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Quantum computing and artificial intelligence: status and perspectives

T0 review · 2 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read This white paper argues that artificial intelligence and quantum computing form a two-way synergy and that a coordinated research agenda should fund them as a unified discipline.

desk verdict A comprehensive, honest quantum-AI roadmap that deserves referee time, but the hardware milestones need resource estimates and one claim about optimal circuits is overblown. read the letter →

arxiv 2505.23860 v3 pith:O47HPSRK submitted 2025-05-29 quant-ph cs.AIcs.LG

Giovanni Acampora , Andris Ambainis , Natalia Ares , Leonardo Banchi , Pallavi Bhardwaj , Daniele Binosi , G. Andrew D. Briggs , Tommaso Calarco
show 31 more authors
Vedran Dunjko Jens Eisert Olivier Ezratty Paul Erker Federico Fedele Elies Gil-Fuster Martin Gärttner Mats Granath Markus Heyl Iordanis Kerenidis Matthias Klusch Anton Frisk Kockum Richard Kueng Mario Krenn Jörg Lässig Antonio Macaluso Sabrina Maniscalco Florian Marquardt Kristel Michielsen Gorka Muñoz-Gil Daniel Müssig Hendrik Poulsen Nautrup Sophie A. Neubauer Evert van Nieuwenburg Roman Orus Jörg Schmiedmayer Markus Schmitt Philipp Slusallek Filippo Vicentini Christof Weitenberg Frank K. Wilhelm
This is my paper
classification quant-phcs.AIcs.LG
keywords quantummachinelearningartificialintelligenceNISQfault-tolerantcomputingerrorcorrectionresearchagendaAIforoptimization
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This white paper argues that artificial intelligence and quantum computing are not separate fields but a single two-way synergy: classical AI is already accelerating the development of quantum devices, and quantum computers promise, over a longer horizon, to relieve the computational bottlenecks of AI. The paper's core claim is that a coordinated research agenda, funding both directions together, will mature faster than either field pursued alone. It organizes the landscape into 'quantum for AI' (quantum-assisted machine learning, optimization, reasoning) and 'AI for quantum' (circuit compilation, control, error correction, simulation, data analysis), and it ties the agenda to a hardware timeline that moves from noisy intermediate-scale devices to early fault-tolerant machines. A sympathetic reader would take away that the near-term returns are mostly in the AI-for-quantum direction, while the quantum-for-AI payoffs are a mid- to long-term bet that depends on hardware maturing as projected.

What carries the argument

The load-bearing mechanism is the two-way pipeline between quantum and classical computation, organized around a hardware roadmap that progresses from NISQ devices of roughly 100–200 physical qubits to early fault-tolerant machines with 50–100 logical qubits. On the AI-for-quantum side, the workhorse components are machine-learned decoders for quantum error correction, reinforcement learning for circuit compilation and control, neural quantum states for classical simulation, and ML-based tomography and error mitigation. On the quantum-for-AI side, the central objects are parameterized quantum circuits and quantum kernels used in variational quantum algorithms, HHL-based linear algebra subroutines for training, and quantum-generated data for supervised and unsupervised learning. These components are bound together by the paper's scheduling device: concrete short-, mid-, and long-term goals that align algorithmic research with hardware maturity.

What would settle it

If physical qubit error rates do not drop far enough for the surface code to support 50–100 logical qubits within the paper's stated time window (a checkable hardware milestone), or if AI-based decoders cannot be trained to decode at real-time speed for large code distances, the short- and mid-term agenda loses its grounding.

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Extended reading notes

Core claim

On its own terms, this paper sets out to establish that the convergence of AI and quantum computing is already productive and can be made reliably so through deliberate investment. Its central claim is that the two disciplines form a mutually reinforcing loop: AI-driven methods—reinforcement learning for circuit compilation and error decoding, neural networks for quantum simulation and state reconstruction, automated control and calibration—now provide some of the clearest near-term advances in quantum technology, while quantum-assisted machine learning, quantum kernel methods, and quantum-generated training data lay the groundwork for AI capabilities that classical computing cannot match. The paper presents this loop as an actionable research and use-case agenda with short-, mid-, and long-term goals, and it argues that the field's foundational questions—how to define learning when data, labels, and agents are quantum—deserve the same systematic attention as its applications. It does not prove a new theorem or report a single experiment; it compiles the state of the art and stakes out the direction in which the payoff is most likely to be found.

Load-bearing premise

The agenda's milestones assume quantum hardware will progress from today's 100–200 physical-qubit noisy devices to early fault-tolerant machines with 50–100 logical qubits within roughly three to ten years, and that classical AI will scale to handle the growing complexity of quantum device control and decoding.

Editorial extensions

If this is right

  • Near term: AI-based decoders, circuit compilers, and automated control will improve the reliability of existing NISQ devices, making quantum utility more likely for chemistry and error-mitigated computation.
  • Mid term: hybrid quantum-classical pipelines, including quantum-generated training data and quantum-assisted feature extraction, will reach real-world applications in materials, healthcare, and time-series analysis if the hardware roadmap holds.
  • Long term: fault-tolerant machines with 50–100 logical qubits would enable quantum advantage in ML subroutines such as linear algebra, optimization, and sampling, provided the trainability obstacles (barren plateaus, missing quantum backpropagation) are resolved.
  • Resource estimation, including energy consumption, must accompany the agenda so that AI-assisted quantum control does not erase the energy savings quantum computing is expected to deliver.
  • The field requires a new hybrid software-engineering discipline and standardized interfaces so that quantum problems can be expressed in a common machine-learning language.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The paper bundles speculative subfields (quantum natural language processing, quantum multi-agent systems) with proven ones; a cheaper test would be to funnel near-term funding to AI-for-quantum applications such as control, decoding, and simulation, which are already demonstrable.
  • A concrete benchmark to track the synergy: measure whether ML-based decoders for surface codes reach real-time decoding speeds at code distances beyond current practice; if not, the fault-tolerance timetable slips.
  • The paper's energy-resource concern points to a testable extension: quantify the energy cost of AI-based control and decoding against the energy saved by quantum advantage, to see whether the net is actually positive.
  • The quantum-for-AI side is a bet on quantum-generated data; a testable near-term milestone is whether quantum-simulated training data beats purely classical data on any real drug-discovery or materials task.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. This white paper, authored by a large consortium of European researchers, surveys the intersection of quantum computing and artificial intelligence and proposes a strategic research agenda. It covers two main directions: how quantum computing could aid AI (quantum machine learning, optimization, reasoning, planning, multi-agent systems, and applications in healthcare, industry, and physics) and how classical AI can assist quantum technologies (hardware discovery, compilation, error correction, simulation, data analysis, control, and calibration). The paper closes with a set of recommendations for theoretical work, hardware-roadmap alignment, resource estimation, software engineering, open science, education, and societal challenges. It explicitly states in the Introduction that some parts of the text are speculative and positions itself as an early proposal for a European strategic research and innovation agenda.

Significance. If taken as a roadmap document, the paper's value lies in its comprehensive synthesis of a large and recent literature, its balanced treatment of both directions of the quantum-AI relationship, and its explicit acknowledgment of speculative content. The breadth of the author list and the 226 cited references make this a useful reference for the community. The paper does not present new derivations, machine-checked proofs, or reproducible code; its contribution is organizational and programmatic. However, several specific milestones—particularly those involving early fault-tolerant quantum computers—depend on hardware scaling assumptions that are not quantified, and this weakens the internal credibility of the proposed timeline.

major comments (2)
  1. [Section III (goal bullets) and Section VII.B, with Section IV.A.4] The agenda sets concrete milestones tied to hardware evolution: 100–200 physical qubits in the near-to-mid term and 50–100 logical qubits for early fault-tolerant quantum computers. No physical-qubit resource estimate is provided for the eFTQC milestone. Section IV.A.4 states that gate error rates must drop from p ≈ 10^-3 to p ≈ 10^-10 for practical fault tolerance. Under standard surface-code resource models, protecting a single logical qubit at that error rate from a physical error rate of 10^-3 requires on the order of 10^3–10^4 physical qubits, so 50–100 logical qubits would require roughly 10^5–10^6 physical qubits. No vendor roadmap is cited that targets such counts within the stated 5–10 year horizon. Because several recommended milestones (e.g., those in Sections III.B.4–III.B.6) depend on these logical qubits, the white paper should either add concrete resource estimates or explicitly reframe the eFTQC milestones as conditional on hardware developments that are not currently on any cited roadmap. Section VII.C already calls for resource estimation, but the milestones are set before any such estimates are provided.
  2. [Section III.A.3 and Section III.B.3] The claimed speedups for quantum unsupervised learning, such as the exponential speedup of q-means and quantum PCA, are repeatedly qualified with the assumption that the cost of encoding classical data into quantum states is negligible. This assumption is acknowledged in Section III.A.3, but it is precisely the data-loading bottleneck that often negates QML speedups, and the paper does not quantify this cost at that point or connect it to the qRAM discussion in Section VII.B. Since the white paper's short-term goal of demonstrating quantum utility from quantum processors as a pre-processing stage depends on this issue, the roadmap should either provide data-loading cost estimates or restrict the utility claims to cases where quantum-ready data (e.g., states produced by quantum experiments) are available.
minor comments (5)
  1. [Section IV.A.4, Short-term goals] The bulleted list contains an empty bullet between 'AI-based quantum error decoders extended to large code distances and beyond the surface code' and 'Improved design of probe states in quantum sensing'; remove the stray bullet.
  2. [References, Ref. [144]] Reference [144] is malformed: the author list appears to be missing and the title runs into the journal information; it should be reformatted with proper author names and journal details.
  3. [Acronym list and Section VII.F] The acronym 'EIB' is expanded as 'European Innovation Bank'; in the context of large-cap funding, the standard institution is the European Investment Bank, so the expansion should be corrected.
  4. [Section IV.A.4] The variable p in 'p ∼ 10−3 to p ∼ 10−10 or lower' is not defined at first use; please define it explicitly as the gate error probability or qubit error rate.
  5. [Section V.B.1] The sentence 'If the learner uses up all the quantum data during the training phase, then the learning process is essentially classical, as the training set becomes a classical map' is confusing; clarify that this refers to measuring the quantum training data and converting it to classical information.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the white paper makes no derivations and fits no parameters; its roadmap claims are policy assumptions, not reductions to their inputs.

full rationale

This document is a community white paper and research agenda, not a technical derivation. It contains no equations whose outputs equal their inputs, no fitted parameters later renamed as predictions, and no uniqueness theorem imported from prior work to forbid alternatives. The central claims — that AI and quantum computing are synergistic and that coordinated European funding should be pursued — are programmatic statements supported by literature summaries and cited examples. The paper explicitly labels the speculative nature of much of its content (Section II: 'some parts of the text are speculative, due to the great novelty of the subject'), and it repeatedly identifies open challenges and missing validations rather than presenting them as established results. The numerous self-citations (e.g., Refs. [8], [22], [84], [85], [148]) are normal literature attributions in a multi-author roadmap; they are not load-bearing in the sense that the agenda would collapse without them. Ref. [8], for instance, supplies a taxonomy, but the taxonomy is organizational, not an input that is later recovered as an output. Similarly, the claim that the first proofs of quantum learning advantages appeared in Refs. [84, 85] is a priority attribution to externally checkable mathematical results, not a circular derivation. The only notable weakness — the 50–100 logical qubit milestone appearing without a physical-qubit resource estimate (Sections III and VII.B) — is a correctness or feasibility risk, not circularity: the paper sets targets rather than deriving them from fitted inputs. Overall, the derivation chain is absent rather than circular, so the appropriate score is 0.

Assumptions & free parameters 0 free parameters · 3 assumptions · 0 invented entities

As a review and roadmap, the paper makes no new derivations. The ledger captures the external bets on hardware maturation and on the continued scaling of classical AI that the proposed agenda depends on.

assumptions (3)
  • domain assumption Quantum hardware will progress along roadmaps from 100 to 200 physical qubit NISQ devices to early fault-tolerant quantum computers with 50 to 100 logical qubits over the 3 to 10 year horizon.
    The research agenda and its short and mid-term goals assume this hardware trajectory (Section III and Section VII.B).
  • domain assumption Classical AI methods will continue to scale in capability and will be effective for quantum control, calibration, compilation, and decoding tasks.
    Many of the AI-for-quantum recommendations rely on ML models outperforming non-trainable baselines at scale (Section IV).
  • domain assumption Hybrid quantum-classical workflows are the practical near-term operating paradigm for QAI.
    The roadmap concentrates on hybrid systems, e.g., quantum pre-processing plus classical training, and quantum critics removed after training (Sections III.A.1 and III.A.2).

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Cite this review

Pith. "Pith review of Quantum computing and artificial intelligence: status and perspectives." pith.science (2026). https://pith.science/paper/O47HPSRK

@misc{pith2026250523860,
  author       = {Pith},
  title        = {Pith review of: Quantum computing and artificial intelligence: status and perspectives},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/O47HPSRK}},
  note         = {Machine review of arXiv:2505.23860}
}
read the original abstract

This white paper discusses and explores the various points of intersection between quantum computing and artificial intelligence (AI). It describes how quantum computing could support the development of innovative AI solutions. It also examines use cases of classical AI that can empower research and development in quantum technologies, with a focus on quantum computing and quantum sensing. The purpose of this white paper is to provide a long-term research agenda aimed at addressing foundational questions about how AI and quantum computing interact and benefit one another. It concludes with a set of recommendations and challenges, including how to orchestrate the proposed theoretical work, align quantum AI developments with quantum hardware roadmaps, estimate both classical and quantum resources - especially with the goal of mitigating and optimizing energy consumption - advance this emerging hybrid software engineering discipline, and enhance European industrial competitiveness while considering societal implications.

Figures

Figures reproduced from arXiv: 2505.23860 by the authors.

Figure 1
Figure 1. FIG. 1. An overview of the structure of this white paper. [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2 [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Schematic of particular applications of AI for quantum computing, as presented in the different subsections of Section [PITH_FULL_IMAGE:figures/full_fig_p016_3.png] view at source ↗

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Forward citations

Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Qubit Health Analytics and Clustering for HPC-Integrated Quantum Processors

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    Daily calibration data from a 20-qubit NISQ device cluster into stable and noisy qubit groups, and GHZ experiments confirm the stable group runs more reliable circuits.

  2. Parity Cross-Resonance: A Multiqubit Gate

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    An abstract proposing a native three-qubit parity cross-resonance gate is paired with a body text about heart-rate sensor denoising, leaving the gate's derivation and data absent from the manuscript.

  3. AML-QKD: Adaptive Machine Learning Framework for Real-time Parameter Tuning in QKD

    quant-ph 2026-03 conditional novelty 4.0 of 10

    ML-based TCN+PPO controller raises simulated QKD key rates by 14-25% and cuts QBER roughly in half across BB84, E91, and COW, with a separate exploratory QRL variant reporting a 29.2% E91 throughput gain.

  4. Exploring Quantum Responsible Innovation efforts in Canada and the world

    physics.soc-ph 2025-07 conditional novelty 4.0 of 10

    An analysis of national quantum strategies using a ten-principle Responsible Innovation framework finds Canada emphasizes the quantum race more than peers while underweighting inclusion, complementary innovation, and ...

  5. Artificial intelligence for representing and characterizing quantum systems

    quant-ph 2025-09 unverdicted novelty 1.0 of 10

    A review organizes AI-based quantum system characterization into ML, deep learning, and language model paradigms, covering property prediction and implicit state reconstruction.

Reference graph

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.