{"work":{"id":"5dd3e61a-2250-4afb-a3d4-dd7202477631","openalex_id":"https://openalex.org/W4396986598","doi":"10.48550/arxiv.2405.08810","arxiv_id":"2405.08810","raw_key":null,"title":"Quantum computing with Qiskit","authors":null,"authors_text":"Ali Javadi-Abhari, Matthew Treinish, Kevin Krsulich, Christopher J. Wood, Jake Lishman, Julien Gacon","year":2024,"venue":"quant-ph","abstract":"We describe Qiskit, a software development kit for quantum information science. We discuss the key design decisions that have shaped its development, and examine the software architecture and its core components. We demonstrate an end-to-end workflow for solving a problem in condensed matter physics on a quantum computer that serves to highlight some of Qiskit's capabilities, for example the representation and optimization of circuits at various abstraction levels, its scalability and retargetability to new gates, and the use of quantum-classical computations via dynamic circuits. Lastly, we discuss some of the ecosystem of tools and plugins that extend Qiskit for various tasks, and the future ahead.","external_url":"https://arxiv.org/abs/2405.08810","cited_by_count":189,"metadata_source":"pith","metadata_fetched_at":"2026-08-05T02:28:24.338817+00:00","pith_arxiv_id":"2405.08810","created_at":"2026-05-08T23:24:25.710861+00:00","updated_at":"2026-08-05T02:28:24.338817+00:00","title_quality_ok":false,"display_title":"Quantum computing with Qiskit","render_title":"Quantum computing with Qiskit"},"hub":{"state":{"work_id":"5dd3e61a-2250-4afb-a3d4-dd7202477631","tier":"super_hub","tier_reason":"100+ Pith inbound or 10,000+ external citations","pith_inbound_count":222,"external_cited_by_count":189,"distinct_field_count":22,"first_pith_cited_at":"2024-04-30T12:31:03+00:00","last_pith_cited_at":"2026-07-09T10:53:02+00:00","author_build_status":"needed","summary_status":"needed","contexts_status":"needed","graph_status":"needed","ask_index_status":"needed","reader_status":"not_needed","recognition_status":"not_needed","updated_at":"2026-08-22T02:39:25.697971+00:00","tier_text":"super_hub"},"tier":"super_hub","role_counts":[{"context_role":"background","n":18},{"context_role":"method","n":6},{"context_role":"baseline","n":2},{"context_role":"dataset","n":1}],"polarity_counts":[{"context_polarity":"background","n":18},{"context_polarity":"use_method","n":6},{"context_polarity":"baseline","n":2},{"context_polarity":"use_dataset","n":1}],"runs":{"ask_index":{"job_type":"ask_index","status":"succeeded","result":{"title":"Quantum computing with Qiskit","claims":[{"claim_text":"We describe Qiskit, a software development kit for quantum information science. We discuss the key design decisions that have shaped its development, and examine the software architecture and its core components. We demonstrate an end-to-end workflow for solving a problem in condensed matter physics on a quantum computer that serves to highlight some of Qiskit's capabilities, for example the representation and optimization of circuits at various abstraction levels, its scalability and retargetability to new gates, and the use of quantum-classical computations via dynamic circuits. Lastly, we d","claim_type":"abstract","evidence_strength":"source_metadata"},{"claim_text":"tions around multiple axes (Ry andR z) prior to entanglement, which captures more intricate quantum states but increases circuit to a depth of 15 and imposes a heavier optimization burden due to the larger parameter vectorθ. 2.3 Noise Simulation and Optimization To replicate the operational realities of NISQ hardware, all quantum circuits were simulated using Qiskit's AerSimulator [26] equipped with depolarizing noise model. We applied a conservative single-qubit gate error probability ofpgate =","claim_type":"method","confidence":0.95,"evidence_strength":"citation_context"},{"claim_text":"uous optimization problem was solved using a classical gradient-free algorithm [20], specifically L-BFGS-B [21] or COBYLA [20], applied to the fixed topologyτ opt. 2.4 Implementation Details All data processing and analyses were performed in Python. Single-cell data were han- dled usingscanpy[10]. The quantum circuits and simulations were implemented in IBM's Qiskit framework [22, 23], using theqiskit aersimulator [24]. Classical opti- mization used routines fromSciPy.optimize[25]. Code for our ","claim_type":"method","confidence":0.95,"evidence_strength":"citation_context"},{"claim_text":"In:Quantum9 (2025), p. 1752. [56]Constructing Large Increment Gates. June 2015.url:https://algassert.com/circuits/2015/06/ 12/Constructing-Large-Increment-Gates.html. [57] Pedro MQ Cruz and Bruno Murta. \"Shallow unitary decompositions of quantum Fredkin and Toffoli gates for connectivity-aware equivalent circuit averaging\". In:APL Quantum1.1 (2024). [58] Abeynaya Gnanasekaran, Amit Surana, and Hongyu Zhu. \"Variational Quantum Framework for Non- linear PDE Constrained Optimization Using Carleman ","claim_type":"background","confidence":0.95,"evidence_strength":"citation_context"},{"claim_text":"Without phase-specific attribution, observations such as \"SDK X wins bykgates\" are dif- ficult to act upon. A compiler developer cannot tell whether to improve synthesis rules, basis translation or routing heuristics, and an SDK user cannot tell whether changing optimization settings is likely to help on their circuit family. Further, modern produc- tion compilation pipelines often interleave transformations across stages [18] (especially at higher optimization levels), making it challenging to ","claim_type":"baseline","confidence":0.9,"evidence_strength":"citation_context"},{"claim_text":"Application areas include computational mechan- ics [11], fluid dynamics [12], and quantum chemistry [13, 14, 15, 16]. We can only list a limited number of examples here; the broader literature is vast. This scientific breadth has driven grow- ing demand for software tools that support the construction and analysis of block encodings. General-purpose frameworks includeQiskit[ 17],Cirq[ 18],PennyLane[ 19],Qrisp[ 20], and Date: May 11, 2026. The work of M. Deiml and D. Peterseim is partially funde","claim_type":"background","confidence":0.9,"evidence_strength":"citation_context"},{"claim_text":"In order to contextualize the extensive contributions of this manuscript, we first detail previous contributions in the field. A. Noisy Quantum Simulation Techniques and Applications Simulators capable of modeling noisy quantum systems are dearly needed for the development of quantum computers. The realistic noise sources for these devices are diverse, including environmental coupling [29], gate errors [30], [31], and material defects [32]. While modeling and understanding these errors is import","claim_type":"background","confidence":0.9,"evidence_strength":"citation_context"}],"why_cited":"Pith tracks Quantum computing with Qiskit because it crossed a citation-hub threshold. Current citing contexts most often use it as background evidence (13 contexts).","role_counts":[{"n":13,"context_role":"background"},{"n":5,"context_role":"method"},{"n":2,"context_role":"baseline"},{"n":1,"context_role":"dataset"}]},"error":null,"updated_at":"2026-05-19T01:51:26.660086+00:00"},"author_expand":{"job_type":"author_expand","status":"succeeded","result":{"authors_linked":[{"id":"8d3dbc43-8139-4b64-b622-7253f748c408","orcid":null,"display_name":"Ali Javadi-Abhari"},{"id":"1a955ce3-2c73-4996-bd20-3dff251996d0","orcid":null,"display_name":"Matthew Treinish"},{"id":"5fbf91f8-f1df-4c77-be71-1eacb43e5881","orcid":null,"display_name":"Kevin Krsulich"},{"id":"a7a40ecb-c02b-49a7-98be-5d382c793ab2","orcid":null,"display_name":"Christopher J. Wood"},{"id":"d41d5731-2f85-407b-8d5b-60b9ee2c9904","orcid":null,"display_name":"Jake Lishman"},{"id":"0c9dc455-03cd-4347-b704-1aeb26dc4174","orcid":null,"display_name":"Julien Gacon"}]},"error":null,"updated_at":"2026-05-19T01:51:27.587107+00:00"},"context_extract":{"job_type":"context_extract","status":"succeeded","result":{"enqueued_papers":25},"error":null,"updated_at":"2026-05-14T09:28:35.391736+00:00"},"graph_features":{"job_type":"graph_features","status":"succeeded","result":{"co_cited":[{"title":"A Quantum Approximate Optimization Algorithm","work_id":"5a33d9f3-407a-4c7e-a119-ff581c66b173","shared_citers":13},{"title":"PennyLane: Automatic differentiation of hybrid quantum-classical computations","work_id":"83078d0b-6c02-4fc5-822d-4da4204fd057","shared_citers":9},{"title":"Temme, S","work_id":"3c92d785-59f7-4730-bad2-69740ded3274","shared_citers":5},{"title":"https://doi.org/10.1038/ncomms5213","work_id":"39da8f39-782e-47a1-bb98-e482b344cb2a","shared_citers":4},{"title":"Preskill, Quantum C omputing in the NISQ era and beyond , Quantum 2, 79 (2018), doi:10.22331/q-2018-08-06-79","work_id":"db02914e-62e0-4457-a74e-836513460506","shared_citers":4},{"title":"Qrisp: A Framework for Compilable High-Level Programming of Gate-Based Quantum Computers","work_id":"42a4e981-0ad5-4428-ac29-9dc691ae58d1","shared_citers":4},{"title":"Quantum measurements and the Abelian Stabilizer Problem","work_id":"0840a06c-bc13-4619-9fd6-9abb6810e838","shared_citers":4},{"title":"arXiv preprint quant-ph/0410184 , year=","work_id":"800d759b-b04d-4b3d-b7be-0fd926923e1f","shared_citers":3},{"title":"Efficient Variational Quantum Linear Solver for Struc- tured Sparse Matrices","work_id":"4e7d0b46-c07c-41ed-ae04-103755c546cc","shared_citers":3},{"title":"Eisert and J","work_id":"d46e53f4-ead6-49bf-81d5-1387e74fa766","shared_citers":3},{"title":"https://doi.org/10.1103/PRXQuantum.2.040326","work_id":"77ecb336-a15f-46e0-9da0-f2efa1bcb566","shared_citers":3},{"title":"https://doi.org/10.1103/RevModPhys.94.015004","work_id":"a4832486-2224-4a39-b5bf-603061df264c","shared_citers":3},{"title":"Improved simulation of stabilizer circuits.Physical Review A, 70(5)","work_id":"416d6536-5c05-4307-a9ee-9aaaa95ca347","shared_citers":3},{"title":"Lightsabre: A lightweight and enhanced sabre algorithm","work_id":"457270ac-d10c-4246-8a1b-65611a3a12ee","shared_citers":3},{"title":"Proximal Policy Optimization Algorithms","work_id":"240c67fe-d14d-4520-91c1-38a4e272ca19","shared_citers":3},{"title":"Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization","work_id":"6816306e-3b62-4309-b32f-4d209725f0dc","shared_citers":3},{"title":"Quantum Linear System Solvers: A Survey of Algorithms and Applications","work_id":"7afc8efd-85da-49e3-9d3c-0a705ee7121e","shared_citers":3},{"title":"Ransfordet al., Helios: A 98-qubit trapped-ion quantum computer (2025), arXiv:2511.05465 [quant-ph]","work_id":"7e8e1268-8959-465d-9ff8-f13e74e3147f","shared_citers":3},{"title":"Tackling the qubit mapping problem for NISQ-Era quantum devices","work_id":"601b03e1-7457-4ad3-b46f-7cce8eb55012","shared_citers":3},{"title":"2019 , month = sep, volume =","work_id":"2dd6b155-5b3c-496e-b65e-19419459eaee","shared_citers":2},{"title":"Accelerating MPI allreduce communication with efficient gpu-based, compression schemes on modern GPU clusters","work_id":"35f315ad-d6d9-4445-a87a-708ce1d76f72","shared_citers":2},{"title":"and Mitarai, Kosuke and Imai, Ryosuke and Tamiya, Shiro and Yamamoto, Takahiro and Yan, Tennin and Kawakubo, Toru and Nakagawa, Yuya O","work_id":"1426ab9d-52e3-4d63-a766-4d1d886aa9be","shared_citers":2},{"title":"astropy/photutils: Version 1.8.0","work_id":"a2be0426-0f15-4d3d-a62d-9b21d5ec8960","shared_citers":2},{"title":"Available: https://arxiv.org/abs/2308.01999","work_id":"008215c1-e3df-4dfc-bd37-e5f858fc1b17","shared_citers":2}],"time_series":[{"n":65,"year":2026}],"dependency_candidates":[]},"error":null,"updated_at":"2026-05-14T09:38:51.533805+00:00"},"identity_refresh":{"job_type":"identity_refresh","status":"succeeded","result":{"items":[{"title":"Qwen3 Technical Report","outcome":"unchanged","work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e","resolver":"local_arxiv","confidence":0.98,"old_work_id":"25a4e30c-1232-48e7-9925-02fa12ba7c9e"}],"counts":{"fixed":0,"merged":0,"unchanged":1,"quarantined":0,"needs_external_resolution":0},"errors":[],"attempted":1},"error":null,"updated_at":"2026-05-14T09:28:41.447194+00:00"},"role_polarity":{"job_type":"role_polarity","status":"succeeded","result":{"title":"Quantum computing with Qiskit","claims":[{"claim_text":"We describe Qiskit, a software development kit for quantum information science. We discuss the key design decisions that have shaped its development, and examine the software architecture and its core components. We demonstrate an end-to-end workflow for solving a problem in condensed matter physics on a quantum computer that serves to highlight some of Qiskit's capabilities, for example the representation and optimization of circuits at various abstraction levels, its scalability and retargetability to new gates, and the use of quantum-classical computations via dynamic circuits. Lastly, we d","claim_type":"abstract","evidence_strength":"source_metadata"},{"claim_text":"tions around multiple axes (Ry andR z) prior to entanglement, which captures more intricate quantum states but increases circuit to a depth of 15 and imposes a heavier optimization burden due to the larger parameter vectorθ. 2.3 Noise Simulation and Optimization To replicate the operational realities of NISQ hardware, all quantum circuits were simulated using Qiskit's AerSimulator [26] equipped with depolarizing noise model. We applied a conservative single-qubit gate error probability ofpgate =","claim_type":"method","confidence":0.95,"evidence_strength":"citation_context"},{"claim_text":"uous optimization problem was solved using a classical gradient-free algorithm [20], specifically L-BFGS-B [21] or COBYLA [20], applied to the fixed topologyτ opt. 2.4 Implementation Details All data processing and analyses were performed in Python. Single-cell data were han- dled usingscanpy[10]. The quantum circuits and simulations were implemented in IBM's Qiskit framework [22, 23], using theqiskit aersimulator [24]. Classical opti- mization used routines fromSciPy.optimize[25]. Code for our ","claim_type":"method","confidence":0.95,"evidence_strength":"citation_context"},{"claim_text":"In:Quantum9 (2025), p. 1752. [56]Constructing Large Increment Gates. June 2015.url:https://algassert.com/circuits/2015/06/ 12/Constructing-Large-Increment-Gates.html. [57] Pedro MQ Cruz and Bruno Murta. \"Shallow unitary decompositions of quantum Fredkin and Toffoli gates for connectivity-aware equivalent circuit averaging\". In:APL Quantum1.1 (2024). [58] Abeynaya Gnanasekaran, Amit Surana, and Hongyu Zhu. \"Variational Quantum Framework for Non- linear PDE Constrained Optimization Using Carleman ","claim_type":"background","confidence":0.95,"evidence_strength":"citation_context"},{"claim_text":"Without phase-specific attribution, observations such as \"SDK X wins bykgates\" are dif- ficult to act upon. A compiler developer cannot tell whether to improve synthesis rules, basis translation or routing heuristics, and an SDK user cannot tell whether changing optimization settings is likely to help on their circuit family. Further, modern produc- tion compilation pipelines often interleave transformations across stages [18] (especially at higher optimization levels), making it challenging to ","claim_type":"baseline","confidence":0.9,"evidence_strength":"citation_context"},{"claim_text":"Application areas include computational mechan- ics [11], fluid dynamics [12], and quantum chemistry [13, 14, 15, 16]. We can only list a limited number of examples here; the broader literature is vast. This scientific breadth has driven grow- ing demand for software tools that support the construction and analysis of block encodings. General-purpose frameworks includeQiskit[ 17],Cirq[ 18],PennyLane[ 19],Qrisp[ 20], and Date: May 11, 2026. The work of M. Deiml and D. Peterseim is partially funde","claim_type":"background","confidence":0.9,"evidence_strength":"citation_context"},{"claim_text":"In order to contextualize the extensive contributions of this manuscript, we first detail previous contributions in the field. A. Noisy Quantum Simulation Techniques and Applications Simulators capable of modeling noisy quantum systems are dearly needed for the development of quantum computers. The realistic noise sources for these devices are diverse, including environmental coupling [29], gate errors [30], [31], and material defects [32]. While modeling and understanding these errors is import","claim_type":"background","confidence":0.9,"evidence_strength":"citation_context"}],"why_cited":"Pith tracks Quantum computing with Qiskit because it crossed a citation-hub threshold. Current citing contexts most often use it as background evidence (13 contexts).","role_counts":[{"n":13,"context_role":"background"},{"n":5,"context_role":"method"},{"n":2,"context_role":"baseline"},{"n":1,"context_role":"dataset"}]},"error":null,"updated_at":"2026-05-19T01:51:26.656548+00:00"},"summary_claims":{"job_type":"summary_claims","status":"succeeded","result":{"title":"Quantum computing with Qiskit","claims":[{"claim_text":"We describe Qiskit, a software development kit for quantum information science. We discuss the key design decisions that have shaped its development, and examine the software architecture and its core components. We demonstrate an end-to-end workflow for solving a problem in condensed matter physics on a quantum computer that serves to highlight some of Qiskit's capabilities, for example the representation and optimization of circuits at various abstraction levels, its scalability and retargetability to new gates, and the use of quantum-classical computations via dynamic circuits. Lastly, we d","claim_type":"abstract","evidence_strength":"source_metadata"}],"why_cited":"Pith tracks Quantum computing with Qiskit because it crossed a citation-hub threshold.","role_counts":[]},"error":null,"updated_at":"2026-05-14T09:38:55.536438+00:00"}},"summary":{"title":"Quantum computing with Qiskit","claims":[{"claim_text":"We describe Qiskit, a software development kit for quantum information science. We discuss the key design decisions that have shaped its development, and examine the software architecture and its core components. We demonstrate an end-to-end workflow for solving a problem in condensed matter physics on a quantum computer that serves to highlight some of Qiskit's capabilities, for example the representation and optimization of circuits at various abstraction levels, its scalability and retargetability to new gates, and the use of quantum-classical computations via dynamic circuits. Lastly, we d","claim_type":"abstract","evidence_strength":"source_metadata"}],"why_cited":"Pith tracks Quantum computing with Qiskit because it crossed a citation-hub threshold.","role_counts":[]},"graph":{"co_cited":[{"title":"A Quantum Approximate Optimization Algorithm","work_id":"5a33d9f3-407a-4c7e-a119-ff581c66b173","shared_citers":13},{"title":"PennyLane: Automatic differentiation of hybrid quantum-classical computations","work_id":"83078d0b-6c02-4fc5-822d-4da4204fd057","shared_citers":9},{"title":"Temme, S","work_id":"3c92d785-59f7-4730-bad2-69740ded3274","shared_citers":5},{"title":"https://doi.org/10.1038/ncomms5213","work_id":"39da8f39-782e-47a1-bb98-e482b344cb2a","shared_citers":4},{"title":"Preskill, Quantum C omputing in the NISQ era and beyond , Quantum 2, 79 (2018), doi:10.22331/q-2018-08-06-79","work_id":"db02914e-62e0-4457-a74e-836513460506","shared_citers":4},{"title":"Qrisp: A Framework for Compilable High-Level Programming of Gate-Based Quantum Computers","work_id":"42a4e981-0ad5-4428-ac29-9dc691ae58d1","shared_citers":4},{"title":"Quantum measurements and the Abelian Stabilizer Problem","work_id":"0840a06c-bc13-4619-9fd6-9abb6810e838","shared_citers":4},{"title":"arXiv preprint quant-ph/0410184 , year=","work_id":"800d759b-b04d-4b3d-b7be-0fd926923e1f","shared_citers":3},{"title":"Efficient Variational Quantum Linear Solver for Struc- tured Sparse Matrices","work_id":"4e7d0b46-c07c-41ed-ae04-103755c546cc","shared_citers":3},{"title":"Eisert and J","work_id":"d46e53f4-ead6-49bf-81d5-1387e74fa766","shared_citers":3},{"title":"https://doi.org/10.1103/PRXQuantum.2.040326","work_id":"77ecb336-a15f-46e0-9da0-f2efa1bcb566","shared_citers":3},{"title":"https://doi.org/10.1103/RevModPhys.94.015004","work_id":"a4832486-2224-4a39-b5bf-603061df264c","shared_citers":3},{"title":"Improved simulation of stabilizer circuits.Physical Review A, 70(5)","work_id":"416d6536-5c05-4307-a9ee-9aaaa95ca347","shared_citers":3},{"title":"Lightsabre: A lightweight and enhanced sabre algorithm","work_id":"457270ac-d10c-4246-8a1b-65611a3a12ee","shared_citers":3},{"title":"Proximal Policy Optimization Algorithms","work_id":"240c67fe-d14d-4520-91c1-38a4e272ca19","shared_citers":3},{"title":"Quantum-Centric Algorithm for Sample-Based Krylov Diagonalization","work_id":"6816306e-3b62-4309-b32f-4d209725f0dc","shared_citers":3},{"title":"Quantum Linear System Solvers: A Survey of Algorithms and Applications","work_id":"7afc8efd-85da-49e3-9d3c-0a705ee7121e","shared_citers":3},{"title":"Ransfordet al., Helios: A 98-qubit trapped-ion quantum computer (2025), arXiv:2511.05465 [quant-ph]","work_id":"7e8e1268-8959-465d-9ff8-f13e74e3147f","shared_citers":3},{"title":"Tackling the qubit mapping problem for NISQ-Era quantum devices","work_id":"601b03e1-7457-4ad3-b46f-7cce8eb55012","shared_citers":3},{"title":"2019 , month = sep, volume =","work_id":"2dd6b155-5b3c-496e-b65e-19419459eaee","shared_citers":2},{"title":"Accelerating MPI allreduce communication with efficient gpu-based, compression schemes on modern GPU clusters","work_id":"35f315ad-d6d9-4445-a87a-708ce1d76f72","shared_citers":2},{"title":"and Mitarai, Kosuke and Imai, Ryosuke and Tamiya, Shiro and Yamamoto, Takahiro and Yan, Tennin and Kawakubo, Toru and Nakagawa, Yuya O","work_id":"1426ab9d-52e3-4d63-a766-4d1d886aa9be","shared_citers":2},{"title":"astropy/photutils: Version 1.8.0","work_id":"a2be0426-0f15-4d3d-a62d-9b21d5ec8960","shared_citers":2},{"title":"Available: https://arxiv.org/abs/2308.01999","work_id":"008215c1-e3df-4dfc-bd37-e5f858fc1b17","shared_citers":2}],"time_series":[{"n":65,"year":2026}],"dependency_candidates":[]},"authors":[{"id":"8d3dbc43-8139-4b64-b622-7253f748c408","orcid":null,"display_name":"Ali Javadi-Abhari","source":"manual","import_confidence":0.72},{"id":"a7a40ecb-c02b-49a7-98be-5d382c793ab2","orcid":null,"display_name":"Christopher J. Wood","source":"manual","import_confidence":0.72},{"id":"d41d5731-2f85-407b-8d5b-60b9ee2c9904","orcid":null,"display_name":"Jake Lishman","source":"manual","import_confidence":0.72},{"id":"0c9dc455-03cd-4347-b704-1aeb26dc4174","orcid":null,"display_name":"Julien Gacon","source":"manual","import_confidence":0.72},{"id":"5fbf91f8-f1df-4c77-be71-1eacb43e5881","orcid":null,"display_name":"Kevin Krsulich","source":"manual","import_confidence":0.72},{"id":"1a955ce3-2c73-4996-bd20-3dff251996d0","orcid":null,"display_name":"Matthew Treinish","source":"manual","import_confidence":0.72}]}}