Skip to main content

Documentation Index

Fetch the complete documentation index at: https://prod-mint.classiq.io/llms.txt

Use this file to discover all available pages before exploring further.

View on GitHub

Open this notebook in GitHub to run it yourself
The Quantum Singlar Value Transformation (QSVT) [1] is an algorithmic framework, used to apply polynomial transformation on the singular values of a block encoded matrix. It has wide range of applications such as matrix inversion, amplitude amplification and hamiltonian simulation. Given a unitary UU, a list of phase angles ϕ1,ϕ2,...,ϕd+1\phi_1, \phi_2, ..., \phi_{d+1} and 2 projector-controlled-not operands CΠNOT,CΠ~NOTC_{\Pi}NOT,C_{\tilde{\Pi}}NOT , the QSVT sequence is as follows: Π~ϕd+1U∏k=1(d−1)/2(Πϕd−2kU†Π~ϕd−(2k+1)U)Πϕ1\tilde{\Pi}_{\phi_{d+1}}U \prod_{k=1}^{(d-1)/2} (\Pi_{\phi_{d-2k}} U^{\dagger}\tilde{\Pi}_{\phi_{d - (2k+1)}}U)\Pi_{\phi_{1}} for odd dd, and: ∏k=1d/2(Πϕd−(2k−1)U†Π~ϕd−2kU)Πϕ1\prod_{k=1}^{d/2} (\Pi_{\phi_{d-(2k-1)}} U^{\dagger}\tilde{\Pi}_{\phi_{d-2k}}U)\Pi_{\phi_{1}} for even dd. Each of the projector-controlled-phase unitaries Π\Pi consists of a ZZ rotation of an auxilliary qubit wrapped by the CΠNOTC_{\Pi}NOTs, NOTing the auxilliary qubit: Πϕ=(CΠNOT)e−iϕ2Z(CΠNOT)\Pi_{\phi} = (C_{\Pi}NOT) e^{-i\frac{\phi}{2}Z}(C_{\Pi}NOT) The transformation will result with a polynomial of order dd. Function: qsvt Arguments:
  • phase_seq: CArray[CReal] - a d+1d+1 sized sequence of phase angles.
  • proj_cnot_1: QCallable[QArray[QBit], QBit] - projector-controlled-not unitary that locates the encoded matrix columns within UU.
Accepts quantum variable of the size of qvar, and a qubit that is set to ∣1⟩|1\rangle when the state is in the block.
  • proj_cnot_2: QCallable[QArray[QBit], QBit] - projector-controlled-not unitary that locates the encoded matrix rows within UU.
Accepts quantum variable of the size of qvar, and a qubit that is set to ∣1⟩|1\rangle when the state is in the block.
  • u: QCallable[QArray[QBit]] - UU a block encoding unitary of a matrix AA, such that A=Π~UΠA = \tilde{\Pi}U\Pi.
  • qvar: QArray[QBit] - the quantum variable on which UU applies, which resides in the entire block encoding space.
  • aux: QBit - a zero auxilliary qubit, used for the projector-controlled-phase rotations.
Given as an input so that qsvt can be used as a building-block in a larger algorithm. 
!pip install -qq "classiq[qsp]"

​

​
Example: polynomial transformation on a (x)\sqrt(x) block encoding

The following example implements a random polynomial transformation on a given block, based on [2]. The unitary UU here is a square-root transformation: U∣x⟩n∣0⟩n+1=∣x⟩n(x∣ψ0⟩n∣0⟩+1−x∣ψ1⟩n∣1⟩)U|x\rangle_n|0\rangle_{n+1} = |x\rangle_n(\sqrt{x}|\psi_0\rangle_{n}|0\rangle + \sqrt{1-x}|\psi_1\rangle_{n}|1\rangle) where xx is a fixed-point variable in the range [0,1)[0, 1). The example samples a random odd-polynomial, calculates the necessary phase sequence, then applies the qsvt and verifies the results. There are 2 distinct projector-controlled-not unitaries - one is applying on the entire (n+1)(n+1) variable, and the second is on the 1-qubits auxilliary in the image. 
from typing import Dict, Tuple

import numpy as np
from numpy.polynomial import Polynomial

from classiq import *
from classiq.qmod.symbolic import logical_and

NUM_QUBITS = 4


@qfunc
def u_sqrt(state: QNum, ref: QNum, ind: QBit) -> None:
    hadamard_transform(ref)
    ind ^= state <= ref


@qfunc
def qsvt_sqrt_polynomial(
    qsvt_phases: list[float], state: QNum, ref: QNum, ind: QBit, qsvt_aux: QBit
) -> None:
    qsvt(
        qsvt_phases,
        lambda _aux: inplace_xor(ref == 0, _aux),
        lambda _aux: inplace_xor(ind == 0, _aux),
        lambda: u_sqrt(state, ref, ind),
        qsvt_aux,
    )


import matplotlib.pyplot as plt

from classiq.applications.qsp import qsvt_phases


def sample_random_chebyshev_polynomial(degree):
    # Generate random coefficients
    coefficients = np.random.uniform(-1, 1, degree + 1)
    # take care for parity
    coefficients[int(degree + 1) % 2 :: 2] = 0

    # Create the polynomial
    p = np.polynomial.Chebyshev(coefficients)
    # Evaluate the polynomial over the interval [-1, 1]
    x = np.linspace(-1, 1, 500)
    y = p(x)

    # Normalize the polynomial
    poly = p / (np.max(np.abs(y)) + 0.001)
    print(poly)
    return poly


def parse_qsvt_results(result) -> Tuple[np.ndarray, np.ndarray]:
    parsed_state_vector = result.parsed_state_vector

    d: Dict = {x: [] for x in range(2**NUM_QUBITS)}
    for parsed_state in parsed_state_vector:
        if (
            parsed_state["qsvt_aux"] == 0
            and parsed_state["ind"] == 0
            and np.linalg.norm(parsed_state.amplitude) > 1e-15
            and (DEGREE % 2 == 1 or parsed_state["ref"] == 0)
        ):
            d[parsed_state["state"]].append(parsed_state.amplitude)
    d = {k: np.linalg.norm(v) for k, v in d.items()}
    values = [d[i] for i in range(len(d))]

    x = np.sqrt(np.linspace(0, 1 - 1 / (2**NUM_QUBITS), 2**NUM_QUBITS))

    measured_poly_values = np.sqrt(2**NUM_QUBITS) * np.array(values)
    target_poly_values = np.abs(POLY(x))

    plt.scatter(x, measured_poly_values, label="measured", c="g")
    plt.plot(x, target_poly_values, label="target")
    plt.xlabel(r"$\sqrt{x}$")
    plt.ylabel(r"$P(\sqrt{x})$")
    plt.legend()

    return measured_poly_values, target_poly_values


DEGREE = 5

np.random.seed(1)

# choosing in purpose odd polynomial
POLY = sample_random_chebyshev_polynomial(DEGREE)
QSVT_PHASES = qsvt_phases(POLY.coef)
Output:
0.0 + 0.33582085·T₁(x) + 0.0·T₂(x) - 0.30128672·T₃(x) + 0.0·T₄(x) -
  0.62136169·T₅(x)
  


@qfunc
def main(
    state: Output[QNum[NUM_QUBITS]],
    ref: Output[QNum[NUM_QUBITS]],
    ind: Output[QBit],
    qsvt_aux: Output[QBit],
) -> None:
    allocate(state)
    allocate(ref)
    allocate(ind)
    allocate(qsvt_aux)

    hadamard_transform(state)
    qsvt_sqrt_polynomial(QSVT_PHASES, state, ref, ind, qsvt_aux)


qmod = create_model(
    main,
    constraints=Constraints(max_width=13),
    execution_preferences=ExecutionPreferences(
        num_shots=1,
        backend_preferences=ClassiqBackendPreferences(
            backend_name="simulator_statevector"
        ),
    ),
)
qprog = synthesize(qmod)


show(qprog)
Output:

Quantum program link: https://platform.classiq.io/circuit/35YpmcY6jPIzVAr5ml414uoWxOo
  


result = execute(qprog).result_value()

measured, target = parse_qsvt_results(result)
assert np.allclose(measured, target, atol=0.02)
output

​
References

[1]: András Gilyén, Yuan Su, Guang Hao Low, and Nathan Wiebe. 2019. Quantum singular value transformation and beyond: exponential improvements for quantum matrix arithmetics. In Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing (STOC 2019). Association for Computing Machinery, New York, NY, USA, 193–204 https://doi.org/10.1145/3313276.3316366. [2]: Stamatopoulos, Nikitas, and William J. Zeng. “Derivative pricing using quantum signal processing.” arXiv preprint arXiv:2307.14310 (2023).