Logarithmic-depth quantum state preparation of polynomials

TL;DR

This paper presents a resource-efficient quantum state preparation method for polynomial amplitudes, achieving logarithmic circuit depth and minimal ancilla use, significantly advancing quantum algorithm scalability.

Contribution

Introduces a novel logarithmic-depth circuit construction for preparing polynomial-amplitude quantum states using block-encoding and generalized quantum eigenvalue transformation.

Findings

Achieves logarithmic circuit depth in the number of qubits.

Uses only O(n) ancilla qubits, improving resource efficiency.

Demonstrates practical implementation on a 14-qubit trapped-ion processor.

Abstract

Quantum state preparation is a central primitive in many quantum algorithms, yet it is generally resource intensive, with efficient constructions known only for structured families of states. This work introduces a method for preparing quantum states whose amplitudes are given by a degree$-d$ polynomial, using circuits with logarithmic depth in the number $n$ of qubits and only $\mathcal O(n)$ ancilla qubits, improving previous approaches that required linear-depth circuits. The construction first relies on a block-encoding of an affine diagonal operator based on its Pauli-basis decomposition, which involves only $n$ terms. A modified linear-combination-of-unitaries (LCU) technique is introduced to implement this decomposition in logarithmic depth, together with a novel circuit for the EXACT-one oracle that flags basis states in which exactly one qubit is in the state $|1\rangle$. It then uses a generalized quantum eigenvalue transformation (GQET) to promote this affine operator to an arbitrary degree polynomial. Theoretical analysis and numerical simulations are reported along with a proof-of-principle implementation on a trapped-ion quantum processor using $14$ qubits and more than $500$ primitive quantum gates. Because polynomial approximations are ubiquitous in scientific computing, this construction provides a scalable and resource-efficient approach to quantum state preparation, further improving the potential of quantum algorithms in fields such as chemistry, physics, engineering, and finance.