Year: 2023
Type: article
Abstract: In the race to build scalable quantum computers, minimizing the resource consumption of their full stack to achieve a target performance becomes crucial.It mandates a synergy of fundamental physics and engineering: the former for the microscopic aspects of computing performance and the latter for the macroscopic resource consumption.For this, we propose a holistic methodology dubbed metric noise resource (MNR) that is able to quantify and optimize all aspects of the full-stack quantum computer, bringing together concepts from quantum physics (e.g., noise on the qubits), quantum information (e.g., computing architecture and type of error correction), and enabling technologies (e.g., cryogenics, control electronics, and wiring).This holistic approach allows us to define and study resource efficiencies as ratios between performance and resource cost.As a proof of concept, we use MNR to minimize the power consumption of a full-stack quantum computer, performing noisy or fault-tolerant computing with a target performance for the task of interest.Comparing this with a classical processor performing the same task, we identify a quantum energy advantage in regimes of parameters distinct from the commonly considered quantum computational advantage.This provides a previously overlooked practical argument for building quantum computers.While our illustration uses highly idealized parameters inspired by superconducting qubits with concatenated error correction, the methodology is universal-it applies to other qubits and error-correcting codes-and it provides experimenters with guidelines to build energy-efficient quantum computers.In some regimes of high energy consumption, it can reduce this consumption by orders of magnitude.Overall, our methodology lays the theoretical foundation for resource-efficient quantum technologies.
Source: PRX Quantum
Institutions: Institut polytechnique de Grenoble, Centre National de la Recherche Scientifique, Institut Néel, University of Warsaw, Université Grenoble Alpes, Centre for Quantum Technologies, National University of Singapore, Entropica Labs (Singapore), Commissariat à l'Énergie Atomique et aux Énergies Alternatives, CEA Grenoble, Yale-NUS College, UMI MajuLab, Laboratoire de Physique et Modélisation des Milieux Condensés
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,511
OpenAlex ID: W4297812238