咨詢公司KPMG與丹麥工業(yè)大學(xué)（DTU）的一個(gè)研究團(tuán)隊(duì)和一家未給出名字的歐洲銀行合作，試圖利用量子計(jì)算來決定股票的買入和沽出，目的是獲得最大回報(bào)，這是一宗古老的銀行業(yè)務(wù)，名為投資組合優(yōu)化。
Consultancy firm KPMG, together with a team of researchers from the Technical University of Denmark (DTU) and a yet-to-be-named European bank, has been piloting the use of quantum computing to determine which stocks to buy and sell for maximum return, an age-old banking operation known as portfolio optimization.
咨詢公司KPMG與丹麥工業(yè)大學(xué)（DTU）的一個(gè)研究團(tuán)隊(duì)和一家未給出名字的歐洲銀行合作，試圖利用量子計(jì)算來決定股票的買入和沽出，目的是獲得最大回報(bào)，這是一宗古老的銀行業(yè)務(wù)，名為投資組合優(yōu)化。
The researchers ran a model for portfolio optimization on Canadian company D-Wave's 2,000-qubit quantum annealing processor, comparing the results to those obtained with classical means. They found that the quantum annealer performed better and faster than other methods, while being capable of resolving larger problems – although the study also indicated that D-Wave's technology still comes with some issues to do with ease of programming and scalability.
這些研究人員在加拿大公司D-Wave的2000位量子退火處理器上運(yùn)行了某個(gè)投資組合優(yōu)化模型，并將結(jié)果與用經(jīng)典手段獲得的結(jié)果進(jìn)行比較。他們發(fā)現(xiàn)，量子退火器的性能比其他方法更好及更快，同時(shí)還能夠解決更大的問題，盡管該研究也表明，D-Wave的技術(shù)仍然存在一些問題，例如有關(guān)編程便利性和可擴(kuò)展性方面的問題。
研究人員在加拿大公司D-Wave的2000位量子退火處理器上運(yùn)行投資組合優(yōu)化模型。（圖：D-Wave）
The smart distribution of portfolio assets is a problem that stands at the very heart of banking. Theorized by economist Harry Markowitz as early as 1952, it consists of allocating a fixed budget to a collection of financial assets in a way that will produce as much return as possible over time. In other words, it is an optimization problem: an investor should look to maximize gain and minimize risk for a given financial portfolio.
證券資產(chǎn)的巧妙分配是占據(jù)銀行業(yè)務(wù)核心的問題。經(jīng)濟(jì)學(xué)家Harry Markowitz早在1952年就將該問題進(jìn)行理論化，證券資產(chǎn)分配包括將固定的預(yù)算在金融資產(chǎn)集合上配給，令其在一段時(shí)間內(nèi)產(chǎn)生盡可能多的回報(bào)。換句話說，是個(gè)優(yōu)化問題：投資者應(yīng)該設(shè)法在給定的金融組合里實(shí)現(xiàn)收益最大化和風(fēng)險(xiǎn)最小化。
As the number of assets in the portfolio multiplies, the difficulty of the calculation exponentially increases, and the problem can quickly become intractable, even to the world's largest supercomputers. Quantum computing, on the other hand, offers the possibility of running multiple calculations at once thanks to a special quantum state that is adopted by quantum bits, or qubits.
投資組合的資產(chǎn)數(shù)量成倍增加時(shí)，計(jì)算的難度也會(huì)隨著成倍增加，最后很快可能變成即使用上世界上最大的超級(jí)計(jì)算機(jī)也解決不了的優(yōu)化問題。量子計(jì)算由于量子比特（或稱Qubits）采用了特殊的量子狀態(tài)可以同時(shí)進(jìn)行多次的計(jì)算。
Quantum systems, for now, cannot support enough qubits to have a real-world impact. But in principle, large-scale quantum computers could one day solve complex portfolio optimization problems in a matter of minutes – which is why the world's largest banks are already putting their research team to work on developing quantum algorithms.
量子系統(tǒng)目前還未能支持足夠的量子比特，還不足以對(duì)現(xiàn)實(shí)世界產(chǎn)生影響。但一般而言，大規(guī)模量子計(jì)算機(jī)有朝一日將可以在幾分鐘內(nèi)解決各種復(fù)雜的投資組合優(yōu)化問題，這也是為什么世界上最大的銀行都已經(jīng)在組建研究團(tuán)隊(duì)開發(fā)量子算法。
To translate Markowitz's classical model for the portfolio selection problem into a quantum algorithm, the DTU's researchers formulated the equation into a quantum model called a quadratic unconstrained binary optimization (QUBO) problem, which they based on the usual criteria used for the operation such as budget and expected return.
DTU的研究人員為了將Markowitz的投資組合選擇問題的經(jīng)典模型轉(zhuǎn)化為量子算法，他們先將該方程表述為一個(gè)名為為二次無約束二元優(yōu)化（QUBO）問題的量子模型，然后再在預(yù)算和預(yù)期收益等參數(shù)的基礎(chǔ)上利用QUBO進(jìn)行通常的操作。
When deciding which quantum hardware to pick to test their model, the team was faced with a number of options: IBM and Google are both working on a superconducting quantum computer, while Honeywell and IonQ are building trapped-ion devices; Xanadu is looking at photonic quantum technologies, and Microsoft is creating a topological quantum system.
DTU團(tuán)隊(duì)在決定選量子硬件測試他們的模型時(shí)也面臨許多選擇。IBM和谷歌都在研究超導(dǎo)量子計(jì)算機(jī)，Honeywell和IonQ則在打造陷離子（Trapped-ion）設(shè)備，而Xanadu則在研究光子量子技術(shù)，微軟也在創(chuàng)建拓?fù)淞孔酉到y(tǒng)。
D-Wave's quantum annealing processor is yet another approach to quantum computing. Unlike other systems, which are gate-based quantum computers, it is not possible to control the qubits in a quantum annealer; instead, D-Wave's technology consists of manipulating the environment surrounding the system, and letting the device find a "ground state". In this case, the ground state corresponds to the most optimal portfolio selection.
D-Wave的量子退火處理器是量子計(jì)算的另一種方法。量子退火處理器與其他系統(tǒng)不同，其他系統(tǒng)是基于門的量子計(jì)算機(jī)，不能控制量子退火器里的量子比特，而D-Wave的技術(shù)則包括操縱系統(tǒng)周圍的環(huán)境，使得設(shè)備可以找到一個(gè) "基底狀態(tài)"。在投資組合選擇的情況下，基底狀態(tài)對(duì)應(yīng)的是最優(yōu)化的組合選擇。
This approach, while limiting the scope of the problems that can be resolved by a quantum annealer, also enable D-Wave to work with many more qubits than other devices. The company's latest device counts 5,000 qubits, while IBM's quantum computer, for example, supports less than 100 qubits.
D-Wave這種方法雖然限制了量子退火器所能解決的問題范圍，但也令D-Wave能夠比其他設(shè)備多出許多量子比特。D-Wave公司最新的設(shè)備可達(dá)5000量子比特，IBM的量子計(jì)算機(jī)支持的量子比特不到100。
The researchers explained that the maturity of D-Wave's technology prompted them to pick quantum annealing to trial the algorithm; and equipped with the processor, they were able to embed and run the problem for up to 65 assets.
研究人員解釋稱，D-Wave技術(shù)的成熟度促使他們選擇了量子退火進(jìn)行該算法的試用；他們利用配備的退火處理器能夠嵌入并運(yùn)行含多達(dá)65個(gè)資產(chǎn)的問題。
To benchmark the performance of the processor, they also ran the Markowitz equation with classical means, called brute force. With the computational resources at their disposal, brute force could only be used for up to 25 assets, after which the problem became intractable for the method.
他們?yōu)榱藴y試處理器的性能還用經(jīng)典方法運(yùn)行了Markowitz方程，即所謂的蠻力法。在他們所掌握的計(jì)算資源下，蠻力法最多只能用于解決25個(gè)資產(chǎn)的問題，再多蠻力法就難以解決了。
Comparing between the two methods, the scientists found that the quality of the results provided by D-Wave's processor was equal to that delivered by brute force – proving that quantum annealing can reliably be used to solve the problem. In addition, as the number of assets grew, the quantum processor overtook brute force as the fastest method.
科學(xué)家們?cè)趦煞N方法之間進(jìn)行比較后發(fā)現(xiàn)，D-Wave的處理器提供的結(jié)果質(zhì)量與蠻力法提供的結(jié)果質(zhì)量不差上下，這證明了量子退火可以可靠地用于解決這個(gè)問題。此外，隨著資產(chǎn)數(shù)量的增加，量子處理器超過蠻力法成了最快的方法。
From 15 assets onwards, D-Wave's processor effectively started showing significant speed-up over brute force, as the problem got closer to becoming intractable for the classical computer.
在資產(chǎn)達(dá)到15個(gè)或更多時(shí)，D-Wave的處理器的有效速度明顯開始快過蠻力法，15個(gè)資產(chǎn)優(yōu)化問題接近經(jīng)典計(jì)算機(jī)的不可解局限。
To benchmark the performance of the quantum annealer for more than 25 assets – which is beyond the capability of brute force – the researchers compared the results obtained with D-Wave's processor to those obtained with a method called simulated annealing. There again, shows the study, the quantum processor provided high-quality results.
為了對(duì)量子退火器的性能在資產(chǎn)超過25時(shí)（蠻力法已力不能及）進(jìn)行基準(zhǔn)測試，研究人員還比較了使用D-Wave處理器獲得的結(jié)果與另外一種使用名為模擬退火的方法獲得的結(jié)果。這里的研究也表明，D-Wave量子處理器提供了高質(zhì)量結(jié)果。
Although the experiment suggests that quantum annealing might show a computational advantage over classical devices, therefore, Ulrich Busk Hoff, researcher at DTU, who participated in the research, warns against hasty conclusions.
雖然實(shí)驗(yàn)表明量子退火可能顯示了比經(jīng)典器件更強(qiáng)的計(jì)算優(yōu)勢，但參與了該項(xiàng)研究的DTU研究員Ulrich Busk Hoff表示要謹(jǐn)慎，不能草率地就此下結(jié)論。
"For small-sized problems, the D-Wave quantum annealer is indeed competitive, as it offers a speed-up and solutions of high quality," he tells ZDNet. "That said, I believe that the study is premature for making any claims about an actual quantum advantage, and I would refrain from doing that. That would require a more rigorous comparison between D-Wave and classical methods – and using the best possible classical computational resources, which was far beyond the scope of the project."
他告訴記者，" D-Wave量子退火器對(duì)于小一點(diǎn)的問題確實(shí)具有競爭力，因?yàn)榱孔油嘶鹌魈峁┝烁焖俣群透哔|(zhì)量的解決方案。但話又說回來，我認(rèn)為這項(xiàng)研究要對(duì)于實(shí)際的量子優(yōu)勢做出任何結(jié)論還為時(shí)過早，我會(huì)避免做出結(jié)論。要最后做結(jié)論的話需要在D-Wave和經(jīng)典方法之間進(jìn)行更嚴(yán)格的比較，得用上最好的經(jīng)典計(jì)算資源，這已經(jīng)遠(yuǎn)遠(yuǎn)超出了這個(gè)項(xiàng)目的范圍。"
DTU's team also flagged some scalability issues, highlighting that as the portfolio size increased, there was a need to fine-tune the quantum model's parameters in order to prevent a drop in results quality. "As the portfolio size was increased, a degradation in the quality of the solutions found by quantum annealing was indeed observed," says Hoff. "But after optimization, the solutions were still competitive and were more often than not able to beat simulated annealing."
DTU團(tuán)隊(duì)還提出了一些可擴(kuò)展性問題，特別是隨著組合規(guī)模的增加，量子模型的參數(shù)需要進(jìn)行微調(diào)后才能防止結(jié)果質(zhì)量的下降。Hoff表示，"隨著組合規(guī)模的增加，量子退火法找到的解確實(shí)出現(xiàn)質(zhì)量上的下降。但經(jīng)優(yōu)化后解決方案仍然具有競爭力，更多的時(shí)候仍然能夠擊敗模擬退火。"
In addition, with the quantum industry still largely in its infancy, the researchers pointed to the technical difficulties that still come with using quantum technologies. Implementing quantum models, they explained, requires a new way of thinking; translating classical problems into quantum algorithms is not straightforward, and even D-Wave's fairly accessible software development kit cannot be described yet as "plug-and-play".
此外，由于量子產(chǎn)業(yè)在很大程度上仍處于起步階段，研究人員指出，使用量子技術(shù)在操作上還存在一些困難。他們解釋表示，實(shí)現(xiàn)量子模型需要一種新的思維方式；要將經(jīng)典問題轉(zhuǎn)化為量子算法并不容易，即便是D-Wave已經(jīng)相當(dāng)方便的軟件開發(fā)工具包都也還不能說可以 "即插即用"。
The Canadian company's quantum processor nevertheless shows a lot of promise for solving problems such as portfolio optimization. Although the researchers shared doubts that quantum annealing would have as much of an impact as large-scale gate-based quantum computers, they pledged to continue to explore the capabilities of the technology in other fields.
不過，這家加拿大公司的量子處理器在解決投資組合優(yōu)化等問題上表現(xiàn)出了很好的前景。盡管研究人員對(duì)于量子退火是否會(huì)像大規(guī)�；陂T的量子計(jì)算機(jī)那樣產(chǎn)生巨大影響表示懷疑，但他們表示一定會(huì)繼續(xù)探索該技術(shù)在其他領(lǐng)域的應(yīng)用。
"I think it's fair to say that D-Wave is a competitive candidate for solving this type of problem and it is certainly worthwhile further investigation," says Hoff.
Hoff表示，"我認(rèn)為可以公平地說，D-Wave是解決這一類問題的有力競爭者，當(dāng)然也值得進(jìn)一步研究。"
KPMG, DTU's researchers and large banks are far from alone in experimenting with D-Wave's technology for near-term applications of quantum computing. For example, researchers from pharmaceutical company GlaxoSmithKline (GSK) recently trialed the use of different quantum methods to sequence gene expression, and found that quantum annealing could already compete against classical computers to start addressing life-sized problems.
利用D-Wave技術(shù)試驗(yàn)量子計(jì)算近期應(yīng)用的遠(yuǎn)不止KPMG、DTU的研究人員和大型銀行。例如，制藥公司GlaxoSmithKline (GSK)的研究人員最近在用不同的量子方法對(duì)基因表達(dá)進(jìn)行測序的試驗(yàn)，也發(fā)現(xiàn)量子退火在一些現(xiàn)實(shí)世界問題上已經(jīng)可以與經(jīng)典計(jì)算機(jī)一較高低了。
第5期 2021/03/31
