Quantum Algorithms for Fixed Qubit Architectures

TL;DR

This paper proposes a quantum algorithm tailored for fixed qubit architectures that can solve combinatorial problems like MaxCut without error correction, using parameterized unitaries guided by measurements, and demonstrates a guaranteed approximation ratio.

Contribution

The paper introduces a hardware-aware quantum algorithm for fixed qubit layouts that achieves a provable approximation ratio for MaxCut, without requiring error correction or compilation.

Findings

Achieves at least 0.5293 approximation ratio for MaxCut on large instances.

Demonstrates the algorithm's adaptability to different qubit connectivities.

Provides a framework for parameter optimization on fixed hardware layouts.

Abstract

Gate model quantum computers with too many qubits to be simulated by available classical computers are about to arrive. We present a strategy for programming these devices without error correction or compilation. This means that the number of logical qubits is the same as the number of qubits on the device. The hardware determines which pairs of qubits can be addressed by unitary operators. The goal is to build quantum states that solve computational problems such as maximizing a combinatorial objective function or minimizing a Hamiltonian. These problems may not fit naturally on the physical layout of the qubits. Our algorithms use a sequence of parameterized unitaries that sit on the qubit layout to produce quantum states depending on those parameters. Measurements of the objective function (or Hamiltonian) guide the choice of new parameters with the goal of moving the objective function up (or lowering the energy). As an example we consider finding approximate solutions to MaxCut on 3-regular graphs whereas the hardware is physical qubits laid out on a rectangular grid. We prove that the lowest depth version of the Quantum Approximate Optimization Algorithm will achieve an approximation ratio of at least 0.5293 on all large enough instances which beats random guessing (0.5). We open up the algorithm to have different parameters for each single qubit $X$ rotation and for each $ZZ$ interaction associated with the nearest neighbor interactions on the grid. Small numerical experiments indicate that an enveloping classical algorithm can be used to find the parameters which sit on the grid to optimize an objective function with a different connectivity. We discuss strategies for finding good parameters but offer no evidence yet that the proposed approach can beat the best classical algorithms. Ultimately the strength of this approach will be determined by running on actual hardware.