A survey of Quantum Algorithm Applications and End-to-end Complexities

Arxiv – A survey of applications and end-to-end complexities. (337 pages. Oct 2023) by Researchers at AWS Center for Quantum Computing, Institute for Quantum Information, RWTH Aachen University (Germany), Caltech, Imperial College of London, Harvard, Alfred R´enyi Institute of Mathematics, Budapest, Hungary, IT University of Copenhagen, Copenhagen, Denmark, Amazon Quantum Solutions Lab.

The anticipated applications of quantum computers span across science and industry, ranging from quantum chemistry and many-body physics to optimization, finance, and machine learning. Proposed quantum solutions in these areas typically combine multiple quantum algorithmic primitives into an overall quantum algorithm, which must then incorporate the methods of quantum error correction and fault tolerance to be implemented correctly on quantum hardware. As such, it can be difficult to assess how much a particular application benefits from quantum computing, as the various approaches are often sensitive to intricate technical details about the underlying primitives and their complexities. Here we present a survey of several potential application areas of quantum algorithms and their underlying algorithmic primitives, carefully considering technical caveats and subtleties. We outline the challenges and opportunities in each area in an “end-to-end” fashion by clearly defining the problem being solved alongside the input-output model, instantiating all “oracles,” and spelling out all hidden costs. We also compare quantum solutions against state-ofthe-art classical methods and complexity-theoretic limitations to evaluate possible quantum speedups.

The survey is written in a modular, wiki-like fashion to facilitate navigation of the content. Each primitive and application area is discussed in a standalone section, with its own bibliography of references and embedded hyperlinks that direct to other relevant sections. This structure mirrors that of complex quantum algorithms that involve several layers of abstraction, and it enables rapid evaluation of how end-to-end complexities are impacted when subroutines are altered.

They summarize the end-to-end complexities of a number of leading quantum application proposals (by which we mean quantum algorithms applied to a well-defined real-world problem). The complexities of these applications are deduced from the complexities of their underlying primitives, which they review in detail. The modular structure of the survey aids the highlevel understanding of the costs and tradeoffs coming from the various choices one makes when
designing and compiling a quantum algorithm, as well as identifying the bottlenecks for a given application. On the technical front, this survey does not attempt to advance the state of the art; rather, it aims to collect, synthesize, and contextualize key results in the literature. They consider algorithms in the quantum circuit model, which is arguably the best-studied model for quantum computation, and renders the presented complexities hardware agnostic. In order to obtain concrete bounds we require oracles to be explicitly instantiated. They generally assume that quantum error correction of some form will be necessary, due to unavoidable imperfections inherent to all known quantum hardware modalities. As such, they typically consider the nonClifford cost of quantum algorithms as the dominant cost, in keeping with leading quantum fault-tolerance schemes. Due to the general lack of application-scale experimental data, they focus on elucidating provable speedups, and only mention noisy, intermediate scale quantum (NISQ) algorithms in passing, where appropriate, since they are typically heuristic.

Throughout this survey, they attempt to be thorough, but not exhaustive in presentation; they only aim to give a representative collection of references, rather than providing a complete list. Generally, they try to explain how asymptotic complexity statements arise from their underlying primitives, but technical results are typically presented without explicit derivation or proofs, for which they refer the reader to the cited references. Additionally, they often quote resource estimates from the literature without covering all of the application-specific optimizations to the underlying primitives that are required to arrive at the reported constant factors. They survey a number of quantum applications, primitives, and fault-tolerance schemes, however the omission of other approaches does not indicate that they are unimportant. Also, the scope of this work excludes substantial topics, such as: quantum sensing or communications, measurement-based quantum computing, adiabatic quantum computing and quantum annealing, analog quantum simulators, quantum-inspired (“dequantization”) methods, and tensor network algorithms.

An overarching takeaway of this survey is that the current literature generally lacks fully end-to-end analyses for concrete quantum applications. Consequently, in several parts of this survey, a fully satisfactory end-to-end accounting is not achieved. In part, this is due to certain technical aspects of the relevant quantum algorithms being underexplored, and in some cases also due to a lack of specific details on how the output of the quantum algorithm will integrate into concrete computational workflows for future quantum computing users. Quantum algorithms research often works upward from algorithmic primitives to identify computational tasks with maximal quantum speedups, but these may not align with the tasks most relevant to the user.
On the other hand, potential users themselves may not yet know exactly how they would use a new capability to advance their high-level goals. Yet, we find ourselves at a point in the history of quantum computing at which it behooves us to fill in these details and adopt this end-to end lens. As more end-to-end applications are found and with small fault-tolerant quantum computers now on the horizon, they expect the story to continue to evolve—this survey provides a snapshot of the state of play in 2023. While improved quantum algorithms and approaches to quantum error correction and fault tolerance are likely to be discovered, classical computers continue to grow in scale and speed, and classical algorithms are also constantly refined and developed, thereby moving the goalposts for end-to-end quantum speedups. They hope the reader will find this survey a valuable guide for navigating this complex and dynamic landscape.

1 thought on “A survey of Quantum Algorithm Applications and End-to-end Complexities”

  1. Sounds like a WHOLE LOT of words, and yet precious few actual quantum computing algorithm results. Lots of ‘projecting’, ‘small quantum’ and ‘holistic’ approaches.

    Naysaying, perhaps?

    But although it puts me dead center in the gunsights of the Quantum enthusiasts and leagues of investors, I remain fairly close to Ms. Sabine Hossenfelder’s unflinchingly clear-eyed assessment. Namely, that quantum has not yet accomplished a real-world (scale size) problem, and all the theory notwithstanding, very likely will not … any time soon.

    As I said, I can hear the bullets buzzing past my head.
    Unpopular, to say the least.

    Even so, IF I AM WRONG, and if there really are — today — real-world quantum computational results at least on par with conventional computing alternatives, I sure would like to hear what they are, and how they’re working in action. Are millions of quantum shuffles happening per (what … second? day? month?). These kind of realities.

    Certainly we MUST wish Godspeed and high success rates, as well as the usual unexpected breakthroughs, to the quantum computing communities worldwide. I just would not start a revolutionary company whose business plan relies on the marvel of quantum computing delivering results.

    ⋅-⋅-⋅ Just saying, ⋅-⋅-⋅
    ⋅-=≡ GoatGuy ✓ ≡=-⋅

    PS To anyone: please feel free to cite actual real-world results!

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