{"id":"https://openalex.org/W7221138136","doi":"https://doi.org/10.48550/arxiv.2610.12148","title":"Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting","display_name":"Large-Scale Benchmarking of Quantum Neural Network Configurations for Financial Time Series Forecasting","publication_year":2026,"publication_date":"2026-10-08","ids":{"openalex":"https://openalex.org/W7221138136","doi":"https://doi.org/10.48550/arxiv.2610.12148"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2610.12148","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.12148","pdf_url":"https://arxiv.org/pdf/2610.12148","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2610.12148","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5017316446","display_name":"J WALLER","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Waller, Jack","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5158158596","display_name":"Xing Liang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I205051169","display_name":"Kingston University London","ror":"https://ror.org/05bbqza97","country_code":"GB","type":"education","lineage":["https://openalex.org/I205051169"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Liang, Xing","raw_affiliation_strings":["School of Computer Science and Mathematics , Kingston University London , London , KT1 2EE , U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Mathematics , Kingston University London , London , KT1 2EE , U.K","institution_ids":["https://openalex.org/I205051169"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137999783","display_name":"Dimitrios Makris","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Makris, Dimitrios","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5080223610","display_name":"Rajagopal Nilavalan","orcid":"https://orcid.org/0000-0001-8168-2039","observed_orcids":["https://orcid.org/0000-0001-8168-2039"]},"institutions":[{"id":"https://openalex.org/I59433898","display_name":"Brunel University of London","ror":"https://ror.org/00dn4t376","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I59433898"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nilavalan, Rajagopal","raw_affiliation_strings":["Department of Electronics and Electrical Engineering , Brunel University of London , London , UB8 3PH , U.K"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Electrical Engineering , Brunel University of London , London , UB8 3PH , U.K","institution_ids":["https://openalex.org/I59433898"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10682","display_name":"Quantum Computing Algorithms and Architecture","score":0.9970999956130981,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.002199999988079071,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11804","display_name":"Quantum many-body systems","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/quantum-neural-networks","display_name":"quantum neural networks","score":0.9990000128746033},{"id":"https://openalex.org/keywords/financial-time-series-forecasting","display_name":"financial time series forecasting","score":0.8980000019073486},{"id":"https://openalex.org/keywords/ansatz-design","display_name":"ansatz design","score":0.8849999904632568},{"id":"https://openalex.org/keywords/gbp-usd-exchange-rate","display_name":"GBP/USD exchange rate","score":0.859000027179718},{"id":"https://openalex.org/keywords/quantum-hardware-noise","display_name":"quantum hardware noise","score":0.5189999938011169},{"id":"https://openalex.org/keywords/gate-errors","display_name":"gate errors","score":0.5830000042915344},{"id":"https://openalex.org/keywords/decoherence","display_name":"decoherence","score":0.984000027179718},{"id":"https://openalex.org/keywords/entanglement","display_name":"entanglement","score":0.7630000114440918},{"id":"https://openalex.org/keywords/near-term-quantum-devices","display_name":"near-term quantum devices","score":0.7429999709129333}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6776999831199646},{"id":"https://openalex.org/C130979935","wikidata":"https://www.wikidata.org/wiki/Q568954","display_name":"Ansatz","level":2,"score":0.5827999711036682},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.49070000648498535},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46219998598098755},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.4401000142097473},{"id":"https://openalex.org/C58053490","wikidata":"https://www.wikidata.org/wiki/Q176555","display_name":"Quantum computer","level":3,"score":0.42910000681877136},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42879998683929443},{"id":"https://openalex.org/C203087015","wikidata":"https://www.wikidata.org/wiki/Q378201","display_name":"Qubit","level":3,"score":0.42179998755455017},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4065000116825104},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.3292999863624573},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32710000872612},{"id":"https://openalex.org/C113775141","wikidata":"https://www.wikidata.org/wiki/Q428691","display_name":"Computer engineering","level":1,"score":0.32260000705718994},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3221000134944916},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.3100000023841858},{"id":"https://openalex.org/C62611344","wikidata":"https://www.wikidata.org/wiki/Q1062658","display_name":"Node (physics)","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C124148022","wikidata":"https://www.wikidata.org/wiki/Q2122210","display_name":"Quantum circuit","level":5,"score":0.3034999966621399},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3001999855041504},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.29670000076293945},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.2948000133037567},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28949999809265137},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.28459998965263367},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.2745000123977661},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.27390000224113464},{"id":"https://openalex.org/C108650721","wikidata":"https://www.wikidata.org/wiki/Q1783253","display_name":"Counterfactual thinking","level":2,"score":0.2612000107765198}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2610.12148","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.12148","pdf_url":"https://arxiv.org/pdf/2610.12148","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2610.12148","doi":"https://doi.org/10.48550/arxiv.2610.12148","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2610.12148","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2610.12148","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2610.12148","pdf_url":"https://arxiv.org/pdf/2610.12148","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G3277110238","display_name":null,"funder_award_id":"NQCC200921","funder_id":"https://openalex.org/F4320314731","funder_display_name":"UK Research and Innovation"}],"funders":[{"id":"https://openalex.org/F4320311734","display_name":"Kingston University","ror":"https://ror.org/05bbqza97"},{"id":"https://openalex.org/F4320314731","display_name":"UK Research and Innovation","ror":"https://ror.org/001aqnf71"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7221138136.pdf","grobid_xml":"https://content.openalex.org/works/W7221138136.grobid-xml"},"referenced_works_count":6,"referenced_works":["https://openalex.org/W2767328092","https://openalex.org/W2976734616","https://openalex.org/W4221159811","https://openalex.org/W4389362805","https://openalex.org/W7134812065","https://openalex.org/W7162685625"],"related_works":["https://openalex.org/W4221159811","https://openalex.org/W4414903531","https://openalex.org/W4387195883","https://openalex.org/W4402266009","https://openalex.org/W4413027924","https://openalex.org/W4412967909","https://openalex.org/W7220027702","https://openalex.org/W2981542969","https://openalex.org/W7148178324","https://openalex.org/W2308335441"],"abstract_inverted_index":{"Quantum":[0],"machine":[1],"learning,":[2],"and":[3,82,99,120,132,186,198,219],"quantum":[4,170,230],"neural":[5],"networks":[6],"(QNNs)":[7],"in":[8,92],"particular,":[9],"are":[10,122],"advancing":[11],"fields":[12],"with":[13,195],"growing":[14],"potential.":[15],"Although":[16],"systematic":[17,48],"comparisons":[18],"of":[19,51,94,111,139,158,168,205,224],"QNN":[20,52,152,217,225],"configurations":[21,54],"have":[22],"been":[23,33],"explored":[24],"primarily":[25],"for":[26,55,216],"classification":[27],"tasks,":[28],"comparatively":[29],"little":[30],"attention":[31],"has":[32],"given":[34],"to":[35,125,192],"regression":[36],"problems,":[37],"particularly":[38],"financial":[39,56],"time":[40,57],"series":[41,58],"forecasting.":[42],"This":[43],"study":[44],"presents":[45],"a":[46,67,136,161,189,221],"large-scale":[47],"comparative":[49],"evaluation":[50],"component":[53],"forecasting,":[59],"using":[60],"the":[61,109,140,147,166,178,210],"GBP/USD":[62],"spot":[63],"exchange":[64],"rate":[65],"as":[66,116,202],"case":[68],"study.":[69],"A":[70],"grid":[71],"search":[72],"across":[73],"encoding":[74],"methods,":[75],"ansatz":[76,148],"designs,":[77],"qubit":[78],"counts,":[79],"layer":[80],"depths,":[81],"cost":[83],"functions":[84],"yields":[85],"1,368":[86],"distinct":[87],"model":[88,126],"configurations,":[89],"each":[90],"evaluated":[91],"terms":[93],"prediction":[95],"accuracy,":[96],"computational":[97],"cost,":[98],"convergence":[100],"behaviour.":[101],"The":[102,150],"results":[103],"reveal":[104],"unique":[105],"insights":[106],"into":[107],"how":[108],"choice":[110],"methods":[112],"influences":[113],"performance,":[114],"such":[115],"that":[117,133,183],"gate":[118,184,196],"selection":[119,197],"arrangement":[121],"more":[123],"critical":[124],"success":[127],"than":[128,144],"raw":[129],"parameter":[130],"count,":[131],"entanglement":[134],"is":[135,173],"system-level":[137],"property":[138],"full":[141],"circuit":[142,199],"rather":[143],"solely":[145],"at":[146],"level.":[149],"best-performing":[151],"configuration":[153],"achieves":[154],"an":[155],"$R^2$":[156],"score":[157],"0.985,":[159],"outperforming":[160],"classical":[162],"BiLSTM":[163],"baseline.":[164],"Additionally,":[165],"impact":[167],"real":[169],"hardware":[171,206],"noise":[172,207,226],"assessed":[174],"through":[175],"execution":[176],"on":[177,228],"IQM":[179],"Emerald":[180],"device,":[181],"revealing":[182],"errors":[185],"decoherence":[187],"represent":[188],"significant":[190],"barrier":[191],"practical":[193,213],"deployment,":[194],"depth":[200],"identified":[201],"key":[203],"determinants":[204],"resilience.":[208],"Overall,":[209],"findings":[211],"provide":[212],"architectural":[214],"guidance":[215],"design":[218],"establish":[220],"baseline":[222],"characterisation":[223],"sensitivity":[227],"near-term":[229],"devices.":[231]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-10-10T00:00:00"}