Using-Namespace-System/classiq-models

Jupyter notebooks, qmods and applications

Create Quantum Programs with Classiq

The Classiq platform is all you need for creating any quantum program. Read more for the details.

flowchart
    IDEInput[<a href='https://platform.classiq.io/'>Classiq IDE</a>]

    SDKInput[<a href='https://docs.classiq.io/latest/getting-started/python-sdk/'>Classiq python SDK</a>]

    Model[<a href='https://docs.classiq.io/latest/user-guide/platform/qmod/'>Quantum Model</a>]

    Synthesis[<a href='https://docs.classiq.io/latest/user-guide/platform/synthesis/'>Synthesis Engine</a>]

    QuantumProgram[Quantum Program]

    Execution[<a href='https://docs.classiq.io/latest/user-guide/platform/executor/'>Execution</a>]

    Analyze[<a href='https://docs.classiq.io/latest/user-guide/platform/analyzer/'>Analyze & Debug</a>]

    ExternalProgram[<a href='https://docs.classiq.io/latest/user-guide/platform/executor/alternative-formats/'>Any Format, e.g QASM</a>]

    IBM[IBM]
    AWS[AWS Braket]
    Azure[Azure Quantum]
    Nvidia[Nvidia]


    IDEInput --> Model;
    SDKInput --> Model;
    Model --> Synthesis;
    Synthesis --> QuantumProgram;
    ExternalProgram <--> QuantumProgram;
    QuantumProgram --> Analyze;
    QuantumProgram --> Execution;
    Execution --> IBM
    Execution --> AWS
    Execution --> Azure
    Execution --> Nvidia

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Classiq's Github Repository

You can find a wide collection of quantum functions, algorithms, applications and tutorials built with Classiq.

Usage Example - Portfolio Optimization:

Go to the Portfolio optimization directory: applications/finance/portfolio_optimizaition

In the IDE:

1. Sign up to the classiq platform
2. Upload the .qmod file to the synthesis tab

In the SDK:

1. Start with downloading Classiq:

pip install -U classiq

2. Download the .ipynb file
3. Run the notebook in your preferred environment

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Build Your Own

With Classiq, you can build anything. Classiq provides a powerful modeling language to describe any quantum program, which can then be synthesized and executed on any hardware or simulator. Explore our Documentation to learn everything.

3+5 with Classiq

In the IDE:

1. Create a model (paste in the model tab)

qfunc get_3(output x: qbit[2]){
allocate<2>(x);
 X(x[0:1]);
 X(x[1:2]);
}

qfunc get_5(output x: qbit[3]){
 allocate<3>(x);
 X(x[0:1]);
 X(x[2:3]);
}

qfunc main(output res: qbit[4]){
 a: qbit[2];
 b: qbit[3];
 get_3(a);
 get_5(b);
 res = a + b;
}

2. Press Synthesize:

3. Press Execute:

3. Press Run:

4. View Results:

In the SDK:

from classiq import (
    QArray,
    Output,
    allocate,
    QFunc,
    X,
    QInt,
    synthesize,
    create_model,
    show,
    execute,
)


@QFunc
def get_3(x: Output[QArray]) -> None:
    allocate(2, x)
    X(x[0])
    X(x[1])


@QFunc
def get_5(x: Output[QArray]) -> None:
    allocate(3, x)
    X(x[0])
    X(x[2])


@QFunc
def main(res: Output[QInt]) -> None:
    a = QInt("a")
    b = QInt("b")
    get_3(a)
    get_5(b)
    res |= a + b  # should be 8


qprog = synthesize(create_model(main))

show(qprog)
result = execute(qprog).result()
print(result[0].value.parsed_counts)

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