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arXiv:1911.00577

Comment on "Quantum supremacy using a programmable superconducting processor"

Karl Svozil

Published Nov 1, 2019• 4 citations
arXivHTMLPDF

Abstract

Even if Google AI's Sycamore processor is efficient for the particular task it has been designed for it fails to deliver universal computational capacity. Furthermore, even classical devices implementing transverse homoclinic orbits realize exponential speedups with respect to universal classical as well as quantum computations. Moreover, relative to the validity of quantum mechanics, there already exist quantum oracles which violate the Church-Turing thesis.

DOI
10.48550/arxiv.1911.00577

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Even if Google AI's Sycamore processor is efficient for the particular task it has been designed for it fails to deliver universal computational capacity. Furthermore, even classical devices implementing transverse homoclinic orbits realize exponential speedups with respect to universal classical as well as quantum computations. Moreover, relative to the validity of quantum mechanics, there already exist quantum oracles which violate the Church-Turing thesis.
The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor. We have used a programmable superconducting processor to create quantum states on 53 qubits, corresponding to a "parallel computation" of 10 million trillion states. For a simple algorithm, our Sycamore processor takes about 200 seconds to run a quantum circuit a million times - the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy, heralding a much-anticipated computing paradigm.
The promise of quantum computers is that certain computational tasks might be executed exponentially faster on a quantum processor than on a classical processor1. A fundamental challenge is to build a high-fidelity processor capable of running quantum algorithms in an exponentially large computational space. Here we report the use of a processor with programmable superconducting qubits2–7 to create quantum states on 53 qubits, corresponding to a computational state-space of dimension 253 (about 1016). Measurements from repeated experiments sample the resulting probability distribution, which we verify using classical simulations. Our Sycamore processor takes about 200 seconds to sample one instance of a quantum circuit a million times—our benchmarks currently indicate that the equivalent task for a state-of-the-art classical supercomputer would take approximately 10,000 years. This dramatic increase in speed compared to all known classical algorithms is an experimental realization of quantum supremacy8–14 for this specific computational task, heralding a much-anticipated computing paradigm. Quantum supremacy is demonstrated using a programmable superconducting processor known as Sycamore, taking approximately 200 seconds to sample one instance of a quantum circuit a million times, which would take a state-of-the-art supercomputer around ten thousand years to compute.
Quantum computation promises to execute certain computational tasks on time scales much faster than any known algorithm on an existing classical computer, for example calculating the prime factors of large integers. Recently a research team from Google claimed to have carried out such a task with a quantum computer, demonstrating in practice a case of this so-called quantum supremacy. Here we argue that this claim was not justified. Unlike other comments, our criticism is concerned with the missing verification of the output data of the quantum computation.
The Google Quantum AI team's long-term goal is to realize a large-scale fault-tolerant quantum computer. Of the technologies available to implement the quantum processor at the core of such a system, solid-state superconducting circuits based on Josephson junctions (JJs) are among the strongest contenders. Recent demonstrations with this technology include computation beyond the capabilities of today's most powerful supercomputers [1] and execution of quantum error correction (QEC) codes that can detect and correct either bit or phase flip errors [2]. These exciting results were enabled by low-error-rate quantum processors with tens of qubits and the supporting infrastructure to run these devices. However, today's quantum computers still have orders of magnitude fewer qubits than is required for a useful fault-tolerant quantum computer; for this, an estimated million or more qubits will be needed and scaling-up today's technology to this extent is non-trivial. Here, we review the Google Quantum AI team's recent progress and future plans. Specifically, we describe our post-classical Sycamore quantum computer and discuss the Google Quantum AI roadmap to fault-tolerant quantum computing.
Quantum computing, leveraging the principles of superposition and entanglement, has emerged as a revolutionary technology with the potential to outperform classical computers in specific tasks. This paper explores the concept of quantum supremacy, marked by Google's Sycamore processor, and its implications for classical computing. It discusses the foundations of quantum computing, including qubits, superposition, and key quantum algorithms like Shor's and Grover's, highlighting their advantages over classical systems. This study addresses the milestones in achieving quantum supremacy, including experimental benchmarks and performance comparisons. The challenges in scaling quantum systems, mitigating decoherence, and addressing criticisms of quantum supremacy claims are examined. Additionally, the paper outlines emerging applications in cryptography, optimization, and artificial intelligence and emphasizes the importance of hybrid quantum-classical systems in bridging current technological gaps. The future of quantum computing lies in advancing fault-tolerant systems and fostering interdisciplinary collaborations to realize its transformative potential across industries.
This study aims to analyze recent developments in quantum supremacy by comparing the computational performance of stabilized superconducting qubit systems with classical supercomputing capabilities. A systematic literature review was conducted on major experimental studies published between 2019 and 2024, focusing on random circuit sampling, qubit stability, gate fidelity, and computational runtime comparisons. The analysis covers key quantum processors, including Sycamore and Zuchongzhi, by evaluating three main parameters: number of qubits, circuit complexity, and performance gap relative to classical simulation.The results show that quantum processors with 50–100 qubits and high gate fidelity are able to complete specific sampling tasks within seconds to hours, whereas equivalent classical simulations would require thousands to billions of years. The findings also indicate that computational advantage increases exponentially with system scale and is strongly influenced by qubit stability and error suppression techniques. Although the demonstrated tasks remain specialized and not yet applicable to practical problems, the evidence confirms that stabilized qubit systems have achieved a measurable computational regime beyond classical feasibility.This review provides a clear synthesis of current experimental achievements and highlights that future progress toward practical quantum advantage depends on improvements in error correction, scalability, and hardware reliability.