TornadoQSim | High-performance and modular quantum simulator in Java

 by   beehive-lab Java Version: Current License: Apache-2.0

kandi X-RAY | TornadoQSim Summary

kandi X-RAY | TornadoQSim Summary

TornadoQSim is a Java library typically used in Quantum Computing applications. TornadoQSim has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However TornadoQSim build file is not available. You can download it from GitHub.

TornadoQSim is a quantum computing framework, that allows to develop quantum algorithms and simulate them on a variety of quantum simulators. The aim is to achieve high-performance simulation of quantum circuits without the need to develop the simulator back-end in a platform specific programming language, such as CUDA. This framework is developed entirely in Java, including the quantum simulator back-ends that are accelerated on heterogeneous hardware using TornadoVM. TornadoQSim was developed by Ales Kubicek as part of the BSc Computer Science degree (final year project) at the University of Manchester.
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              TornadoQSim has a low active ecosystem.
              It has 13 star(s) with 1 fork(s). There are 7 watchers for this library.
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              It had no major release in the last 6 months.
              TornadoQSim has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of TornadoQSim is current.

            kandi-Quality Quality

              TornadoQSim has no bugs reported.

            kandi-Security Security

              TornadoQSim has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

            kandi-License License

              TornadoQSim is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

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              TornadoQSim releases are not available. You will need to build from source code and install.
              TornadoQSim has no build file. You will be need to create the build yourself to build the component from source.
              Installation instructions, examples and code snippets are available.

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            TornadoQSim Key Features

            No Key Features are available at this moment for TornadoQSim.

            TornadoQSim Examples and Code Snippets

            No Code Snippets are available at this moment for TornadoQSim.

            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

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            Install TornadoQSim

            Install TornadoVM. The following example builds TornadoVM with GraalVM JDK 11 and OpenCL:

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