Quantum Gate Model

Quantum Approximate Optimization Algorithm

Usage via LunaSolve
Usage via LunaBench
Backends

Fire Opal Quantum Approximate Optimization Algorithm

Usage via LunaSolve
Usage via LunaBench
Backends

Variational Quantum Eigensolver

Usage via LunaSolve
Usage via LunaBench
Backends

Quantum Approximate Optimization Algorithm

Short Name: QAOA
Algorithm Type: Hybrid
Category: Hybrid-External
Native Input Format: QUBO

The Quantum Approximate Optimization Algorithm (QAOA) solves combinatorial optimization problems by approximating the solution.

For a given problem represented as a cost Hamiltonian we formulate two unitary operators. The QAOA solves the problem by iteratively applying the two unitary operators on the cost Hamiltonian for a number of steps p. The angles for the unitary operators are iteratively updated by measuring the state after applying these (like in VQE).

Usage via LunaSolve

# Example of using QAOA using the IQM backend in LunaSolve
solution = ls.solution.create(
    optimization_id=optimization.id,
    solver_name="QAOA",
    provider="aws",
    solver_parameters={
        "aws_provider": "IQM",
        "aws_device": "Garnet",
        "seed": 385920,
        "reps": 1,
        "initial_values": None,
        "shots": 1024,
        "optimizer_params": {
            "method": "COBYLA",
            "bounds": None,
            "tol": None,
            "options": {'maxiter': 20},
        },
    },
    qpu_tokens=TokenProvider(
        aws_access_key=QpuToken(
            source="inline",
            token="<aws_access_key token>",
        ),
        aws_secret_access_key=QpuToken(
            source="inline",
            token="<aws_secret_access_key token>",
        ),
    ),
)

Usage via LunaBench

# Example of adding QAOA using the IQM backend as an algorithm to LunaBench
algorithms = {
    "QAOA": {
        "location": "cloud",
        "provider": "aws",
        # Provide your token (see user guide on QPU tokens for more details)
        "qpu_tokens": {
            "aws_access_key": {
                "source": "inline",
                "token": "<aws_access_key token>",
            },
            "aws_secret_access_key": {
                "source": "inline",
                "token": "<aws_secret_access_key token>",
            },
        },
    },
}

Backends

This algorithm can be run on the following backends:

IQM

Quantum Approximate Optimization Algorithm available on IQM

IQM, based in Finland, is a leading developer of superconducting quantum computers. Their quantum systems are built using a scalable, high-performance architecture designed to meet the needs of both research and enterprise. IQM’s focus on superconducting qubit technology ensures reliability and precision, making their hardware suitable for a range of quantum applications, including optimization, simulation, and quantum machine learning. With a strong emphasis on innovation and growth, IQM is driving the advancement of quantum computing technologies across Europe and globally.

IonQ

Quantum Approximate Optimization Algorithm available on IonQ

IonQ, based in Maryland, USA, is a leader in trapped-ion quantum computing. Their systems use individual atomic ions as qubits, known for their exceptional coherence times and precision control. IonQ’s fully connected qubit architecture enables highly efficient circuit designs and quantum operations, setting them apart with their ability to execute complex algorithms with minimal error. This approach positions IonQ as a top choice for researchers and businesses that require high-fidelity and flexible quantum computation.

Rigetti

Quantum Approximate Optimization Algorithm available on Rigetti

Rigetti Computing, based in Berkeley, California, USA, is recognized for its innovations in superconducting quantum processors, designed with a unique gate-based architecture. Rigetti’s hardware features a multi-chip processor setup that enhances scalability and interconnectivity, enabling more complex quantum operations. With a focus on rapid deployment and continuous advancements in superconducting technology, Rigetti stands out in providing high-performance quantum systems for tackling industrial-scale computational challenges.

QuEra

Quantum Approximate Optimization Algorithm available on QuEra

QuEra Computing, headquartered in Boston, USA, specializes in neutral-atom quantum computing, utilizing arrays of individually controlled atoms as qubits. Their unique architecture leverages Rydberg states for programmable interactions, allowing for versatile algorithm implementations in fields like optimization, simulation, and quantum machine learning. QuEra’s systems are designed with scalability in mind, promising a clear path toward increasingly powerful quantum processors.

IBM

Quantum Approximate Optimization Algorithm temporarily unavailable on IBM

IBM, headquartered in New York, USA, is a global leader in quantum computing, offering robust and scalable gate-based quantum hardware utilizing superconducting qubit technology. Known for their innovation, IBM’s quantum systems support a broad range of applications and feature one of the most mature quantum ecosystems in the industry. Their strong integration with classical computing resources and their continuous advancements in quantum research make IBM a trusted choice for enterprises and researchers exploring scalable quantum solutions worldwide.

Read more about IBM Quantum here.

IBM Simulator

Quantum Approximate Optimization Algorithm available on IBM Simulator

Read more about IBM Quantum here.

Fire Opal Quantum Approximate Optimization Algorithm

Short Name: QAOA_FO
Algorithm Type: Hybrid
Category: Hybrid-External
Native Input Format: QUBO

QAOA is a popular algorithm that can be applied to a wide range of optimization problems that are out of reach today like portfolio optimization, efficient logistics routing, and asset liability management.

This version of QAOA is from Q-CTRL’s software framework “Fire Opal”. When running QAOA via Fire Opal, all aspects of running QAOA on real hardware are fully optimized to reduce errors and improve the quality of solutions. By tailoring all parts of the algorithm to be hardware-optimized, Fire Opal enables larger problems to converge on the correct solution and do so in fewer iterations, reducing the required execution time.

Using Luna, it is possible to run QAOA using Fire Opal on IBM’s hardware.

For further information, see Q-CTRL’s documentation.

Usage via LunaSolve

# Example of using QAOA_FO using the IBM backend in LunaSolve
solution = ls.solution.create(
    optimization_id=optimization.id,
    solver_name="QAOA_FO",
    provider="qctrl",
    solver_parameters={
        "organization_slug": None,
        "backend_name": "ibm_kyiv",
        "hub": "ibm-q",
        "group": "open",
        "project": "main",
    },
    qpu_tokens=TokenProvider(
        qctrl=QpuToken(
            source="inline",
            token="<qctrl token>",
        ),
        ibm=QpuToken(
            source="inline",
            token="<ibm token>",
        ),
    ),
)

Usage via LunaBench

# Example of adding QAOA_FO using the IBM backend as an algorithm to LunaBench
algorithms = {
    "QAOA_FO": {
        "location": "cloud",
        "provider": "qctrl",
        # Provide your token (see user guide on QPU tokens for more details)
        "qpu_tokens": {
            "qctrl": {
                "source": "inline",
                "token": "<qctrl token>",
            },
            "ibm": {
                "source": "inline",
                "token": "<ibm token>",
            },
        },
    },
}

Backends

This algorithm can be run on the following backends:

IBM

Fire Opal Quantum Approximate Optimization Algorithm available on IBM

Read more about IBM Quantum here.

Variational Quantum Eigensolver

Short Name: VQE
Algorithm Type: Hybrid
Category: Hybrid-Internal
Native Input Format: QUBO

The Variational Quantum Eigensolver (VQE) solves combinatorial optimization problems by approximating the solution.

For a given problem represented as a cost Hamiltonian we apply a classical/quantum hybrid algorithm to find the solution. The VQE solves the problem by iteratively applying a linear transformation (variational form) on the cost Hamiltonian and optimizing the parameters of the transformation using a classical optimizer.

For further information see qiskit’s VQE tutorial.

Usage via LunaSolve

# Example of using VQE using the IBM backend in LunaSolve
solution = ls.solution.create(
    optimization_id=optimization.id,
    solver_name="VQE",
    provider="ibm",
    solver_parameters={
        "backend": {
            "backend_type": "ibm_backend",
            "backend_name": "ibm_kyiv",
        },
        "shots": 1024,
        "dynamical_decoupling": {},
        "optimizer": "COBYLA",
        "maxiter": 1,
        "optimization_level": 2,
        "service_config": {
            "channel": "ibm_quantum",
            "url": None,
            "name": None,
            "instance": None,
            "proxies": None,
            "verify": None,
            "channel_strategy": None,
        },
        "ansatz": "EfficientSU2",
        "ansatz_config": {},
    },
    qpu_tokens=TokenProvider(
        ibm=QpuToken(
            source="inline",
            token="<ibm token>",
        ),
    ),
)

Usage via LunaBench

# Example of adding VQE using the IBM backend as an algorithm to LunaBench
algorithms = {
    "VQE": {
        "location": "cloud",
        "provider": "ibm",
        # Provide your token (see user guide on QPU tokens for more details)
        "qpu_tokens": {
            "ibm": {
                "source": "inline",
                "token": "<ibm token>",
            },
        },
    },
}

Backends

This algorithm can be run on the following backends:

IBM

Variational Quantum Eigensolver temporarily unavailable on IBM

Read more about IBM Quantum here.

IBM Simulator

Variational Quantum Eigensolver available on IBM Simulator

Read more about IBM Quantum here.