Qubit-Efficient Randomized Quantum Algorithms for Linear Algebra
Samson Wang, Sam McArdle, Mario Berta
Abstract
We propose a class of randomized quantum algorithms for the task of sampling from matrix functions, without the use of quantum block encodings or any other coherent oracle access to the matrix elements. As such, our use of qubits is purely algorithmic and no additional qubits are required for quantum data structures. Our algorithms start from a classical data structure in which the matrix of interest is specified in the Pauli basis. For <a:math xmlns:a="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><a:mi>N</a:mi><a:mo>×</a:mo><a:mi>N</a:mi></a:math> Hermitian matrices, the space cost is <d:math xmlns:d="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><d:mi>log</d:mi><d:mo></d:mo><d:mo stretchy="false">(</d:mo><d:mi>N</d:mi><d:mo stretchy="false">)</d:mo><d:mo>+</d:mo><d:mn>1</d:mn></d:math> qubits and, depending on the structure of the matrices, the gate complexity can be comparable to state-of-the-art methods that use quantum data structures of up to size <i:math xmlns:i="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll"><i:mi>O</i:mi><i:mo stretchy="false">(</i:mo><i:msup><i:mi>N</i:mi><i:mn>2</i:mn></i:msup><i:mo stretchy="false">)</i:mo></i:math>, when considering equivalent end-to-end problems. Within our framework, we present a quantum linear system solver that allows one to sample properties of the solution vector, as well as algorithms for sampling properties of ground states and Gibbs states of Hamiltonians. As a concrete application, we combine these subroutines to present a scheme for calculating Green’s functions of quantum many-body systems. Published by the American Physical Society 2024