pub struct VQEWithAutodiff {
pub ansatz: ParametricCircuit,
pub hamiltonian: PauliOperatorSum,
pub context: AutoDiffContext,
pub history: Vec<VQEIteration>,
pub convergence: ConvergenceCriteria,
}Expand description
VQE algorithm with automatic differentiation
Fields§
§ansatz: ParametricCircuitParametric ansatz circuit
hamiltonian: PauliOperatorSumHamiltonian observable
context: AutoDiffContextAutodiff context
history: Vec<VQEIteration>Optimization history
convergence: ConvergenceCriteriaConvergence criteria
Implementations§
Source§impl VQEWithAutodiff
impl VQEWithAutodiff
Sourcepub fn new(
ansatz: ParametricCircuit,
hamiltonian: PauliOperatorSum,
initial_params: Vec<f64>,
gradient_method: GradientMethod,
) -> Self
pub fn new( ansatz: ParametricCircuit, hamiltonian: PauliOperatorSum, initial_params: Vec<f64>, gradient_method: GradientMethod, ) -> Self
Create new VQE instance
Sourcepub const fn with_convergence(self, convergence: ConvergenceCriteria) -> Self
pub const fn with_convergence(self, convergence: ConvergenceCriteria) -> Self
Set convergence criteria
Sourcepub fn evaluate_energy(&mut self) -> Result<f64>
pub fn evaluate_energy(&mut self) -> Result<f64>
Evaluate energy for current parameters
Sourcepub fn compute_gradient(&mut self) -> Result<Vec<f64>>
pub fn compute_gradient(&mut self) -> Result<Vec<f64>>
Compute gradient for current parameters
Sourcepub fn step(&mut self, learning_rate: f64) -> Result<VQEIteration>
pub fn step(&mut self, learning_rate: f64) -> Result<VQEIteration>
Perform one VQE optimization step using gradient descent
Sourcepub fn optimize(&mut self, learning_rate: f64) -> Result<VQEResult>
pub fn optimize(&mut self, learning_rate: f64) -> Result<VQEResult>
Run VQE optimization until convergence
Sourcepub fn optimize_with_optirs(
&mut self,
config: OptiRSConfig,
) -> Result<VQEResult>
pub fn optimize_with_optirs( &mut self, config: OptiRSConfig, ) -> Result<VQEResult>
Run VQE optimization using OptiRS optimizers (Adam, SGD, RMSprop, etc.)
This method provides state-of-the-art optimization using OptiRS’s advanced
machine learning optimizers, which typically converge faster and more robustly
than basic gradient descent.
§Arguments
config-OptiRSoptimizer configuration
§Returns
VQEResult- Optimization result with optimal parameters and energy
§Example
ⓘ
use quantrs2_sim::autodiff_vqe::*;
use quantrs2_sim::optirs_integration::*;
let mut vqe = VQEWithAutodiff::new(...);
let config = OptiRSConfig {
optimizer_type: OptiRSOptimizerType::Adam,
learning_rate: 0.01,
..Default::default()
};
let result = vqe.optimize_with_optirs(config)?;Auto Trait Implementations§
impl Freeze for VQEWithAutodiff
impl !RefUnwindSafe for VQEWithAutodiff
impl Send for VQEWithAutodiff
impl Sync for VQEWithAutodiff
impl Unpin for VQEWithAutodiff
impl UnsafeUnpin for VQEWithAutodiff
impl !UnwindSafe for VQEWithAutodiff
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Source§impl<T> BorrowMut<T> for Twhere
T: ?Sized,
impl<T> BorrowMut<T> for Twhere
T: ?Sized,
Source§fn borrow_mut(&mut self) -> &mut T
fn borrow_mut(&mut self) -> &mut T
Mutably borrows from an owned value. Read more
Source§impl<T> IntoEither for T
impl<T> IntoEither for T
Source§fn into_either(self, into_left: bool) -> Either<Self, Self>
fn into_either(self, into_left: bool) -> Either<Self, Self>
Converts
self into a Left variant of Either<Self, Self>
if into_left is true.
Converts self into a Right variant of Either<Self, Self>
otherwise. Read moreSource§fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
fn into_either_with<F>(self, into_left: F) -> Either<Self, Self>
Converts
self into a Left variant of Either<Self, Self>
if into_left(&self) returns true.
Converts self into a Right variant of Either<Self, Self>
otherwise. Read moreSource§impl<T> Pointable for T
impl<T> Pointable for T
Source§impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SPwhere
SS: SubsetOf<SP>,
Source§fn to_subset(&self) -> Option<SS>
fn to_subset(&self) -> Option<SS>
The inverse inclusion map: attempts to construct
self from the equivalent element of its
superset. Read moreSource§fn is_in_subset(&self) -> bool
fn is_in_subset(&self) -> bool
Checks if
self is actually part of its subset T (and can be converted to it).Source§fn to_subset_unchecked(&self) -> SS
fn to_subset_unchecked(&self) -> SS
Use with care! Same as
self.to_subset but without any property checks. Always succeeds.Source§fn from_subset(element: &SS) -> SP
fn from_subset(element: &SS) -> SP
The inclusion map: converts
self to the equivalent element of its superset.