The　carbon　emission　rights　option　(CERO),　as　a　real　option,　can　be　used　to　measure　market　volatility　risk　and　help　enterprises　obtain　income.　However,　traditional　option　pricing　models　mostly　operate　under　risk　neutrality　and　are　unsuitable　for　CERO　pricing.　Quantum　finance,　as　a　branch　of　economic　physics,　can　effectively　improve　the　uncertain　behavior　of　financial　markets.　Therefore,　in　order　to　obtain　more　reasonable　results,　the　study　designs　a　quantum　Monte　Carlo　simulation　method　for　pricing　CERO　under　risk　uncertainty.　Firstly,　real　options　theory,　quantum　computing,　Monte　Carlo　simulation,　and　stochastic　volatility　(SV)　model　are　combined　to　achieve　quantum　state　preparation　and　payoff　calculation　on　quantum　circuits.　Then,　quantum　algorithms　are　used　to　achieve　CERO　dynamic　pricing.　Finally,　a　case　study　is　conducted　using　data　from　the　Beijing　Green　Exchange　to　verify　the　convergence　and　rationality　of　Quantum　amplitude　estimation　(QAE)　and　its　optimization　algorithm　in　CERO　pricing.　The　study　results　indicate　that　among　the　four　QAE　algorithms　adopted,　the　results　of　two　quantum　algorithms　are　close　to　the　traditional　method　and　have　good　convergence.　Compared　to　traditional　method,　the　study　solves　the　problem　of　difficult　parameter　determination　in　CERO　pricing　and　reduces　the　results　subjectivity.　However,　not　all　quantum　algorithms　are　suitable　for　CERO　pricing.　The　study　further　designed　various　carbon　emission　trading　decisions　among　enterprises　based　on　pricing　results,　providing　a　new　approach　for　the　management　and　operation　of　carbon　assets　in　enterprises.