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arXiv:2202.02671 · Quantum

Stable factorization for phase factors of quantum signal processing

Lexing Ying B

Published Oct 20, 2022• 26 citations
arXivHTMLPDF

Abstract

This paper proposes a new factorization algorithm for computing the phase factors of quantum signal processing. The proposed algorithm avoids root finding of high degree polynomials by using a key step of Prony's method and is numerically stable in the double precision arithmetics. Experimental results are reported for Hamiltonian simulation, eigenstate filtering, matrix inversion, and Fermi-Dirac operator.

DOI
10.22331/q-2022-10-20-842

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This paper proposes a new factorization algorithm for computing the phase factors of quantum signal processing. The proposed algorithm avoids root finding of high degree polynomials by using a key step of Prony's method and is numerically stable in the double precision arithmetics. Experimental results are reported for Hamiltonian simulation, eigenstate filtering, matrix inversion, and Fermi-Dirac operator.
Quantum signal processing (QSP) requires the efficient computation of phase factors associated with a polynomial transformation. One existing stable factorization method avoids direct root finding but relies on Prony's method to recover a polynomial from weighted power sums of its roots (arXiv:2202.02671). We present a modification of this approach that eliminates Prony's method by using the logarithmic derivative of an associated polynomial. Because the logarithmic derivative has the same residue at every root after accounting for multiplicity, its Fourier coefficients directly yield the unweighted power sums of the roots inside the unit disk. The Newton--Girard identities then recover the coefficients of the corresponding polynomial without computing its roots. The resulting algorithm has O(dlog⁡2d)O(d\log^2 d)O(dlog2d) computational complexity and simplifies the factorization procedure of the original method. In numerical experiments on Hamiltonian simulation, eigenstate filtering, matrix inversion, and the Fermi--Dirac operator, our implementation computes phase factors in seconds with maximum errors on the order of 10−1210^{-12}10−12.
Quantum signal processing (QSP) is a powerful quantum algorithm to exactly implement matrix polynomials on quantum computers. Asymptotic analysis of quantum algorithms based on QSP has shown that asymptotically optimal results can in principle be obtained for a range of tasks, such as Hamiltonian simulation and the quantum linear system problem. A further benefit of QSP is that it uses a minimal number of ancilla qubits, which facilitates its implementation on near-to-intermediate term quantum architectures. However, there is so far no classically stable algorithm allowing computation of the phase factors that are needed to build QSP circuits. Existing methods require the use of variable precision arithmetic and can only be applied to polynomials of a relatively low degree. We present here an optimization-based method that can accurately compute the phase factors using standard double precision arithmetic operations. We demonstrate the performance of this approach with applications to Hamiltonian simulation, eigenvalue filtering, and quantum linear system problems. Our numerical results show that the optimization algorithm can find phase factors to accurately approximate polynomials of a degree larger than 1000010\phantom{\rule{0.16em}{0ex}}00010000 with errors below ${10}^{\ensuremath{-}12}$.
Quantum signal processing (QSP) represents a real scalar polynomial of degree d using a product of unitary matrices of size 2 × 2 , parameterized by ( d + 1 ) real numbers called the phase factors. This innovative representation of polynomials has a wide range of applications in quantum computation. When the polynomial of interest is obtained by truncating an infinite polynomial series, a natural question is whether the phase factors have a well defined limit as the degree d → ∞ . While the phase factors are generally not unique, we find that there exists a consistent choice of parameterization so that the limit is well defined in the ℓ 1 space. This generalization of QSP, called the infinite quantum signal processing, can be used to represent a large class of non-polynomial functions. Our analysis reveals a surprising connection between the regularity of the target function and the decay properties of the phase factors. Our analysis also inspires a very simple and efficient algorithm to approximately compute the phase factors in the ℓ 1 space. The algorithm uses only double precision arithmetic operations, and provably converges when the ℓ 1 norm of the Chebyshev coefficients of the target function is upper bounded by a constant that is independent of d . This is also the first numerically stable algorithm for finding phase factors with provable performance guarantees in the limit d → ∞ .
Quantum signal processing (QSP) provides a representation of scalar polynomials of degree ddd as products of matrices in SU(2)\mathrm{SU}(2)SU(2), parameterized by (d+1)(d+1)(d+1) real numbers known as phase factors. QSP is the mathematical foundation of quantum singular value transformation (QSVT), which is often regarded as one of the most important quantum algorithms of the past decade, with a wide range of applications in scientific computing, from Hamiltonian simulation to solving linear systems of equations and eigenvalue problems. In this article we survey recent advances in the mathematical and numerical analysis of QSP. In particular, we focus on its generalization beyond polynomials, the computational complexity of algorithms for phase factor evaluation, and the numerical stability of such algorithms. The resolution to some of these problems relies on an unexpected interplay between QSP, nonlinear Fourier analysis on SU(2)\mathrm{SU}(2)SU(2), fast polynomial multiplications, and Gaussian elimination for matrices with displacement structure.
Quantum Signal Processing (QSP), together with the quantum singular value transformation, is one of the central quantum algorithms due to its efficiency and generality in many fields including quantum simulation, quantum machine learning, and quantum cryptography. The largest bottleneck of QSP and its family is its difficulty in finding the phase angle sequence for signal processing. We find that this is in particular prominent when one employs the generalized formalism of the QSP, or the GQSP, to employ arbitrary single-qubit unitaries for signal processing operator. In this work, we extend the framework of GQSP and propose a robust angle finding algorithm. The proposed angle finding algorithm, based on Prony's method, successfully generates angle sequence of precision 10−1310^{-13}10−13 up to polynomial degrees of hundreds within a second. By applying our method to Hamiltonian simulation, we find that the number of calls, or queries, to signal operators are essentially halved compared to the ordinary framework of QSP.
Quantum signal processing (QSP) offers a framework for implementing polynomial transformations of operators that are block-encoded within unitary matrices. It has been widely adopted as a grand unification framework for quantum algorithms. In this work, we demonstrate that the coefficients of the induced polynomials P(x) and Q(x) of a QSP sequence in the Chebyshev basis can be uniquely determined by the coefficients of a Laurent polynomial. Based on this correspondence, we develop an efficient method for computing QSP matrix multiplications by evaluating this Laurent polynomial to obtain the QSP sequence. Finally, by applying this framework to the fixed-point iterative method for phase factor recovery, we achieve highly efficient computation of phase factors.