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Finance is not the only industry looking for a way to profit from even the small, unstable quantum computers that mark the current state of the art; sectors from aerospace to pharmaceuticals are also hunting for a "quantum advantage". But there are reasons to think finance may be among the first to find it. Mike Biercuk of Q-CTRL, a startup that makes control software for quantum computers, points out that a new financial algorithm can be deployed faster than a new industrial process. The size of financial markets means that even a small advance would be worth a lot of money.
當前最先進的量子計算機規模小且不穩定，即便如此，試圖利用它獲利的行業并不局限在金融業，從航空航天到制藥等眾多行業都在尋求“量子優勢”，只不過我們有理由認為金融業是會捷足先登的行業之一。Q-CTRL是一家為量子計算機開發控制軟件的創業公司，其創始人邁克比埃庫克指出，部署一種新的金融算法可能比部署新的工業流程要快。金融市場的規模如此龐大，就算小小的進步也會價值千金。
Banks are also buying in expertise. Firms including BBVA, Citigroup, JPMorgan and Standard Chartered have set up research teams and signed deals with computing firms. The Boston Consulting Group reckons that, as of June, banks and insurers in America and Europe had hired more than 115 experts—a big number for what remains, even in academia, a small specialism. "We have more physics and maths PhDs than some big universities," jokes Alexei Kondratyev, head of data analytics at Standard Chartered.
銀行也在為專業技術掏腰包。包括西班牙對外銀行、花旗集團、摩根大通和渣打銀行在內的許多公司已經成立了研究團隊，并和計算公司簽署了協議。據波士頓咨詢集團估計，歐美的銀行和保險公司截至6月已經聘請了超過115名專家——鑒于量子研究即使在學術界也仍屬小眾，這個數字可謂相當驚人了。渣打銀行的數據分析負責人阿列克謝康德拉特耶夫打趣道：“我們這里的物理博士和數學博士比一些大型大學還要多。
Startups are exploring possibilities too. Enrique Lizaso of Multiverse Computing reckons his firm's quantum-enhanced algorithms can spot fraud more effectively, and around a hundred times faster, than existing ones. The firm has also experimented with portfolio optimisation, in which analysts seek well-performing investment strategies. Multiverse re-ran decisions made by real traders at a bank. The job was to choose, over the course of a year, the most profitable mix from a group of 50 assets, subject to restrictions, such as how often trades could be made.
創業公司也在試水。平行宇宙計算公司的恩里克·利薩索認為其公司利用經量子增強的算法可以更有效地甄別欺詐，速度比現有算法快了約100倍。該公司還測試了投資組合優化，即讓分析師尋求表現優異的投資策略。它重演了由一家銀行的真實交易員所做的決策。這項工作要求在交易頻率等限制條件下，于一年時間內從50項資產中選擇盈利能力最強的組合。
The result was a problem with around 101,300 possible solutions, a number that far outstrips the number of atoms in the visible universe. In reality, the bank's traders, assisted by models running on classical computers, managed an annual return of 19%. Depending on the amount of volatility investors were prepared to put up with, Multiverse's algorithm generated returns of 20-80%—though it stops short of claiming a definitive quantum advantage.
這樣就產生了一個約有10^1300個可能解的問題，這個數字遠遠超過了可見宇宙中的原子數量。現實中該銀行交易員在經典計算機上運行的模型的輔助下實現了19％的年回報率，而平行宇宙公司的算法在還沒有發揮絕對的量子優勢的前提下，根據投資者愿意承受的波動程度的不同產生了20％到80％的不等回報。
Not all potential uses are so glamorous. Monte Carlo simulations are often used in regulatory stress tests. Christopher Savoie of Zapata, a quantum-computing firm based in Boston, recalls one bank executive telling him: "Don't bring me trading algorithms, bring me a solution to CCAR (an American stress-test regulation). That stuff eats up half my computing budget."
并非所有的潛在應用都令人如此著迷。蒙特卡羅模擬常用于監管壓力測試。克里斯多夫·薩瓦來自一家總部位于波士頓的量子計算公司Zapata，他記得一位銀行高管曾對他說：“不用給我交易算法，能想辦法讓我通過CCAR（美國一項壓力測試法規）就行。這玩意兒吃掉了我一半的計算預算。”
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