Optimizing Variational Circuits for Higher-Order Binary Optimization

TL;DR

This paper introduces new methods for encoding higher-order binary optimization problems into quantum circuits with only two-qubit gates, reducing circuit depth and improving efficiency for near-term quantum computers.

Contribution

It formulates the circuit design as a combinatorial optimization problem and proposes heuristics that solve it in polynomial time, achieving lower circuit depth than existing methods.

Findings

Significant reduction in circuit depth compared to state-of-the-art methods

Effective heuristics for polynomial-time circuit design solutions

Demonstrated advantages in encoding higher-order problems efficiently

Abstract

Variational quantum algorithms have been advocated as promising candidates to solve combinatorial optimization problems on near-term quantum computers. Their methodology involves transforming the optimization problem into a quadratic unconstrained binary optimization (QUBO) problem. While this transformation offers flexibility and a ready-to-implement circuit involving only two-qubit gates, it has been shown to be less than optimal in the number of employed qubits and circuit depth, especially for polynomial optimization. On the other hand, strategies based on higher-order binary optimization (HOBO) could save qubits, but they would introduce additional circuit layers, given the presence of higher-than-two-qubit gates. In this paper, we study HOBO problems and propose new approaches to encode their Hamiltonian into a ready-to-implement circuit involving only two-qubit gates. Our methodology relies on formulating the circuit design as a combinatorial optimization problem, in which we seek to minimize circuit depth. We also propose handy simplifications and heuristics that can solve the circuit design problem in polynomial time. We evaluate our approaches by comparing them with the state of the art, showcasing clear gains in terms of circuit depth.