CUDA-Q Hardware Backends¶

CUDA-Q supports submission to a set of hardware providers. To submit to a hardware backend, you need an account with the respective provider.

IonQ¶

Setting Credentials¶

Programmers of CUDA-Q may access the IonQ Quantum Cloud from either C++ or Python. Generate an API key from your IonQ account and export it as an environment variable:

export IONQ_API_KEY="ionq_generated_api_key"

Submission from C++¶

To target quantum kernel code for execution in the IonQ Cloud, pass the flag --target ionq to the nvq++ compiler.

nvq++ --target ionq src.cpp

This will take the API key and handle all authentication with, and submission to, the IonQ QPU(s). By default, quantum kernel code will be submitted to the IonQ simulator.

Note

A “target” in cudaq refers to a quantum compute provider, such as ionq. However, IonQ’s documentation uses the term “target” to refer to specific QPU’s themselves.

To execute your kernels on a QPU, pass the --ionq-machine flag to the nvq++ compiler to specify which machine to submit quantum kernels to:

nvq++ --target ionq --ionq-machine qpu.aria-1 src.cpp ...

where qpu.aria-1 is an example of a physical QPU.

A list of available QPUs can be found in the API documentation. To see which backends are available with your subscription login to your IonQ account.

To emulate the IonQ machine locally, without submitting through the cloud, you can also pass the --emulate flag to nvq++. This will emit any target specific compiler diagnostics, before running a noise free emulation.

nvq++ --emulate --target ionq src.cpp

To see a complete example for using IonQ’s backends, take a look at our C++ examples.

Submission from Python¶

The target to which quantum kernels are submitted can be controlled with the cudaq::set_target() function.

cudaq.set_target('ionq')

By default, quantum kernel code will be submitted to the IonQ simulator.

Note

A “target” in cudaq refers to a quantum compute provider, such as ionq. However, IonQ’s documentation uses the term “target” to refer to specific QPU’s themselves.

To specify which IonQ QPU to use, set the qpu parameter.

cudaq.set_target("ionq", qpu="qpu.aria-1")

where qpu.aria-1 is an example of a physical QPU.

A list of available QPUs can be found in the API documentation. To see which backends are available with your subscription login to your IonQ account.

To emulate the IonQ machine locally, without submitting through the cloud, you can also set the emulate flag to True. This will emit any target specific compiler diagnostics, before running a noise free emulation.

cudaq.set_target('ionq', emulate=True)

The number of shots for a kernel execution can be set through the shots_count argument to cudaq.sample or cudaq.observe. By default, the shots_count is set to 1000.

cudaq.sample(kernel, shots_count=10000)

To see a complete example for using IonQ’s backends, take a look at our Python examples.

IQM¶

Support for submissions to IQM is currently under development. In particular, two-qubit gates can only be performed on adjacent qubits. For more information, we refer to the respective hardware documentation. Support for automatically injecting the necessary operations during compilation to execute arbitrary multi-qubit gates will be added in future versions.

Setting Credentials¶

Programmers of CUDA-Q may access the IQM Server from either C++ or Python. Following the quick start guide, install iqm-cortex-cli and login to initialize the tokens file. The path to the tokens file can either be passed explicitly via an environment variable or it will be loaded automatically if located in the default location ~/.cache/iqm-cortex-cli/tokens.json.

export IQM_TOKENS_FILE="path/to/tokens.json"

Submission from C++¶

To target quantum kernel code for execution on an IQM Server, pass the --target iqm flag to the nvq++ compiler, along with a specified --iqm-machine.

Note

The --iqm-machine is a mandatory argument. This provided architecture must match the device architecture that the program has been compiled against. The hardware architecture for a specific IQM Server may be checked via https://<IQM server>/cocos/quantum-architecture.

nvq++ --target iqm --iqm-machine Adonis src.cpp

Once the binary for a specific IQM QPU architecture is compiled, it can be executed against any IQM Server with the same QPU architecture:

nvq++ --target iqm --iqm-machine Adonis src.cpp -o program
IQM_SERVER_URL="https://demo.qc.iqm.fi/cocos" ./program

# Executing the same program against an IQM Server with a different underlying QPU
# architecture will result in an error.
IQM_SERVER_URL="https://<Apollo IQM Server>/cocos" ./program

To emulate the IQM machine locally, without submitting to the IQM Server, you can also pass the --emulate flag to nvq++. This will emit any target specific compiler diagnostics, before running a noise free emulation.

nvq++ --emulate --target iqm --iqm-machine Adonis src.cpp

To see a complete example for using IQM server backends, take a look at our C++ examples.

Submission from Python¶

The target to which quantum kernels are submitted can be controlled with the cudaq::set_target() function.

cudaq.set_target("iqm", url="https://<IQM Server>/cocos", **{"qpu-architecture": "Adonis"})

To emulate the IQM Server locally, without submitting to the IQM Server, you can also set the emulate flag to True. This will emit any target specific compiler diagnostics, before running a noise free emulation.

cudaq.set_target('iqm', emulate=True)

The number of shots for a kernel execution can be set through the shots_count argument to cudaq.sample or cudaq.observe. By default, the shots_count is set to 1000.

cudaq.sample(kernel, shots_count=10000)

To see a complete example for using IQM server backends, take a look at our Python examples.

OQC¶

Oxford Quantum Circuits (OQC) is currently providing CUDA-Q integration for multiple Quantum Processing Unit types. The 8 qubit ring topology Lucy device and the 32 qubit Kagome lattice topology Toshiko device are both supported via machine options described below.

Setting Credentials¶

In order to use the OQC devices you will need to register. Registration is achieved by contacting oqc_qcaas_support@oxfordquantumcircuits.com

Once registered you will be able to authenticate with your email and password

There are three environment variables that the OQC target will look for during configuration:

OQC_URL
OQC_EMAIL
OQC_PASSWORD - is mandatory

Submission from C++¶

To target quantum kernel code for execution on the OQC platform, provide the flag --target oqc to the nvq++ compiler.

Users may provide their email and url as extra arguments

nvq++ --target oqc --oqc-email <email> --oqc-url <url> src.cpp -o executable

Where both environment variables and extra arguments are supplied, precedent is given to the extra arguments. To run the output, provide the runtime loaded variables and invoke the pre-built executable

OQC_PASSWORD=<password> ./executable

To emulate the OQC device locally, without submitting through the OQC QCaaS services, you can pass the --emulate flag to nvq++. This will emit any target specific compiler warnings and diagnostics, before running a noise free emulation.

nvq++ --emulate --target oqc src.cpp -o executable

Note

The oqc target supports a --oqc-machine option. The default is the 8 qubit Lucy device. You can set this to be either toshiko or lucy via this flag.

Note

The OQC quantum assembly toolchain (qat) which is used to compile and execute instructions can be found on github as oqc-community/qat

Submission from Python¶

To set which OQC URL, set the url parameter. To set which OQC email, set the email parameter. To set which OQC machine, set the machine parameter.

import os
import cudaq
# ...
os.environ['OQC_PASSWORD'] = password
cudaq.set_target("oqc", url=url, machine="lucy")

You can then execute a kernel against the platform using the OQC Lucy device

kernel = cudaq.make_kernel()
qvec = kernel.qalloc(2)
kernel.h(qvec[0])
kernel.x(qvec[1])
kernel.cx(qvec[0], qvec[1])
kernel.mz(qvec)
str(cudaq.sample(kernel=kernel, shots_count=1000))

ORCA Computing¶

ORCA Computing’s PT Series implement the boson sampling model of quantum computation, in which multiple single photons are interfered with each other within a network of beam splitters, and photon detectors measure where the photons leave this network. This process is implemented within a time-bin interferometer (TBI) architecture where photons are created in different time-bins and interfered within a network of delay lines. This can be represented by a circuit diagram, like the one below, where this illustration example corresponds to 4 photons in 8 modes sent into alternating time-bins in a circuit composed of two delay lines in series.

Setting Credentials¶

Programmers of CUDA-Q may access the ORCA API from either C++ or Python. There is an environment variable ORCA_ACCESS_URL that can be set so that the ORCA target can look for it during configuration.

export ORCA_ACCESS_URL="https://<ORCA API Server>"

Submission from C++¶

To execute a boson sampling experiment on the ORCA platform, provide the flag --target orca to the nvq++ compiler. You should then pass the --orca-url flag set with the previously set environment variable $ORCA_ACCESS_URL or an url.

nvq++ --target orca --orca-url $ORCA_ACCESS_URL src.cpp -o executable

or

nvq++ --target orca --orca-url <url> src.cpp -o executable

To run the output, invoke the executable

./executable

To see a complete example for using ORCA server backends, take a look at our C++ examples.

Submission from Python¶

To set which ORCA URL to be used, set the url parameter.

import os
import cudaq
# ...
orca_url = os.getenv("ORCA_ACCESS_URL", "http://localhost/sample")

cudaq.set_target("orca", url=orca_url)

You can then execute a time-bin boson sampling experiment against the platform using an ORCA device.

bs_angles = [np.pi / 3, np.pi / 6]
input_state = [1, 1, 1]
loop_lengths = [1]
counts = cudaq.orca.sample(input_state, loop_lengths, bs_angles)

To see a complete example for using ORCA’s backends, take a look at our Python examples.

Quantinuum¶

Setting Credentials¶

Programmers of CUDA-Q may access the Quantinuum API from either C++ or Python. Quantinuum requires a credential configuration file. The configuration file can be generated as follows, replacing the email and credentials in the first line with your Quantinuum account details.

# You may need to run: `apt-get update && apt-get install curl jq`
curl -X POST -H "Content Type: application/json" \
    -d '{ "email":"<your_alias>@email.com","password":"<your_password>" }' \
    https://qapi.quantinuum.com/v1/login > $HOME/credentials.json
id_token=`cat $HOME/credentials.json | jq -r '."id-token"'`
refresh_token=`cat $HOME/credentials.json | jq -r '."refresh-token"'`
echo "key: $id_token" >> $HOME/.quantinuum_config
echo "refresh: $refresh_token" >> $HOME/.quantinuum_config

The path to the configuration can be specified as an environment variable:

export CUDAQ_QUANTINUUM_CREDENTIALS=$HOME/.quantinuum_config

Submission from C++¶

To target quantum kernel code for execution in the Quantinuum backends, pass the flag --target quantinuum to the nvq++ compiler. CUDA-Q will authenticate via the Quantinuum REST API using the credential in your configuration file. By default, quantum kernel code will be submitted to the Quantinuum syntax checker. Submission to the syntax checker merely validates the program; the kernels are not executed.

nvq++ --target quantinuum src.cpp ...

To execute your kernels, pass the --quantinuum-machine flag to the nvq++ compiler to specify which machine to submit quantum kernels to:

nvq++ --target quantinuum --quantinuum-machine H1-2 src.cpp ...

where H1-2 is an example of a physical QPU. Hardware specific emulators may be accessed by appending an E to the end (e.g, H1-2E). For access to the syntax checker for the provided machine, you may append an SC to the end (e.g, H1-1SC).

For a comprehensive list of available machines, login to your Quantinuum user account and navigate to the “Account” tab, where you should find a table titled “Machines”.

To emulate the Quantinuum machine locally, without submitting through the cloud, you can also pass the --emulate flag to nvq++. This will emit any target specific compiler warnings and diagnostics, before running a noise free emulation.

nvq++ --emulate --target quantinuum src.cpp

To see a complete example for using Quantinuum’s backends, take a look at our C++ examples.

Submission from Python¶

The target to which quantum kernels are submitted can be controlled with the cudaq::set_target() function.

cudaq.set_target('quantinuum')

By default, quantum kernel code will be submitted to the Quantinuum syntax checker. Submission to the syntax checker merely validates the program; the kernels are not executed.

To execute your kernels, specify which machine to submit quantum kernels to by setting the machine parameter of the target.

cudaq.set_target('quantinuum', machine='H1-2')

where H1-2 is an example of a physical QPU. Hardware specific emulators may be accessed by appending an E to the end (e.g, H1-2E). For access to the syntax checker for the provided machine, you may append an SC to the end (e.g, H1-1SC).

For a comprehensive list of available machines, login to your Quantinuum user account and navigate to the “Account” tab, where you should find a table titled “Machines”.

To emulate the Quantinuum machine locally, without submitting through the cloud, you can also set the emulate flag to True. This will emit any target specific compiler warnings and diagnostics, before running a noise free emulation.

cudaq.set_target('quantinuum', emulate=True)

The number of shots for a kernel execution can be set through the shots_count argument to cudaq.sample or cudaq.observe. By default, the shots_count is set to 1000.

cudaq.sample(kernel, shots_count=10000)

To see a complete example for using Quantinuum’s backends, take a look at our Python examples.