CUDA-Q Solvers Library

Overview

The CUDA-Q Solvers library provides high-level quantum-classical hybrid algorithms and supporting infrastructure for quantum chemistry and optimization problems. It features implementations of VQE, ADAPT-VQE, and supporting utilities for Hamiltonian generation and operator pool management.

Core Components

1. Variational Algorithms:

   • Variational Quantum Eigensolver (VQE)

   • Adaptive Derivative-Assembled Pseudo-Trotter VQE (ADAPT-VQE)

2. Quantum Chemistry Tools:

   • Molecular Hamiltonian Generation

   • One-Particle Operator Creation

   • Geometry Management

3. Operator Infrastructure:

   • Operator Pool Generation

   • Fermion-to-Qubit Mappings

   • Gradient Computation

Operator Infrastructure

Molecular Hamiltonian Options

The molecule_options structure provides extensive configuration for molecular calculations in CUDA-QX.

Option	Type	Default	Description
driver	string	“RESTPySCFDriver”	Quantum chemistry driver backend
fermion_to_spin	string	“jordan_wigner”	Fermionic to qubit operator mapping
type	string	“gas_phase”	Type of molecular system
symmetry	bool	false	Use molecular symmetry
memory	double	4000.0	Memory allocation (MB)
cycles	size_t	100	Maximum SCF cycles
initguess	string	“minao”	Initial SCF guess method
UR	bool	false	Enable unrestricted calculations
nele_cas	optional <size_t>	nullopt	Number of electrons in active space
norb_cas	optional <size_t>	nullopt	Number of spatial orbitals in in active space
MP2	bool	false	Enable MP2 calculations
natorb	bool	false	Use natural orbitals
casci	bool	false	Perform CASCI calculations
ccsd	bool	false	Perform CCSD calculations
casscf	bool	false	Perform CASSCF calculations
integrals_natorb	bool	false	Use natural orbitals for integrals
integrals_casscf	bool	false	Use CASSCF orbitals for integrals
potfile	optional <string>	nullopt	Path to external potential file
verbose	bool	false	Enable detailed output logging

Example Usage

Python

import cudaq_solvers as solvers

# Configure molecular options
options = {
    'fermion_to_spin': 'jordan_wigner',
    'casci': True,
    'memory': 8000.0,
    'verbose': True
}

# Create molecular Hamiltonian
molecule = solvers.create_molecule(
    geometry=[('H', (0., 0., 0.)),
            ('H', (0., 0., 0.7474))],
    basis='sto-3g',
    spin=0,
    charge=0,
    **options
)

C++

using namespace cudaq::solvers;

// Configure molecular options
molecule_options options;
options.fermion_to_spin = "jordan_wigner";
options.casci = true;
options.memory = 8000.0;
options.verbose = true;

// Create molecular geometry
auto geometry = molecular_geometry({
    atom{"H", {0.0, 0.0, 0.0}},
    atom{"H", {0.0, 0.0, 0.7474}}
});

// Create molecular Hamiltonian
auto molecule = create_molecule(
    geometry,
    "sto-3g",
    0,  // spin
    0,  // charge
    options
);

Variational Quantum Eigensolver (VQE)

The VQE algorithm finds the minimum eigenvalue of a Hamiltonian using a hybrid quantum-classical approach.

VQE Examples

The VQE implementation supports multiple usage patterns with different levels of customization.

Basic Usage

Python

import cudaq
from cudaq import spin
import cudaq_solvers as solvers

# Define quantum kernel (ansatz)
@cudaq.kernel
def ansatz(theta: float):
    q = cudaq.qvector(2)
    x(q[0])
    ry(theta, q[1])
    x.ctrl(q[1], q[0])

# Define Hamiltonian
H = 5.907 - 2.1433 * spin.x(0) * spin.x(1) - \
    2.1433 * spin.y(0) * spin.y(1) + \
    0.21829 * spin.z(0) - 6.125 * spin.z(1)

# Run VQE with defaults (cobyla optimizer)
energy, parameters, data = solvers.vqe(
    lambda thetas: ansatz(thetas[0]),
    H,
    initial_parameters=[0.0],
    verbose=True
)
print(f"Ground state energy: {energy}")

C++

#include "cudaq.h"

#include "cudaq/solvers/operators.h"
#include "cudaq/solvers/vqe.h"

// Define quantum kernel
struct ansatz {
  void operator()(std::vector<double> theta) __qpu__ {
      cudaq::qvector q(2);
      x(q[0]);
      ry(theta[0], q[1]);
      x<cudaq::ctrl>(q[1], q[0]);
  }
};

// Create Hamiltonian
auto H = 5.907 - 2.1433 * x(0) * x(1) -
        2.1433 * y(0) * y(1) +
        0.21829 * z(0) - 6.125 * z(1);

// Run VQE with default optimizer
auto result = cudaq::solvers::vqe(
    ansatz{},
    H,
    {0.0},  // Initial parameters
    {{"verbose", true}}
);
printf("Ground state energy: %lf\n", result.energy);

Custom Optimization

Python

# Using L-BFGS-B optimizer with parameter-shift gradients
energy, parameters, data = solvers.vqe(
    lambda thetas: ansatz(thetas[0]),
    H,
    initial_parameters=[0.0],
    optimizer='lbfgs',
    gradient='parameter_shift',
    verbose=True
)

# Using SciPy optimizer directly
from scipy.optimize import minimize

def callback(xk):
    exp_val = cudaq.observe(ansatz, H, xk[0]).expectation()
    print(f"Energy at iteration: {exp_val}")

energy, parameters, data = solvers.vqe(
    lambda thetas: ansatz(thetas[0]),
    H,
    initial_parameters=[0.0],
    optimizer=minimize,
    callback=callback,
    method='L-BFGS-B',
    jac='3-point',
    tol=1e-4,
    options={'disp': True}
)

C++

// Using L-BFGS optimizer with central difference gradients
auto optimizer = cudaq::optim::optimizer::get("lbfgs");
auto gradient = cudaq::observe_gradient::get(
    "central_difference",
    ansatz{},
    H
);

auto result = cudaq::solvers::vqe(
    ansatz{},
    H,
    *optimizer,
    *gradient,
    {0.0},  // Initial parameters
    {{"verbose", true}}
);

Shot-based Simulation

Python

# Run VQE with finite shots
energy, parameters, data = solvers.vqe(
    lambda thetas: ansatz(thetas[0]),
    H,
    initial_parameters=[0.0],
    shots=10000,
    max_iterations=10,
    verbose=True
)

# Analyze measurement data
for iteration in data:
    counts = iteration.result.counts()
    print("\nMeasurement counts:")
    print("XX basis:", counts.get_register_counts('XX'))
    print("YY basis:", counts.get_register_counts('YY'))
    print("ZI basis:", counts.get_register_counts('ZI'))
    print("IZ basis:", counts.get_register_counts('IZ'))

C++

// Run VQE with finite shots
auto optimizer = cudaq::optim::optimizer::get("lbfgs");
auto gradient = cudaq::observe_gradient::get(
    "parameter_shift",
    ansatz{},
    H
);

auto result = cudaq::solvers::vqe(
    ansatz{},
    H,
    *optimizer,
    *gradient,
    {0.0},
    {
        {"shots", 10000},
        {"verbose", true}
    }
);

// Analyze measurement data
for (auto& iteration : result.iteration_data) {
    std::cout << "Iteration type: "
            << (iteration.type == observe_execution_type::gradient
                ? "gradient" : "function")
            << "\n";
    iteration.result.dump();
}

ADAPT-VQE

The Adaptive Derivative-Assembled Pseudo-Trotter Variational Quantum Eigensolver (ADAPT-VQE) is an advanced quantum algorithm that dynamically builds a problem-tailored ansatz based on operator gradients.

Key Features

• Dynamic ansatz construction

• Gradient-based operator selection

• Automatic termination criteria

• Support for various operator pools

• Compatible with multiple optimizers

Basic Usage

Python

import cudaq
import cudaq_solvers as solvers

# Define molecular geometry
geometry = [
    ('H', (0., 0., 0.)),
    ('H', (0., 0., 0.7474))
]

# Create molecular Hamiltonian
molecule = solvers.create_molecule(
    geometry,
    'sto-3g',
    spin=0,
    charge=0,
    casci=True
)

# Generate operator pool
operators = solvers.get_operator_pool(
    "spin_complement_gsd",
    num_orbitals=molecule.n_orbitals
)

numElectrons = molecule.n_electrons

# Define initial state preparation
@cudaq.kernel
def initial_state(q: cudaq.qview):
    for i in range(numElectrons):
        x(q[i])

# Run ADAPT-VQE
energy, parameters, operators = solvers.adapt_vqe(
    initial_state,
    molecule.hamiltonian,
    operators,
    verbose=True
)
print(f"Ground state energy: {energy}")

C++

#include "cudaq/solvers/adapt.h"
#include "cudaq/solvers/operators.h"

// compile with
// nvq++ adaptEx.cpp --enable-mlir -lcudaq-solvers
// ./a.out

int main() {
    // Define initial state preparation
    auto initial_state = [](cudaq::qvector<>& q) __qpu__ {
        for (std::size_t i = 0; i < 2; ++i)
            x(q[i]);
    };

    // Create Hamiltonian (H2 molecule example)
    cudaq::solvers::molecular_geometry geometry{{"H", {0., 0., 0.}},
                                        {"H", {0., 0., .7474}}};
    auto molecule = cudaq::solvers::create_molecule(
        geometry, "sto-3g", 0, 0, {.casci = true, .verbose = true});

    auto h = molecule.hamiltonian;

    // Generate operator pool
    auto pool = cudaq::solvers::operator_pool::get(
        "spin_complement_gsd");
    auto operators = pool->generate({
        {"num-orbitals", h.num_qubits() / 2}
    });

    // Run ADAPT-VQE
    auto [energy, parameters, selected_ops] =
        cudaq::solvers::adapt_vqe(
            initial_state,
            h,
            operators,
            {
                {"grad_norm_tolerance", 1e-3},
                {"verbose", true}
            }
        );
}

Advanced Usage

Custom Optimization Settings

Python

# Using L-BFGS-B optimizer with central difference gradients
energy, parameters, operators = solvers.adapt_vqe(
    initial_state,
    molecule.hamiltonian,
    operators,
    optimizer='lbfgs',
    gradient='central_difference',
    verbose=True
)

# Using SciPy optimizer directly
from scipy.optimize import minimize
energy, parameters, operators = solvers.adapt_vqe(
    initial_state,
    molecule.hamiltonian,
    operators,
    optimizer=minimize,
    method='L-BFGS-B',
    jac='3-point',
    tol=1e-8,
    options={'disp': True}
)

C++

// Using L-BFGS optimizer with central difference gradients
auto optimizer = cudaq::optim::optimizer::get("lbfgs");
auto [energy, parameters, operators] =
    cudaq::solvers::adapt_vqe(
        initial_state{},
        h,
        operators,
        *optimizer,
        "central_difference",
        {
            {"grad_norm_tolerance", 1e-3},
            {"verbose", true}
        }
    );

Available Operator Pools

CUDA-QX provides several pre-built operator pools for ADAPT-VQE:

• spin_complement_gsd: Spin-complemented generalized singles and doubles

• uccsd: UCCSD operators

• qaoa: QAOA mixer excitation operators

# Generate different operator pools
gsd_ops = solvers.get_operator_pool(
    "spin_complement_gsd",
    num_orbitals=molecule.n_orbitals
)

uccsd_ops = solvers.get_operator_pool(
    "uccsd",
    num_orbitals=molecule.n_orbitals,
    num_electrons=molecule.n_electrons
)

Algorithm Parameters

ADAPT-VQE supports various configuration options:

• grad_norm_tolerance: Convergence threshold for operator gradients

• max_iterations: Maximum number of ADAPT iterations

• verbose: Enable detailed output

• shots: Number of measurements for shot-based simulation

energy, parameters, operators = solvers.adapt_vqe(
    initial_state,
    hamiltonian,
    operators,
    grad_norm_tolerance=1e-3,
    max_iterations=20,
    verbose=True,
    shots=10000
)

Results Analysis

The algorithm returns three components:

1. energy: Final ground state energy

2. parameters: Optimized parameters for each selected operator

3. operators: List of selected operators in order of application

# Analyze results
print(f"Final energy: {energy}")
print("\nSelected operators and parameters:")
for param, op in zip(parameters, operators):
    print(f"θ = {param:.6f} : {op}")