6. Quantum Kernels

[1] To differentiate between host and quantum device code, the CUDA-Q programming model defines the concept of a quantum kernel. A quantum kernel is any callable in C++ annotated to indicate compilation and execution on an available quantum coprocessor.

[2] All quantum kernels must be annotated to indicate they are to be compiled for, and executed on, a specified quantum coprocessor. CUDA-Q requires the __qpu__ function attribute for quantum kernel declarations. Other language bindings may opt to use other language features to enable function annotation or decoration (e.g. a @cudaq.kernel() function decorator in Python).

[3] CUDA-Q specifically differentiates between kernels invoked from host code and those invoked from within another quantum kernel. The former are denoted entry-point quantum kernels, the latter are pure device quantum kernels. Entry point kernels can only take classical types as input and return classical output. Pure-device kernels can take quantum types as input.

// Entry point lambda kernel
auto my_first_entry_point_kernel = [](double x) __qpu__ { ... quantum code ... };

// Entry point typed callable kernel
struct my_second_entry_point_kernel {
  void operator()(double x, std::vector<double> params) __qpu__ {
    ... quantum code ...
  }
};

// Pure device free function kernel
__qpu__ void my_first_pure_device_kernel(cudaq::qview<> qubits) {
   ... quantum code ...
}

@cudaq.kernel()
def my_first_entry_point_kernel(x : float):
   ... quantum code ...

@cudaq.kernel()
def my_second_entry_point_kernel(x : float, params : list[float]):
   ... quantum code ...

@cudaq.kernel()
def my_first_pure_device_kernel(qubits : cudaq.qview):
   ... quantum code ...

[4] Quantum kernel function bodies are programmed in a subset of the parent classical language. Kernels can be composed of the following:

  • Quantum intrinsic operations and measurements

  • In-scope kernel calls with or without any kernel modifiers (e.g. cudaq::control)

  • Classical control flow constructs from the classical language (if, for, while, etc.)

  • Stack variable declarations for supported types.

  • Arithmetic operations on integer and floating point stack variables

  • Coherent conditional execution - if ( boolExprFromQubitMeasurement ) { x (another_qubit); }

  • Syntax for common quantum programming patterns (e.g. compute-action-uncompute).

[5] CUDA-Q defines a set of allowed types that can be leveraged in quantum kernel function signatures (input and return types), in-function variable declaration or construction, and in variable capture from parent scope.

The allowed types are as follows:

  • T such that std::is_arithmetic_v<T> == true

  • std::vector<T> such that std::is_arithmetic_v<T> == true

  • std::span<T> such that std::is_arithmetic_v<T> == true

  • std::vector<V> such that type V is a valid std::vector<T> (possibly recursively)

  • struct types composed of any valid CUDA-Q type.

  • std::tuple<Ts...> such that T is any allowed type.

  • std::pair<T,U> such that T is any allowed type.

struct MyCustomSimpleStruct {
   int i = 0;
   int j = 0;
   std::vector<double> angles;
};

// Valid CUDA-Q Types used in Kernels
auto kernel = [](int N, bool flag, float angle, std::vector<std::size_t> layers,
         std::vector<double> parameters, std::vector<std::vector<float>> recursiveVec,
         MyCustomSimpleStruct var) __qpu__ { ... }

__qpu__ double kernelThatReturns() {
   ...
   return M_PI_2;
}

class MySimpleStruct(object):
    def __init__(self):
        self.i = 0
        self.j = 0

@cudaq.kernel
def kernel(N : int, flag : bool, angle : float, layers : list[int],
            parameters : list[float], recursiveList : list[list[float]],
            var : MySimpleStruct): ...

@cudaq.kernel
def kernelThatReturns() -> float:
    ...
    return np.pi / 2.0

[6] Any variable with an allowed CUDA-Q type can be allocated on the stack within CUDA-Q kernels. Variables of type std::vector<T> for any allowed type T can only be constructed with known size. Vector-like variables cannot be default constructed and later filled with type T data (i.e. no dynamic memory allocation).

// Valid CUDA-Q Types used in Kernels
auto kernel = []() __qpu__ {

  // Not Allowed.
  // std::vector<int> i;
  // i.push_back(1);

  // Valid variable declarations

  std::vector<int> i(5);
  i[2] = 3;

  std::vector<float> f {1.0, 2.0, 3.0};

  int k = 0;

  double pi = 3.1415926
};

@cudaq.kernel
def kernel():
   # Not Allowed
   # i = []
   # i.append(1)

   i = [0 for k in range(5)]
   i[2] = 3

   f = [1., 2., 3.]

   k = 0

   pi = 3.1415926

[7] All entry-point kernel arguments adhere to pass-by-value semantics.

auto kernel = [](int i, std::vector<double> v) __qpu__ {
   // i == 2, allocate 2 qubits
   cudaq::qvector q(i);
   // v[1] == 2.0, angle here is 2.0
   ry(v[1], q[0]);

   // Change the variables, caller does not see this
   i = 5;
   v[0] = 3.0;
};

int k = 2;
std::vector<double> d {1.0, 2.0};

kernel(k, d);

// k is still 2, pass by value
// d is still {1.0, 2.0}, pass by value

@cudaq.kernel
def kernel(i : int, v : list[float]):
    # i = 2, allocate 2 qubits
    q = cudaq.qvector(i)
    # v[1] == 2.0, angle here is 2.0
    ry(v[1], q[0])

    # Change the variables, caller does not see this
    i = 5
    v[0] = 3.0

k, d = 2, [1., 2.]
kernel(i, d)

# k is still 2, pass by value
# d is still {1.0, 2.0}, pass by value

[8] CUDA-Q kernel lambdas in C++ can capture variables of allowed type by value. CUDA-Q kernels defined as custom callable types can define non-reference type class members of any allowed type. These member variables can be set with pass-by-value semantics at kernel construction.

struct kernel {
  int i;
  float f;

  void operator()() __qpu__ {
     cudaq::qvector q(i);
     ry(f, q[0]);
  }
};

kernel{2, 2.2}();

int i = 2;
double f = 2.2;

auto kernelLambda = [=]() __qpu__ {
  // Use captured variables
  cudaq::qvector q(i);
  ry(f, q[0]);
  i = 5;
  return i;
};

auto k = kernelLambda();

// Pass by value semantics
assert(k != i);

i = 2
f = np.pi / 2.

@cudaq.kernel
def kernel() -> int:
   # Use captured variables
   q = cudaq.qvector(i)
   ry(f, q[0])

   i = 5
   return i

k = kernel()

# Pass by value semantics
assert k != i

[9] All quantum kernel invocations are synchronous calls by default.

[10] CUDA-Q kernels can serve as input to other quantum kernels and invoked by kernel function body code. To support CUDA-Q kernel parameterization on callable quantum kernel code, programmers can leverage standard C++ template definitions or dynamic typing in language bindings such as Python:

__qpu__ void MyStatePrep(cudaq::qview<> qubits) {
    ... apply state prep operations on qubits ...
}

struct MyGenericAlgorithm {
  template<typename StatePrep>
  void operator()(const StatePrep& statePrep) __qpu__ {
    cudaq::qarray<10> q;
    statePrep(q);
    ...
  }
};

// -or- with placeholder type specifiers
struct MyGenericAlgorithm2 {
  void operator()(const auto& statePrep) __qpu__ {
    cudaq::qarray<10> q;
    statePrep(q);
    ...
  }
};

void callKernels() {

  MyGenericAlgorithm algorithm;
  algorithm(MyStatePrep);

  MyGenericAlgorithm2 anotherVersion;
  anotherVersion(MyStatePrep);
}

@cudaq.kernel()
def MyStatePrep(qubits : cudaq.qview):
    ... apply state prep operations on qubits ...

@cudaq.kernel()
def MyGenericAlgorithm(statePrep : typing.Callable[[cudaq.qview], None]):
    q = cudaq.qvector(10)
    statePrep(q)
    ...

MyGenericAlgorithm(MyStatePrep)

CUDA-Q kernel inputs can also be constrained.

namespace cudaq {

  // Generic constraint on Kernel Function Signatures
  template <typename Kernel, typename Signature>
  concept signature = std::is_convertible_v<Kernel, std::function<Signature>>;

  // Specialized for taking a single qubit
  template<typename Kernel>
  concept takes_qubit = signature<Kernel, void(qubit&)>;
}

struct MyGenericAlgorithmOnQarray {
  void operator()(cudaq::signature<void(cudaq::qarray&)> auto&& statePrep) __qpu__ {
    cudaq::qarray<10> q;
    statePrep(q);
    ...
  }
};

struct MyGenericAlgorithmOnQubit {
  void operator()(cudaq::takes_qubit auto&& statePrep) __qpu__ {
    cudaq::qarray<10> q;
    statePrep(q[0]);
    ...
  }
};

This approach enables the development of generic libraries of quantum algorithms that are parameterized on sub-units of the global circuit representation.