{"id":"https://openalex.org/W7225083596","doi":"https://doi.org/10.5281/zenodo.23876051","title":"AQRC-FM: Finite-Shot Quantum Reservoir Computing with Fading Memory and Confidence-Gated Memory for Multi-Horizon Weather Forecasting and Rare-Event Assessment","display_name":"AQRC-FM: Finite-Shot Quantum Reservoir Computing with Fading Memory and Confidence-Gated Memory for Multi-Horizon Weather Forecasting and Rare-Event Assessment","publication_year":2026,"publication_date":"2026-11-02","ids":{"openalex":"https://openalex.org/W7225083596","doi":"https://doi.org/10.5281/zenodo.23876051"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.23876051","doi":"https://doi.org/10.5281/zenodo.23876051","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23876051","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},"volume":null,"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.23876051","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063781411","display_name":"Achal Nilhani","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Nilhani, Achal","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0001-8569-996X","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5063781411"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9839000105857849,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9839000105857849,"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/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.0024999999441206455,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.0012000000569969416,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/quantum-reservoir-computing","display_name":"quantum reservoir computing","score":0.9950000047683716},{"id":"https://openalex.org/keywords/fading-memory","display_name":"fading memory","score":0.7730000019073486},{"id":"https://openalex.org/keywords/rare-event-detection","display_name":"rare event detection","score":0.765999972820282},{"id":"https://openalex.org/keywords/cyclone-detection","display_name":"cyclone detection","score":0.4819999933242798},{"id":"https://openalex.org/keywords/flood-detection","display_name":"flood detection","score":0.8009999990463257},{"id":"https://openalex.org/keywords/echo-state-networks","display_name":"echo state networks","score":0.5460000038146973},{"id":"https://openalex.org/keywords/quantum-advantage","display_name":"quantum advantage","score":0.4490000009536743},{"id":"https://openalex.org/keywords/indian-monsoon","display_name":"Indian monsoon","score":0.3370000123977661},{"id":"https://openalex.org/keywords/era5-reanalysis","display_name":"ERA5 reanalysis","score":0.6119999885559082},{"id":"https://openalex.org/keywords/ibtracs","display_name":"IBTrACS","score":0.640999972820282},{"id":"https://openalex.org/keywords/forecast-calibration","display_name":"forecast calibration","score":0.6190000176429749},{"id":"https://openalex.org/keywords/shot-noise","display_name":"shot noise","score":0.4339999854564667}],"concepts":[{"id":"https://openalex.org/C135796866","wikidata":"https://www.wikidata.org/wiki/Q7315328","display_name":"Reservoir computing","level":4,"score":0.6682999730110168},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5698999762535095},{"id":"https://openalex.org/C44249647","wikidata":"https://www.wikidata.org/wiki/Q208498","display_name":"Confidence interval","level":2,"score":0.5216000080108643},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.4528999924659729},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.4408999979496002},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.4287000000476837},{"id":"https://openalex.org/C121117317","wikidata":"https://www.wikidata.org/wiki/Q4439221","display_name":"Credible interval","level":3,"score":0.4104999899864197},{"id":"https://openalex.org/C32848918","wikidata":"https://www.wikidata.org/wiki/Q845789","display_name":"Observable","level":2,"score":0.38510000705718994},{"id":"https://openalex.org/C84114770","wikidata":"https://www.wikidata.org/wiki/Q46344","display_name":"Quantum","level":2,"score":0.3449999988079071},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.33959999680519104},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.33629998564720154},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.33239999413490295},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.32420000433921814},{"id":"https://openalex.org/C180591934","wikidata":"https://www.wikidata.org/wiki/Q1253369","display_name":"Downtime","level":2,"score":0.31139999628067017},{"id":"https://openalex.org/C175309249","wikidata":"https://www.wikidata.org/wiki/Q725864","display_name":"Pipeline transport","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.30570000410079956},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.29840001463890076},{"id":"https://openalex.org/C2777052490","wikidata":"https://www.wikidata.org/wiki/Q5072826","display_name":"Chaotic","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C74256435","wikidata":"https://www.wikidata.org/wiki/Q134052","display_name":"Flood myth","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C58053490","wikidata":"https://www.wikidata.org/wiki/Q176555","display_name":"Quantum computer","level":3,"score":0.2854999899864197},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2816999852657318},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.27880001068115234},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.2750000059604645},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.2685000002384186},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.2667999863624573},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.2655999958515167},{"id":"https://openalex.org/C41661131","wikidata":"https://www.wikidata.org/wiki/Q220764","display_name":"Interrupt","level":3,"score":0.2653999924659729},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2646999955177307},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C32277403","wikidata":"https://www.wikidata.org/wiki/Q740445","display_name":"Ridge","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C158379750","wikidata":"https://www.wikidata.org/wiki/Q214111","display_name":"Network packet","level":2,"score":0.262800008058548},{"id":"https://openalex.org/C2776292839","wikidata":"https://www.wikidata.org/wiki/Q5179217","display_name":"Coverage probability","level":3,"score":0.2621999979019165},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25699999928474426}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.23876051","doi":"https://doi.org/10.5281/zenodo.23876051","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23876051","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},{"id":"doi:10.5281/zenodo.23876052","doi":"https://doi.org/10.5281/zenodo.23876052","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23876052","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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":"doi:10.5281/zenodo.23876051","doi":"https://doi.org/10.5281/zenodo.23876051","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23876051","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"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"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7196955976","https://openalex.org/W4390722955","https://openalex.org/W4407364274","https://openalex.org/W7167918767","https://openalex.org/W4408004960","https://openalex.org/W7163563573","https://openalex.org/W7152067714","https://openalex.org/W7129013916","https://openalex.org/W6929915542","https://openalex.org/W7172394476"],"abstract_inverted_index":{"Abstract:":[0],"Quantum":[1],"reservoir":[2,26,298],"computing":[3],"(QRC)":[4],"is":[5,21,38,160,198,219,243,269],"attractive":[6],"for":[7,115,238],"temporal":[8],"prediction":[9],"because":[10],"the":[11,190,200,211],"quantum":[12,284,297],"dynamics":[13],"are":[14],"fixed":[15],"and":[16,32,80,101,131,179,248,254,279],"only":[17],"a":[18,25,46,52,59,65,76,82,89,94,106,120,141,153,180,216,232,263],"linear":[19],"readout":[20,218],"trained.":[22],"Whether":[23],"such":[24],"helps":[27],"on":[28,98,111,175],"real":[29],"meteorological":[30],"data,":[31],"which":[33,197],"of":[34,67,145,155,234,283],"its":[35],"components":[36],"matter,":[37],"rarely":[39],"tested":[40],"against":[41,236],"strong":[42,167,276],"classical":[43,168,277],"baselines":[44],"under":[45],"leakage-audited":[47],"protocol.":[48],"We":[49,108,258],"present":[50],"AQRC-FM,":[51],"simulated":[53],"finite-shot":[54,77,293],"QRC":[55,266],"pipeline":[56],"with":[57,72,119,136,227,245],"(i)":[58],"fading-memory":[60],"input":[61],"window":[62],"processed":[63],"by":[64,193],"bank":[66],"random":[68],"8-qubit":[69],"Hamiltonian":[70],"reservoirs":[71],"four":[73],"encodings,":[74],"(ii)":[75],"observable":[78],"readout,":[79],"(iii)":[81],"confidence-gated":[83],"feature":[84],"memory":[85,91,187],"that":[86,268],"switches":[87],"to":[88],"regime-specific":[90],"prototype":[92],"when":[93],"confidence":[95,149,201,241],"score,":[96],"based":[97],"shot":[99],"noise":[100],"encoding":[102],"disagreement,":[103],"falls":[104],"below":[105],"threshold.":[107],"evaluate":[109],"it":[110],"pooled":[112],"ERA5/IMD/IBTRACS":[113],"series":[114],"three":[116],"Indian":[117],"states":[118],"purged":[121],"rolling-origin":[122],"protocol":[123],"(test":[124],"years":[125],"2023-2025,":[126],"lead":[127],"times":[128],"6-48":[129],"h)":[130],"an":[132],"eight-check":[133],"leakage":[134],"audit":[135],"negative":[137],"controls.":[138],"AQRC-FM":[139,230,261],"reaches":[140,231],"mean":[142,191],"raw-unit":[143],"R^2":[144,192],"0.686":[146],"(95%":[147],"block-bootstrap":[148],"interval":[150],"[0.671,":[151],"0.700]),":[152],"skill":[154],"+0.107":[156],"beyond":[157],"climatology.":[158],"It":[159],"competitive":[161,270],"with,":[162,271],"but":[163,272],"does":[164,273],"not":[165,274,280],"outperform,":[166,275],"models:":[169],"gradient":[170],"boosting":[171],"(0.723),":[172],"ridge":[173],"regression":[174],"engineered":[176,228],"features":[177],"(0.719),":[178],"256-unit":[181],"echo-state":[182],"network":[183],"(ESN,":[184],"0.706).":[185],"The":[186,240],"gate":[188],"changes":[189],"at":[194],"most":[195],"0.002,":[196],"inside":[199],"intervals,":[202],"so":[203],"we":[204,249],"find":[205],"no":[206],"measurable":[207],"forecasting":[208,303],"benefit":[209],"from":[210],"gate.":[212],"For":[213],"rare":[214],"hazards,":[215],"quantum-only":[217],"weak":[220],"(cyclone":[221],"F1":[222,225],"0.07,":[223],"flood":[224],"0.03);":[226],"features,":[229],"macro-F1":[233],"0.53":[235],"0.61":[237],"boosting.":[239],"score":[242],"associated":[244],"forecast":[246],"error,":[247],"report":[250],"calibration,":[251],"event-level":[252],"recall,":[253],"neural-head":[255],"training":[256],"dynamics.":[257],"therefore":[259],"position":[260],"as":[262,281],"rigorously":[264],"evaluated,":[265],"benchmark":[267],"baselines,":[278],"evidence":[282],"advantage.":[285],"Index":[286],"Terms:":[287],"echo":[288],"state":[289],"network,":[290],"extreme-event":[291],"detection,":[292],"sampling,":[294],"leakage-free":[295],"evaluation,":[296],"computing,":[299],"rare-event":[300],"classification,":[301],"weather":[302]},"counts_by_year":[],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2026-11-03T00:00:00"}