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 !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
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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>
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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
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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.