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‎pr-2711/_sources/using/backends/hardware/neutralatom.rst.txt

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@@ -177,56 +177,108 @@ Alternatively, users can set the following environment variables directly.
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export PASQAL_PROJECT_ID=<>
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Submission from Python
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Submitting
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`````````````````````````
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.. tab:: Python
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The target to which quantum kernels are submitted
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can be controlled with the ``cudaq::set_target()`` function.
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The target to which quantum kernels are submitted
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can be controlled with the ``cudaq::set_target()`` function.
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.. code:: python
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.. code:: python
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cudaq.set_target('pasqal')
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cudaq.set_target('pasqal')
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This accepts an optional argument, ``machine``, which is used in the cloud platform to
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select the corresponding Pasqal QPU or emulator to execute on.
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See the `Pasqal cloud portal <https://portal.pasqal.cloud/>`__ for an up to date list.
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The default value is ``EMU_MPS`` which is an open-source tensor network emulator based on the
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Matrix Product State formalism running in Pasqal's cloud platform. You can see the
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documentation for the publicly accessible emulator `here <https://pasqal-io.github.io/emulators/latest/emu_mps/>`__.
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This accepts an optional argument, ``machine``, which is used in the cloud platform to
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select the corresponding Pasqal QPU or emulator to execute on.
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See the `Pasqal cloud portal <https://portal.pasqal.cloud/>`__ for an up to date list.
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The default value is ``EMU_MPS`` which is an open-source tensor network emulator based on the
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Matrix Product State formalism running in Pasqal's cloud platform. You can see the
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documentation for the publicly accessible emulator `here <https://pasqal-io.github.io/emulators/latest/emu_mps/>`__.
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To target the QPU use the FRESNEL machine name. Note that there are restrictions
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regarding the values of the pulses as well as the register layout. We invite you to
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consult our `documentation <https://docs.pasqal.com/cloud/fresnel-job>`__. Note that
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the CUDA-Q integration currently only works with `arbitrary layouts <https://docs.pasqal.com/cloud/fresnel-job/#arbitrary-layouts>`__
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which are implemented with automatic calibration for less than 30 qubits. For jobs
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larger than 30 qubits please use the `atom_sites` to define the layout, and use the
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`atom_filling` to select sites as filled or not filled in order to define the register.
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To target the QPU use the FRESNEL machine name. Note that there are restrictions
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regarding the values of the pulses as well as the register layout. We invite you to
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consult our `documentation <https://docs.pasqal.com/cloud/fresnel-job>`__. Note that
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the CUDA-Q integration currently only works with `arbitrary layouts <https://docs.pasqal.com/cloud/fresnel-job/#arbitrary-layouts>`__
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which are implemented with automatic calibration for less than 30 qubits. For jobs
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larger than 30 qubits please use the `atom_sites` to define the layout, and use the
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`atom_filling` to select sites as filled or not filled in order to define the register.
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Due to the nature of the underlying hardware, this target only supports the
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``evolve`` and ``evolve_async`` APIs.
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The `hamiltonian` must be an `Operator` of the type `RydbergHamiltonian`. The only
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other supported parameters are `schedule` (mandatory) and `shots_count` (optional).
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Due to the nature of the underlying hardware, this target only supports the
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``evolve`` and ``evolve_async`` APIs.
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The `hamiltonian` must be an `Operator` of the type `RydbergHamiltonian`. The only
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other supported parameters are `schedule` (mandatory) and `shots_count` (optional).
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For example,
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For example,
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.. code:: python
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.. code:: python
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evolution_result = evolve(RydbergHamiltonian(atom_sites=register,
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amplitude=omega,
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phase=phi,
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delta_global=delta),
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schedule=schedule)
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The number of shots for a kernel execution can be set through the ``shots_count``
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argument to ``evolve`` or ``evolve_async``. By default, the ``shots_count`` is
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set to 100.
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.. code:: python
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cudaq.evolve(RydbergHamiltonian(...), schedule=s, shots_count=1000)
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To see a complete example for using Pasqal's backend, take a look at our :doc:`Python examples <../../examples/hardware_providers>`.
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.. tab:: C++
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evolution_result = evolve(RydbergHamiltonian(atom_sites=register,
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amplitude=omega,
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phase=phi,
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delta_global=delta),
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schedule=schedule)
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To target quantum kernel code for execution on Pasqal QPU or simulators,
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pass the flag ``--target pasqal`` to the ``nvq++`` compiler.
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The number of shots for a kernel execution can be set through the ``shots_count``
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argument to ``evolve`` or ``evolve_async``. By default, the ``shots_count`` is
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set to 100.
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.. code:: bash
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nvq++ --target pasqal src.cpp
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You can also pass the flag ``--pasqal-machine`` to select the corresponding Pasqal QPU or emulator to execute on.
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See the `Pasqal cloud portal <https://portal.pasqal.cloud/>`__ for an up to date list.
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The default value is ``EMU_MPS`` which is an open-source tensor network emulator based on the
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Matrix Product State formalism running in Pasqal's cloud platform. You can see the
245+
documentation for the publicly accessible emulator `here <https://pasqal-io.github.io/emulators/latest/emu_mps/>`__.
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.. code:: bash
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nvq++ --target pasqal --pasqal-machine EMU_FREE src.cpp
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To target the QPU use the FRESNEL machine name. Note that there are restrictions
252+
regarding the values of the pulses as well as the register layout. We invite you to
253+
consult our `documentation <https://docs.pasqal.com/cloud/fresnel-job>`__. Note that
254+
the CUDA-Q integration currently only works with `arbitrary layouts <https://docs.pasqal.com/cloud/fresnel-job/#arbitrary-layouts>`__
255+
which are implemented with automatic calibration for less than 30 qubits. For jobs
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larger than 30 qubits please use the `atom_sites` to define the layout, and use the
257+
`atom_filling` to select sites as filled or not filled in order to define the register.
258+
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Due to the nature of the underlying hardware, this target only supports the
260+
``evolve`` and ``evolve_async`` APIs.
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The `hamiltonian` must be of the type `rydberg_hamiltonian`. Only
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other parameters supported are `schedule` (mandatory) and `shots_count` (optional).
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For example,
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.. code:: python
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.. code:: cpp
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cudaq.evolve(RydbergHamiltonian(...), schedule=s, shots_count=1000)
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auto evolution_result = cudaq::evolve(
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cudaq::rydberg_hamiltonian(register_sites, omega, phi, delta),
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schedule);
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The number of shots for a kernel execution can be set through the ``shots_count``
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argument to ``evolve`` or ``evolve_async``. By default, the ``shots_count`` is
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set to 100.
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.. code:: cpp
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auto evolution_result = cudaq::evolve(cudaq::rydberg_hamiltonian(...), schedule, 1000);
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To see a complete example for using Pasqal's backend, take a look at our :doc:`C++ examples <../../examples/hardware_providers>`.
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To see a complete example for using Pasqal's backend, take a look at our :doc:`Python examples <../../examples/hardware_providers>`.
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.. note::
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@@ -261,47 +313,81 @@ Alternatively, users can set the following environment variables.
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export AWS_SECRET_ACCESS_KEY="<access_key>"
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export AWS_SESSION_TOKEN="<token>"
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Submission from Python
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About Aquila
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`````````````````````````
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The target to which quantum kernels are submitted
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can be controlled with the ``cudaq::set_target()`` function.
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.. code:: python
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cudaq.set_target('quera')
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By default, analog Hamiltonian will be submitted to the Aquila system.
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Aquila is a "field programmable qubit array" operated as an analog
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Hamiltonian simulator on a user-configurable architecture, executing
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programmable coherent quantum dynamics on up to 256 neutral-atom qubits.
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Refer to QuEra's `whitepaper <https://cdn.prod.website-files.com/643b94c382e84463a9e52264/648f5bf4d19795aaf36204f7_Whitepaper%20June%2023.pdf>`__ for details.
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Due to the nature of the underlying hardware, this target only supports the
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``evolve`` and ``evolve_async`` APIs.
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The `hamiltonian` must be an `Operator` of the type `RydbergHamiltonian`. Only
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other parameters supported are `schedule` (mandatory) and `shots_count` (optional).
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Submitting
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`````````````````````````
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.. tab:: Python
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328+
The target to which quantum kernels are submitted
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can be controlled with the ``cudaq::set_target()`` function.
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.. code:: python
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For example,
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cudaq.set_target('quera')
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.. code:: python
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Due to the nature of the underlying hardware, this target only supports the
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``evolve`` and ``evolve_async`` APIs.
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The `hamiltonian` must be an `Operator` of the type `RydbergHamiltonian`. Only
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other parameters supported are `schedule` (mandatory) and `shots_count` (optional).
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340+
For example,
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.. code:: python
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evolution_result = evolve(RydbergHamiltonian(atom_sites=register,
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amplitude=omega,
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phase=phi,
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delta_global=delta),
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schedule=schedule)
349+
350+
The number of shots for a kernel execution can be set through the ``shots_count``
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argument to ``evolve`` or ``evolve_async``. By default, the ``shots_count`` is
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set to 100.
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354+
.. code:: python
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cudaq.evolve(RydbergHamiltonian(...), schedule=s, shots_count=1000)
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To see a complete example for using QuEra's backend, take a look at our :doc:`Python examples <../../examples/hardware_providers>`.
359+
360+
.. tab:: C++
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362+
To target quantum kernel code for execution on QuEra's Aquila,
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pass the flag ``--target quera`` to the ``nvq++`` compiler.
364+
365+
.. code:: bash
366+
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nvq++ --target quera src.cpp
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Due to the nature of the underlying hardware, this target only supports the
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``evolve`` and ``evolve_async`` APIs.
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The `hamiltonian` must be of the type `rydberg_hamiltonian`. Only
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other parameters supported are `schedule` (mandatory) and `shots_count` (optional).
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For example,
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.. code:: cpp
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evolution_result = evolve(RydbergHamiltonian(atom_sites=register,
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amplitude=omega,
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phase=phi,
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delta_global=delta),
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schedule=schedule)
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auto evolution_result = cudaq::evolve(
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cudaq::rydberg_hamiltonian(register_sites, omega, phi, delta),
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schedule);
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The number of shots for a kernel execution can be set through the ``shots_count``
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argument to ``evolve`` or ``evolve_async``. By default, the ``shots_count`` is
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set to 100.
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The number of shots for a kernel execution can be set through the ``shots_count``
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argument to ``evolve`` or ``evolve_async``. By default, the ``shots_count`` is
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set to 100.
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.. code:: python
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.. code:: cpp
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cudaq.evolve(RydbergHamiltonian(...), schedule=s, shots_count=1000)
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auto evolution_result = cudaq::evolve(cudaq::rydberg_hamiltonian(...), schedule, 1000);
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To see a complete example for using QuEra's backend, take a look at our :doc:`Python examples <../../examples/hardware_providers>`.
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To see a complete example for using QuEra's backend, take a look at our :doc:`C++ examples <../../examples/hardware_providers>`.
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.. note::
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‎pr-2711/_sources/using/examples/hardware_providers.rst.txt

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.. literalinclude:: ../../targets/python/pasqal.py
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:language: python
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.. tab:: C++
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.. literalinclude:: ../../targets/cpp/pasqal.cpp
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:language: cpp
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Quantinuum
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==================================
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.. literalinclude:: ../../targets/python/quera_basic.py
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:language: python
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.. tab:: C++
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.. literalinclude:: ../../targets/cpp/quera_basic.cpp
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:language: cpp
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‎pr-2711/applications/python/adapt_qaoa.html

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@@ -790,7 +790,7 @@ <h1>ADAPT-QAOA algorithm<a class="headerlink" href="#ADAPT-QAOA-algorithm" title
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parameter</p>
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<p>3- Optimize all parameters currently in the Ansatz <span class="math notranslate nohighlight">\(\beta_m, \gamma_m = 1, 2, ...k\)</span> such that <span class="math notranslate nohighlight">\(\braket{\psi (k)|H_C|\psi(k)}\)</span> is minimized, and return to the second step.</p>
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<p>Below is a schematic representation of the ADAPT-QAOA algorithm explained above.</p>
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<div><p><img alt="32aa93e6d321459abfc140fb6b19a2b9" class="no-scaled-link" src="../../_images/adapt-qaoa.png" style="width: 600px;" /></p>
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<div><p><img alt="daf11e79225f4a5c888b33a7581823e0" class="no-scaled-link" src="../../_images/adapt-qaoa.png" style="width: 600px;" /></p>
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</div><div class="nbinput nblast docutils container">
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<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
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</pre></div>

‎pr-2711/applications/python/deutschs_algorithm.html

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@@ -847,7 +847,7 @@ <h2>XOR <span class="math notranslate nohighlight">\(\oplus\)</span><a class="he
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</section>
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<section id="Quantum-oracles">
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<h2>Quantum oracles<a class="headerlink" href="#Quantum-oracles" title="Permalink to this heading"></a></h2>
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<p><img alt="38c9ad2e9740475c9db5dddb22f57702" class="no-scaled-link" src="../../_images/oracle.png" style="width: 300px; height: 150px;" /></p>
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<p><img alt="1ab4a26063104ce6ae4a69ec470464f1" class="no-scaled-link" src="../../_images/oracle.png" style="width: 300px; height: 150px;" /></p>
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<p>Suppose we have <span class="math notranslate nohighlight">\(f(x): \{0,1\} \longrightarrow \{0,1\}\)</span>. We can compute this function on a quantum computer using oracles which we treat as black box functions that yield the output with an appropriate sequence of logical gates.</p>
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<p>Above you see an oracle represented as <span class="math notranslate nohighlight">\(U_f\)</span> which allows us to transform the state <span class="math notranslate nohighlight">\(\ket{x}\ket{y}\)</span> into:</p>
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<div class="math notranslate nohighlight">
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<h2>Deutsch’s Algorithm:<a class="headerlink" href="#Deutsch's-Algorithm:" title="Permalink to this heading"></a></h2>
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<p>Our aim is to find out if <span class="math notranslate nohighlight">\(f: \{0,1\} \longrightarrow \{0,1\}\)</span> is a constant or a balanced function? If constant, <span class="math notranslate nohighlight">\(f(0) = f(1)\)</span>, and if balanced, <span class="math notranslate nohighlight">\(f(0) \neq f(1)\)</span>.</p>
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<p>We step through the circuit diagram below and follow the math after the application of each gate.</p>
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<p><img alt="d443918c3cbb4c7d8c30bece21fd7068" class="no-scaled-link" src="../../_images/deutsch.png" style="width: 500px; height: 210px;" /></p>
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<p><img alt="4f515c9a00e84296bfb9876d77822c2d" class="no-scaled-link" src="../../_images/deutsch.png" style="width: 500px; height: 210px;" /></p>
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<div class="math notranslate nohighlight">
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\[\ket{\psi_0} = \ket{01}
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\tag{1}\]</div>

‎pr-2711/applications/python/edge-detection.html

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<section id="Quantum-Probability-Image-Encoding-(QPIE):">
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<h2>Quantum Probability Image Encoding (QPIE):<a class="headerlink" href="#Quantum-Probability-Image-Encoding-(QPIE):" title="Permalink to this heading"></a></h2>
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<p>Lets take as an example a classical 2x2 image (4 pixels). We can label each pixel with its position</p>
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<div><p><img alt="20b03a8a9ec34d9ab07567824d30bbde" class="no-scaled-link" src="../../_images/pixels-img.png" style="width: 200px;" /></p>
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<div><p><img alt="e10d2297a4cf4ed7a7066469082a0b54" class="no-scaled-link" src="../../_images/pixels-img.png" style="width: 200px;" /></p>
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</div><p>Each pixel will have its own color intensity represented along with its position label as an 8-bit black and white color. To convert the pixel intensity to probability amplitudes of a quantum state</p>
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<div class="math notranslate nohighlight">
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\[c_i = \frac{I_{yx}}{\sqrt(\sum I^2_{yx})}\]</div>

‎pr-2711/applications/python/quantum_transformer.html

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‎pr-2711/examples/python/performance_optimizations.html

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@@ -775,9 +775,9 @@ <h1>Optimizing Performance<a class="headerlink" href="#Optimizing-Performance" t
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<section id="Gate-Fusion">
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<h2>Gate Fusion<a class="headerlink" href="#Gate-Fusion" title="Permalink to this heading"></a></h2>
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<p>Gate fusion is an optimization technique where consecutive gates are combined into a single gate operation to improve the efficiency of the simulation (See figure below). By targeting the <code class="docutils literal notranslate"><span class="pre">nvidia-mgpu</span></code> backend and setting the <code class="docutils literal notranslate"><span class="pre">CUDAQ_MGPU_FUSE</span></code> environment variable, you can select the degree of fusion that takes place. A full command line example would look like <code class="docutils literal notranslate"><span class="pre">CUDAQ_MGPU_FUSE=4</span> <span class="pre">python</span> <span class="pre">c2h2VQE.py</span> <span class="pre">--target</span> <span class="pre">nvidia</span> <span class="pre">--target-option</span> <span class="pre">fp64,mgpu</span></code></p>
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<p><img alt="b678221c2d5245d5a5c77800ba53701d" src="../../_images/gate-fuse.png" /></p>
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<p><img alt="26d92652f47d4940afe76934efed439c" src="../../_images/gate-fuse.png" /></p>
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<p>The importance of gate fusion is system dependent, but can have a large influence on the performance of the simulation. See the example below for a 24 qubit VQE experiment where changing the fusion level resulted in significant performance boosts.</p>
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<p><img alt="93a654c50b824d49acec568c73b79c8b" src="../../_images/gatefusion.png" /></p>
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<p><img alt="1d9205cf22224e7893f15d1880a7c5bb" src="../../_images/gatefusion.png" /></p>
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</section>
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</section>
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‎pr-2711/objects.inv

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‎pr-2711/searchindex.js

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