Controlization Schemes Based on Orthogonal Arrays

TL;DR

This paper introduces more efficient quantum controlization schemes for unknown Hamiltonian evolutions using orthogonal arrays, improving upon previous random-sampling methods and leveraging the interaction graph's structure.

Contribution

It presents novel controlization schemes based on orthogonal arrays for unknown 2-local Hamiltonians, enhancing efficiency and scalability over existing random-sampling approaches.

Findings

Schemes outperform previous methods in efficiency.

Numerical experiments confirm performance improvements.

Can be extended to k-local Hamiltonians and structured interaction graphs.

Abstract

Realizing controlled operations is fundamental to the design and execution of quantum algorithms. In quantum simulation and learning of quantum many-body systems, an important subroutine consists of implementing a controlled Hamiltonian time-evolution. Given only black-box access to the uncontrolled evolution $e^{-iHt}$, controlizing it, i.e., implementing $\mathrm{ctrl}(e^{-iHt}) = |0\rangle\langle 0|\otimes I + |1\rangle\langle 1 |\otimes e^{-iHt}$ is non-trivial. Controlization has been recently used in quantum algorithms for transforming unknown Hamiltonian dynamics [OKTM24] leveraging a scheme introduced in Refs. [NSM15, DNSM21]. The main idea behind the scheme is to intersperse the uncontrolled evolution with suitable operations such that the overall dynamics approximates the desired controlled evolution. Although efficient, this scheme uses operations randomly sampled from an exponentially large set. In the present work, we show that more efficient controlization schemes can be constructed with the help of orthogonal arrays for unknown 2-local Hamiltonians. We conduct a detailed analysis of their performance and demonstrate the resulting improvements through numerical experiments. This construction can also be generalized to $k$-local Hamiltonians. Moreover, our controlization schemes based on orthogonal arrays can take advantage of the interaction graph's structure and be made more efficient.