics term of interaction between a pair of (neighbouring in the case of the Ising model) spins. My guess is that the h and J coefficients must be learned from some given data. Your job is to cast (interpret) the data available to you into an Ising model configuration (state) and then use some sort of optimization with unknown h and J that minimizes the difference between the model's solutions (theoretical Ising model configuration) and the observed data.

            Source https://stackoverflow.com/questions/67462360

            QUESTION

            Where was Qiskit-Textbook downloaded?
            Asked 2021-May-11 at 12:41

            I've installed Qiskit-textbook by pip install git+https://github.com/qiskit-community/qiskit-textbook.git#subdirectory=qiskit-textbook-src. But I don't know where is it downloaded

            ...

            ANSWER

            Answered 2021-May-11 at 12:41

            That command installs the Qiskit Textbook package, which is a Python package containing some of the problems and widgets used in the textbook. You can see the location of an installed package using pip show :

            Source https://stackoverflow.com/questions/67448011

            QUESTION

            Quantum computing vs traditional base10 systems
            Asked 2021-Feb-24 at 18:40

            This may show my naiveté but it is my understanding that quantum computing's obstacle is stabilizing the qbits. I also understand that standard computers use binary (on/off); but it seems like it may be easier with today's tech to read electric states between 0 and 9. Binary was the answer because it was very hard to read the varying amounts of electricity, components degrade over time, and maybe maintaining a clean electrical "signal" was challenging.

            But wouldn't it be easier to try to solve the problem of reading varying levels of electricity so we can go from 2 inputs to 10 and thereby increasing the smallest unit of storage and exponentially increasing the number of paths through the logic gates? I know I am missing quite a bit (sorry the puns were painful) so I would love to hear why or why not. Thank you

            ...

            ANSWER

            Answered 2021-Feb-24 at 18:40

            "Exponentially increasing the number of paths through the logic gates" is exactly the problem. More possible states for each n-ary digit means more transistors, larger gates and more complex CPUs. That's not to say no one is working on ternary and similar systems, but the reason binary is ubiquitous is its simplicity. For storage, more possible states also means we need more sensitive electronics for reading and writing, and a much higher error frequency during these operations. There's a lot of hype around using DNA (base-4) for storage, but this is more on account of the density and durability of the substrate.

            You're correct, though that your question is missing quite a bit - qubits are entirely different from classical information, whether we use bits or digits. Classical bits and trits respectively correspond to vectors like

            Source https://stackoverflow.com/questions/66148518

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install quimb

            You can download it from GitHub, GitLab.

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/jcmgray/quimb.git

          • CLI

            gh repo clone jcmgray/quimb

          • sshUrl

            git@github.com:jcmgray/quimb.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link

            Explore Related Topics

            Consider Popular Jupyter Notebook Libraries

            Try Top Libraries by jcmgray

            cotengra

            by jcmgrayPython

            autoray

            by jcmgrayJupyter Notebook

            xyzpy

            by jcmgrayPython

            opt_einsum_samples

            by jcmgrayJupyter Notebook

            einsum_bmm

            by jcmgrayPython