On the energy landscape of symmetric quantum signal processing

Abstract

Symmetric quantum signal processing provides a parameterized representation of a real polynomial, which can be translated into an efficient quantum circuit for performing a wide range of computational tasks on quantum computers. For a given polynomial $f$, the parameters (called phase factors) can be obtained by solving an optimization problem. However, the cost function is non-convex, and has a very complex energy landscape with numerous global and local minima. It is therefore surprising that the solution can be robustly obtained in practice, starting from a fixed initial guess ${\Phi }^0$ that contains no information of the input polynomial. To investigate this phenomenon, we first explicitly characterize all the global minima of the cost function. We then prove that one particular global minimum (called the maximal solution) belongs to a neighborhood of ${\Phi }^0$, on which the cost function is strongly convex under the condition ${\Vert f\Vert }_{\infty }=\mathcal{O}(d^{-1})$ with $d=\mathrm{deg}(f)$. Our result provides a partial explanation of the aforementioned success of optimization algorithms.

48 pages, 6 figures

References in corpus (2)

• A Grand Unification of Quantum Algorithms
• Stable factorization for phase factors of quantum signal processing

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