Performance claims in quantum technology are not properties of hardware alone. They are properties of a declared task, system boundary, normalization denominator, uncertainty or security convention, and comparator. We perform a cross-domain analysis spanning quantum computing, simulation, communication, sensing, clocks, randomness generation, and their enabling technologies. The analysis changes substantive conclusions in several representative cases. For the longest direct finite-key quantum key distribution case analyzed here, the same final key gives rates differing by a factor of $1.91$ when normalized by complete acquisition rather than transmission-active time. In Advanced LIGO, $6.1\,\mathrm{dB}$ peak quantum-noise reduction coexists with a $0.534$ coincident analysis-ready fraction, separating detector-level gain from delivered observing service. In quantum computation and photonic sampling, matching the observable, error tolerance, loss model, sample count, amortization, and classical hardware moves or reverses published crossover claims. Across domains, the recurring limits are correlated error, multiplicative interface loss, thermal and nonequilibrium occupation, calibration covariance, measurement efficiency, fabrication yield, and control latency. A quantum advantage is therefore established only for a fixed task and boundary when the accepted output outperforms the best documented alternative at matched accuracy, elapsed time, availability, and lifecycle cost. The resulting framework identifies the measurements required to convert component records into reproducible system capability. Credible progress is defined by reproduced logical workloads, prospectively validated simulations, repeater links outperforming direct transmission, long-duration calibrated sensors and clocks, integrated hardware with predictable yield and reliability.
Performance claims in quantum technology are not properties of hardware alone. They are properties of a declared task, system boundary, normalization denominator, uncertainty or security convention, and comparator. We perform a cross-domain analysis spanning quantum computing, simulation, communication, sensing, clocks, randomness generation, and their enabling technologies. The analysis changes substantive conclusions in several representative cases. For the longest direct finite-key quantum key distribution case analyzed here, the same final key gives rates differing by a factor of 1.91 when normalized by complete acquisition rather than transmission-active time. In Advanced LIGO, 6.1dB peak quantum-noise reduction coexists with a 0.534 coincident analysis-ready fraction, separating detector-level gain from delivered observing service. In quantum computation and photonic sampling, matching the observable, error tolerance, loss model, sample count, amortization, and classical hardware moves or reverses published crossover claims. Across domains, the recurring limits are correlated error, multiplicative interface loss, thermal and nonequilibrium occupation, calibration covariance, measurement efficiency, fabrication yield, and control latency. A quantum advantage is therefore established only for a fixed task and boundary when the accepted output outperforms the best documented alternative at matched accuracy, elapsed time, availability, and lifecycle cost. The resulting framework identifies the measurements required to convert component records into reproducible system capability. Credible progress is defined by reproduced logical workloads, prospectively validated simulations, repeater links outperforming direct transmission, long-duration calibrated sensors and clocks, integrated hardware with predictable yield and reliability.
Quantum integrated sensing and communication (QISAC) is emerging as a key paradigm for next-generation quantum technologies, in which entanglement, coherence, and non-classical correlations are jointly exploited to enable secure communication, distributed sensing, and networked quantum functionalities. As quantum systems transition from laboratory demonstrations to deployable infrastructures, performance is increasingly limited not by individual components but by cross-layer interactions among hardware noise, channel loss, error-management strategies, and network architecture. This survey presents a system-level architectural framework for QISAC, focusing on how physical-layer constraints, quantum channel characteristics, and error-mitigation mechanisms jointly determine end-to-end performance and architectural feasibility in scalable quantum networks. We analyze representative quantum hardware platforms-including superconducting circuits, trapped ions, neutral atoms, photonic systems, and solid-state spin qubits-through the lens of coherence time, fidelity, interface capability, and technology readiness, emphasizing their implications for networked sensing and communication rather than device-level operation. The roles of quantum error correction (QEC) and entanglement purification are examined as complementary error-management layers, highlighting trade-offs among resource overhead, latency, and achievable entanglement fidelity under realistic noise and memory constraints. We further connect these architectural considerations to QISAC-enabled applications such as distributed quantum sensing, quantum key distribution (QKD), and repeater-assisted entanglement distribution, clarifying how hardware limitations and error-management choices propagate to application-level performance. By synthesizing noise, error management, and network co-design into a unified system perspective, this survey provides design-oriented insights and architectural guidelines for building scalable, noise-resilient QISAC networks.
This record contains a manuscript and supporting reproducibility materials for “Ledger-Valid Recovery Diagnostics for Quantum Computing: A Prespecified Sequential Audit for Claim-Limited Recovery Validation.” The paper proposes a methodological validation framework for quantum-computing recovery claims. Its central claim is that endpoint recovery success is necessary but not sufficient for a strong recovery claim when calibration failure, false-carrier acceptance, postselection artifacts, leakage, sentinel blindness, or hidden discard can carry failure modes out of view. The manuscript introduces a prespecified S0–S4 ledger audit that converts familiar diagnostic ingredients—label calibration, candidate recovery, false-carrier challenge tests, postselection/leakage accounting, and sentinel observability—into an ordered, claim-limiting taxonomy. A supportive recovery claim is permitted only when all required gates pass; otherwise, the first failed stage determines a lower claim ceiling and an interpretable failure class. The evaluation is based on synthetic endpoint-level validation suites rather than circuit-level simulation or hardware data. These suites test the behavior of the decision architecture under clean support, degraded observability, false-carrier artifacts, postselection/leakage artifacts, sentinel-blind cases, correlated artifacts, and adversarial overlap. The results show a strong specificity/sensitivity tradeoff: the full ledger protocol sharply reduces synthetic non-support supportive-call rates relative to endpoint-only reporting, but with substantial loss of mechanism-positive support sensitivity. The paper explicitly treats this as a conservative claim-validity architecture, not as a new quantum error-correcting code, new physical observable, hardware speedup, or proof of improved device performance. The supporting materials document the final synthetic validation protocol, endpoint definitions, decision-bound implementation, direction-aware threshold sensitivity analysis, latent-threshold eligibility analysis, mechanism-negative/latent-eligible boundary cases, and figure/table traceability. The archive is intended to support review of the manuscript’s aggregate numerical claims and methodological provenance. Hardware implementation, independent simulator validation, and full raw-event-ledger reproduction are identified as future extensions. Suggested keywords: quantum computing; quantum error correction; validation methodology; recovery diagnostics; leakage; postselection; benchmarking; synthetic validation; decision architecture; reproducibility.
Quantum Artificial Intelligence (QAI) has emerged at the nexus of quantum computing and AI, promising to redefine computational frontiers. This survey critically synthesizes the state-of-the- art through 2024, elucidating the profound bidirectional synergy between these fields. We analyze how classical machine learning is accelerating quantum hardware control, circuit optimization, and error correction. Conversely, we assess the potential quantum advantage of algorithms, including variational and kernel-based methods, across domains such as drug discovery, financial modeling, and cybersecurity. Our analysis reveals a critical trade-of between the utility of near-term Noisy Intermediate-Scale Quantum (NISQ) devices and the long-term promise of fault-tolerant architectures. We identify fundamental obstacles to QAI’s advancement, including hardware decoherence, algorithmic barren plateaus, and data-encoding bottlenecks. While QAI’s potential is transformative, achieving practical quantum advantage requires a concerted effort to overcome these core challenges at the hardware-software interface. This work provides a roadmap for navigating the current landscape and prioritizing future research in this rapidly evolving discipline. • We introduce a bi-directional Quantum-AI framework, AI-for-QC and QC-for-AI, and provide a practical roadmap for integrating both paradigms across the full stack. • We quantify AI-enabled gains: calibration time drops from 8 hours to 2 hours, circuit depth falls by up to 42%, and ML decoders cut logical error rates by about half. • We synthesize comparative guidance on VQAs, quantum kernels, and hybrid/quantum Transformers, clarifying trainability limits and criteria for credible quantum advantage. • We deliver a clinically grounded playbook for radiology, covering small-data use cases, plug-in hybrid modules, OOD validation, fairness assessments, and TRIPOD/CONSORT-AI reporting.
Quantum computer deployment in space remains constrained by demanding hardware, environmental, and communications requirements. This paper reviews the opportunities and challenges of using quantum computing in space systems. It focuses on payload integration, radiation protection, quantum-memory stability, autonomous hybrid classical–quantum control, and quantum-network performance. The work is integrative, not novel in algorithms, architecture, optimization, or experiments. It organizes reviewed literature around system-level deployment barriers and highlights open research gaps. Compared with existing reviews, this paper adds a category taxonomy, a system-level constraint framing, updated coverage, and an integration of rate–loss fundamentals. The analysis highlights the need for standardized interfaces, autonomous classical–quantum control, and evolutionary qualification of subsystems in orbit. Modular, radiation-adaptive payloads and calibration-aware classical processing are identified as important strategies for improving reliability and mission resilience. The industrial outlook indicates a gradual transition from subsystem demonstrations toward standardized, interoperable, and vertically integrated quantum-communication services, including LEO QKD constellations, space-qualified protocol stacks, and CubeSat-scale photonic devices. Readiness claims are classified using a multi-stage ladder: launch, power-on, communication, calibration, quantum operation, and peer-reviewed useful computational performance. Fully fault-tolerant quantum computing in orbit remains a longer-term objective requiring advances in logical-qubit overhead, radiation tolerance, low-power control, memory performance, and repeater architectures.
Quantum computers have the potential to provide an advantage over classical computers in a number of areas. Numerous metrics to benchmark the performance of quantum computers, ranging from their individual hardware components to entire applications, have been proposed over the years. Objective comparisons are further hampered in practice as different variations of the same metric are used, and the data disclosed together with a reported metric value is often not sufficient to reproduce the measurements. In this poster, we present our recent article, where we address these challenges by providing a review of metrics and benchmarks for quantum computers [1], including 1) a comprehensive collection of benchmarks allowing holistic comparisons of quantum computers, 2) a consistent format of the definitions across all metrics including a transparent description of the methodology and of the main assumptions and limitations, and 3) a reproducible approach by linking the metrics to open-source software used to evaluate them . We identify five areas where international standardization working groups could be established, namely: i) the identification and agreement on the categories of metrics that comprehensively benchmark device performance; ii) the identification and agreement on a set of well-established metrics that together comprehensively benchmark performance; iii) the identification of metrics specific to hardware platforms, including non-gate-based quantum computers; iv) inter-laboratory comparison studies to develop best practice guides for measurement methodology; and v) agreement on what data and software should be reported together with a metric value to ensure trust, transparency and reproducibility. We provide potential routes to advancing these areas. We expect that this work will accelerate the progress of quantum computing hardware towards quantum advantage. [1] D. Lall et al., A Review and Collection of Metrics and Benchmarks for Quantum Computers: Definitions, Methodologies and Software, arXiv:2502.06717.
Small mineral patches can be spectrally distinct yet difficult to detect when diffraction mixes their light with the surrounding rock. Conventional hyperspectral detectors analyze recorded intensities, leaving an opportunity to recover target evidence through the optical measurement itself. We develop a quantum-assisted framework for exploiting this opportunity in prospective unmanned aerial vehicle (UAV) hyperspectral sensing. A common radiometric and diffraction model describes direct imaging and spatial-mode measurements of the same light. The theory identifies target changes that survive background fitting and explains when mode measurements retain more detection information. It quantifies losses from mode mismatch and unresolved outputs, linking the quantum benchmark to finite receiver design. A common-basis result identifies when material spectra can share a spatial measurement at fixed geometry. An adaptive receiver uses an imaging pilot to select candidate modes and combines both stages in a full-region presence test. Simulations based on measured mineral spectra and published UAV acquisition scales demonstrate gains over complete direct imaging at a shared active-exposure budget, including pilot cost and optical loss. At matched empirical 1\% region-level false alarms, detection increases from 62.95\% to 69.65\% for a representative target. Paired analysis supports gains with registration uncertainty and, separately, under controlled nonuniform backgrounds. Calibrated mode errors reveal where measurement losses weaken or reverse the benefit. These results connect background identifiability to receiver choices and their operating conditions. They provide a framework for designing spatial measurements alongside spectral channels to detect small material features.