rqism 0.4.1

A multi-backend quantum circuit simulator
Documentation 
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use ndarray::Array1;
use rand::Rng;
use rqism::{
    circuit::Circuit, instruction::*, simulator_traits::Simulator, state_vector::QuantumStateVector,
};

struct QNN {
    theta: Array1<f32>,
    epsilon: f32,
    iter_per_epoch: usize,
    learning_rate: f32,
}

impl QNN {
    fn new(theta: Array1<f32>, epsilon: f32, iter_per_epoch: usize, learning_rate: f32) -> Self {
        Self {
            theta,
            epsilon,
            iter_per_epoch,
            learning_rate,
        }
    }

    fn loss(y: &f32, ex: &f32) -> f32 {
        (*y - *ex).powi(2)
    }

    fn run(theta: &Array1<f32>, x: &Array1<f32>) -> f32 {
        let n = x.len() + 1;
        let mut ins = vec![];

        // feature map
        x.iter()
            .enumerate()
            .for_each(|(i, x)| ins.push(Instruction::rx(i, *x)));

        // variational
        for i in 0..n - 1 {
            ins.push(Instruction::cnot([i, i + 1]));
        }

        ins.push(Instruction::cnot([n - 2, 0]));

        for i in 0..n - 1 {
            ins.push(Instruction::ry(i, theta[i]));
        }

        // output
        // TODO: do this less stupidly when i implement measure_into
        ins.push(Instruction::Measure { indices: vec![0] });
        ins.push(Instruction::cnot([0, n - 1]));

        let circ = Circuit { ins, n };

        let sim = QuantumStateVector::new(n);

        let counts = sim.get_counts(&circ, 100);

        let one = counts
            .data
            .iter()
            .find(|(key, _)| key.to_owned().chars().last().unwrap() == '1')
            .unwrap()
            .1;

        *one as f32 / counts.shots as f32
    }

    fn grad(&self, x: &Array1<f32>, y: &f32) -> Array1<f32> {
        (0..self.theta.len())
            .map(|i| {
                let mut delta = self.theta.clone();
                delta[i] += self.epsilon;

                let p1 = Self::run(&delta, x);
                let p2 = Self::run(&self.theta, x);

                (Self::loss(&p1, y) - Self::loss(&p2, y)) / self.epsilon
            })
            .collect()
    }

    fn epoch(&mut self, data: (&[Array1<f32>], &[f32])) {
        let mut losses = vec![];

        for _ in 0..self.iter_per_epoch {
            let mut loss_iter = vec![];

            for (x, y) in data.0.iter().zip(data.1.iter()) {
                let pr = Self::run(&self.theta, x);
                loss_iter.push(Self::loss(&pr, y));
                self.theta = self.theta.clone() - self.learning_rate * self.grad(x, y);
            }

            losses.push(loss_iter.iter().sum::<f32>() / losses.len() as f32);
        }
    }

    fn accuracy(&self, xs: &[Array1<f32>], ys: &[f32]) -> f32 {
        let mut counter = 0.0;

        for (x, y) in xs.iter().zip(ys.iter()) {
            let pred = Self::run(&self.theta, x);

            if pred < 0.5 && *y == 0.0 {
                counter += 1.;
            } else if pred > 0.5 && *y == 1.0 {
                counter += 1.;
            }
        }

        counter / ys.len() as f32
    }
}

fn main() {
    let mut rng = rand::thread_rng();

    let mut nn = QNN::new(
        Array1::from_vec((0..4).map(|_| rng.gen_range(0.0..1.0)).collect()),
        0.01,
        10,
        0.05,
    );

    let xs = [
        vec![5.1, 3.5, 1.4, 0.2],
        vec![4.9, 3.0, 1.4, 0.2],
        vec![4.7, 3.2, 1.3, 0.2],
        vec![4.6, 3.1, 1.5, 0.2],
        vec![5.0, 3.6, 1.4, 0.2],
        vec![5.4, 3.9, 1.7, 0.4],
        vec![4.6, 3.4, 1.4, 0.3],
        vec![5.0, 3.4, 1.5, 0.2],
        vec![4.4, 2.9, 1.4, 0.2],
        vec![4.9, 3.1, 1.5, 0.1],
        vec![5.4, 3.7, 1.5, 0.2],
        vec![4.8, 3.4, 1.6, 0.2],
        vec![4.8, 3.0, 1.4, 0.1],
        vec![4.3, 3.0, 1.1, 0.1],
        vec![5.8, 4.0, 1.2, 0.2],
        vec![5.7, 4.4, 1.5, 0.4],
        vec![5.4, 3.9, 1.3, 0.4],
        vec![5.1, 3.5, 1.4, 0.3],
        vec![5.7, 3.8, 1.7, 0.3],
        vec![5.1, 3.8, 1.5, 0.3],
        vec![5.4, 3.4, 1.7, 0.2],
        vec![5.1, 3.7, 1.5, 0.4],
        vec![4.6, 3.6, 1.0, 0.2],
        vec![5.1, 3.3, 1.7, 0.5],
        vec![4.8, 3.4, 1.9, 0.2],
        vec![5.0, 3.0, 1.6, 0.2],
        vec![5.0, 3.4, 1.6, 0.4],
        vec![5.2, 3.5, 1.5, 0.2],
        vec![5.2, 3.4, 1.4, 0.2],
        vec![4.7, 3.2, 1.6, 0.2],
        vec![4.8, 3.1, 1.6, 0.2],
        vec![5.4, 3.4, 1.5, 0.4],
        vec![5.2, 4.1, 1.5, 0.1],
        vec![5.5, 4.2, 1.4, 0.2],
        vec![4.9, 3.1, 1.5, 0.1],
        vec![5.0, 3.2, 1.2, 0.2],
        vec![5.5, 3.5, 1.3, 0.2],
        vec![4.9, 3.1, 1.5, 0.1],
        vec![4.4, 3.0, 1.3, 0.2],
        vec![5.1, 3.4, 1.5, 0.2],
        vec![5.0, 3.5, 1.3, 0.3],
        vec![4.5, 2.3, 1.3, 0.3],
        vec![4.4, 3.2, 1.3, 0.2],
        vec![5.0, 3.5, 1.6, 0.6],
        vec![5.1, 3.8, 1.9, 0.4],
        vec![4.8, 3.0, 1.4, 0.3],
        vec![5.1, 3.8, 1.6, 0.2],
        vec![4.6, 3.2, 1.4, 0.2],
        vec![5.3, 3.7, 1.5, 0.2],
        vec![5.0, 3.3, 1.4, 0.2],
        vec![7.0, 3.2, 4.7, 1.4],
        vec![6.4, 3.2, 4.5, 1.5],
        vec![6.9, 3.1, 4.9, 1.5],
        vec![5.5, 2.3, 4.0, 1.3],
        vec![6.5, 2.8, 4.6, 1.5],
        vec![5.7, 2.8, 4.5, 1.3],
        vec![6.3, 3.3, 4.7, 1.6],
        vec![4.9, 2.4, 3.3, 1.0],
        vec![6.6, 2.9, 4.6, 1.3],
        vec![5.2, 2.7, 3.9, 1.4],
        vec![5.0, 2.0, 3.5, 1.0],
        vec![5.9, 3.0, 4.2, 1.5],
        vec![6.0, 2.2, 4.0, 1.0],
        vec![6.1, 2.9, 4.7, 1.4],
        vec![5.6, 2.9, 3.6, 1.3],
        vec![6.7, 3.1, 4.4, 1.4],
        vec![5.6, 3.0, 4.5, 1.5],
        vec![5.8, 2.7, 4.1, 1.0],
        vec![6.2, 2.2, 4.5, 1.5],
        vec![5.6, 2.5, 3.9, 1.1],
        vec![5.9, 3.2, 4.8, 1.8],
        vec![6.1, 2.8, 4.0, 1.3],
        vec![6.3, 2.5, 4.9, 1.5],
        vec![6.1, 2.8, 4.7, 1.2],
        vec![6.4, 2.9, 4.3, 1.3],
        vec![6.6, 3.0, 4.4, 1.4],
        vec![6.8, 2.8, 4.8, 1.4],
        vec![6.7, 3.0, 5.0, 1.7],
        vec![6.0, 2.9, 4.5, 1.5],
        vec![5.7, 2.6, 3.5, 1.0],
        vec![5.5, 2.4, 3.8, 1.1],
        vec![5.5, 2.4, 3.7, 1.0],
        vec![5.8, 2.7, 3.9, 1.2],
        vec![6.0, 2.7, 5.1, 1.6],
        vec![5.4, 3.0, 4.5, 1.5],
        vec![6.0, 3.4, 4.5, 1.6],
        vec![6.7, 3.1, 4.7, 1.5],
        vec![6.3, 2.3, 4.4, 1.3],
        vec![5.6, 3.0, 4.1, 1.3],
        vec![5.5, 2.5, 4.0, 1.3],
        vec![5.5, 2.6, 4.4, 1.2],
        vec![6.1, 3.0, 4.6, 1.4],
        vec![5.8, 2.6, 4.0, 1.2],
        vec![5.0, 2.3, 3.3, 1.0],
        vec![5.6, 2.7, 4.2, 1.3],
        vec![5.7, 3.0, 4.2, 1.2],
        vec![5.7, 2.9, 4.2, 1.3],
        vec![6.2, 2.9, 4.3, 1.3],
        vec![5.1, 2.5, 3.0, 1.1],
        vec![5.7, 2.8, 4.1, 1.3],
        vec![6.3, 3.3, 6.0, 2.5],
        vec![5.8, 2.7, 5.1, 1.9],
        vec![7.1, 3.0, 5.9, 2.1],
        vec![6.3, 2.9, 5.6, 1.8],
        vec![6.5, 3.0, 5.8, 2.2],
        vec![7.6, 3.0, 6.6, 2.1],
        vec![4.9, 2.5, 4.5, 1.7],
        vec![7.3, 2.9, 6.3, 1.8],
        vec![6.7, 2.5, 5.8, 1.8],
        vec![7.2, 3.6, 6.1, 2.5],
        vec![6.5, 3.2, 5.1, 2.0],
        vec![6.4, 2.7, 5.3, 1.9],
        vec![6.8, 3.0, 5.5, 2.1],
        vec![5.7, 2.5, 5.0, 2.0],
        vec![5.8, 2.8, 5.1, 2.4],
        vec![6.4, 3.2, 5.3, 2.3],
        vec![6.5, 3.0, 5.5, 1.8],
        vec![7.7, 3.8, 6.7, 2.2],
        vec![7.7, 2.6, 6.9, 2.3],
        vec![6.0, 2.2, 5.0, 1.5],
        vec![6.9, 3.2, 5.7, 2.3],
        vec![5.6, 2.8, 4.9, 2.0],
        vec![7.7, 2.8, 6.7, 2.0],
        vec![6.3, 2.7, 4.9, 1.8],
        vec![6.7, 3.3, 5.7, 2.1],
        vec![7.2, 3.2, 6.0, 1.8],
        vec![6.2, 2.8, 4.8, 1.8],
        vec![6.1, 3.0, 4.9, 1.8],
        vec![6.4, 2.8, 5.6, 2.1],
        vec![7.2, 3.0, 5.8, 1.6],
        vec![7.4, 2.8, 6.1, 1.9],
        vec![7.9, 3.8, 6.4, 2.0],
        vec![6.4, 2.8, 5.6, 2.2],
        vec![6.3, 2.8, 5.1, 1.5],
        vec![6.1, 2.6, 5.6, 1.4],
        vec![7.7, 3.0, 6.1, 2.3],
        vec![6.3, 3.4, 5.6, 2.4],
        vec![6.4, 3.1, 5.5, 1.8],
        vec![6.0, 3.0, 4.8, 1.8],
        vec![6.9, 3.1, 5.4, 2.1],
        vec![6.7, 3.1, 5.6, 2.4],
        vec![6.9, 3.1, 5.1, 2.3],
        vec![5.8, 2.7, 5.1, 1.9],
        vec![6.8, 3.2, 5.9, 2.3],
        vec![6.7, 3.3, 5.7, 2.5],
        vec![6.7, 3.0, 5.2, 2.3],
        vec![6.3, 2.5, 5.0, 1.9],
        vec![6.5, 3.0, 5.2, 2.0],
        vec![6.2, 3.4, 5.4, 2.3],
        vec![5.9, 3.0, 5.1, 1.8],
    ]
    .into_iter()
    .map(|x| Array1::from_vec(x))
    .collect::<Vec<Array1<f32>>>();

    let ys = vec![
        0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
        0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0,
        0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 1.0, 1.0,
        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0,
        1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
        2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
        2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
        2.0, 2.0, 2.0, 2.0, 2.0, 2.0,
    ];

    let (training_data, testing_data) = xs.split_at(50);
    let (training_res, testing_res) = ys.split_at(50);

    for i in 0..100 {
        println!("epoch {}", i + 1);
        nn.epoch((training_data, training_res));
    }

    println!("{}", nn.accuracy(&testing_data, &testing_res));

    //i have no idea how well this works because it takes like 10 minutes
    //to do a single epoch, but theoritically it should work
}