Academic Journal
NISQ in practice: navigating noise, scale, and hardware constraints of near-term devices in quantum machine learning workflows.
| Τίτλος: | NISQ in practice: navigating noise, scale, and hardware constraints of near-term devices in quantum machine learning workflows. |
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| Συγγραφείς: | Daka, Chisomo, Bhattacharyya, Somnath |
| Πηγή: | New Journal of Physics; 2026, Vol. 28 Issue 8, p1-37, 37p |
| Θεματικοί όροι: | Quantum noise, Quantum computers, Quantum computing |
| Περίληψη: | The arrival of noisy intermediate-scale quantum (NISQ) devices defines an era in which quantum utility is fundamentally constrained by finite coherence times, limited qubit counts, gate infidelities, restricted connectivity, and measurement noise. Within this regime, hybrid quantum-classical frameworks have emerged as the dominant operational paradigm, particularly for quantum machine learning (QML). This review identifies a fundamental trade-off between expressivity, trainability, and noise resilience that governs the performance and scalability of NISQ-era QML systems. To contextualize these challenges, we introduce the resource-constrained iterative routing framework, a hardware-aware conceptual architecture that integrates topology-aware classical compression and distributed quantum orchestration to maintain circuit depths within device-dependent limits while routing representations toward symmetry-constrained Hilbert subspaces to mitigate noise-induced barren plateaus. Furthermore, we shift the discussion of quantum advantage away from asymptotic fault-tolerant complexity toward the notion of operational NISQ advantage based on finite-resource scaling, trainability preservation, and sampling complexity. We evaluate the practical resource efficiency of variational quantum algorithms, quantum kernel methods, and quantum neural networks across superconducting, trapped-ion, neutral-atom, and photonic platforms. The review also examines reproducibility challenges, emphasizing benchmarking against optimized classical baselines, and discusses strategies for managing the sampling overhead of error mitigation alongside distributed execution and quantum circuit compilation. Ultimately, NISQ in Practice positions hybrid QML as a physically constrained computational paradigm while outlining a pathway from contemporary noisy workflows toward early fault-tolerant quantum architectures. [ABSTRACT FROM AUTHOR] |
| Copyright of New Journal of Physics is the property of IOP Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 |
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| Header | DbId: edb DbLabel: Complementary Index An: 195897550 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.42456054688 |
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| Items | – Name: Title Label: Title Group: Ti Data: NISQ in practice: navigating noise, scale, and hardware constraints of near-term devices in quantum machine learning workflows. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Daka%2C+Chisomo%22">Daka, Chisomo</searchLink><br /><searchLink fieldCode="AR" term="%22Bhattacharyya%2C+Somnath%22">Bhattacharyya, Somnath</searchLink> – Name: TitleSource Label: Source Group: Src Data: New Journal of Physics; 2026, Vol. 28 Issue 8, p1-37, 37p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Quantum+noise%22">Quantum noise</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+computers%22">Quantum computers</searchLink><br /><searchLink fieldCode="DE" term="%22Quantum+computing%22">Quantum computing</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The arrival of noisy intermediate-scale quantum (NISQ) devices defines an era in which quantum utility is fundamentally constrained by finite coherence times, limited qubit counts, gate infidelities, restricted connectivity, and measurement noise. Within this regime, hybrid quantum-classical frameworks have emerged as the dominant operational paradigm, particularly for quantum machine learning (QML). This review identifies a fundamental trade-off between expressivity, trainability, and noise resilience that governs the performance and scalability of NISQ-era QML systems. To contextualize these challenges, we introduce the resource-constrained iterative routing framework, a hardware-aware conceptual architecture that integrates topology-aware classical compression and distributed quantum orchestration to maintain circuit depths within device-dependent limits while routing representations toward symmetry-constrained Hilbert subspaces to mitigate noise-induced barren plateaus. Furthermore, we shift the discussion of quantum advantage away from asymptotic fault-tolerant complexity toward the notion of operational NISQ advantage based on finite-resource scaling, trainability preservation, and sampling complexity. We evaluate the practical resource efficiency of variational quantum algorithms, quantum kernel methods, and quantum neural networks across superconducting, trapped-ion, neutral-atom, and photonic platforms. The review also examines reproducibility challenges, emphasizing benchmarking against optimized classical baselines, and discusses strategies for managing the sampling overhead of error mitigation alongside distributed execution and quantum circuit compilation. Ultimately, NISQ in Practice positions hybrid QML as a physically constrained computational paradigm while outlining a pathway from contemporary noisy workflows toward early fault-tolerant quantum architectures. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of New Journal of Physics is the property of IOP Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1088/1367-2630/ae8053 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 37 StartPage: 1 Subjects: – SubjectFull: Quantum noise Type: general – SubjectFull: Quantum computers Type: general – SubjectFull: Quantum computing Type: general Titles: – TitleFull: NISQ in practice: navigating noise, scale, and hardware constraints of near-term devices in quantum machine learning workflows. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Daka, Chisomo – PersonEntity: Name: NameFull: Bhattacharyya, Somnath IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 13672630 Numbering: – Type: volume Value: 28 – Type: issue Value: 8 Titles: – TitleFull: New Journal of Physics Type: main |
| ResultId | 1 |