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量子計(jì)算機(jī)還沒到來(lái),量子算法已投入使用

量子計(jì)算機(jī)還沒到來(lái),量子算法已投入使用

Jeremy Kahn 2019-07-21
不少研究人員都在嘗試用針對(duì)量子計(jì)算機(jī)設(shè)計(jì)的算法提高目前現(xiàn)有硬件的計(jì)算效率。

微軟公司的研究人員最近已經(jīng)用上了一種專門為目前還不存在的量子計(jì)算機(jī)設(shè)計(jì)的算法,以提高醫(yī)學(xué)成像的速度和質(zhì)量。

微軟表示,這種技術(shù)或?qū)⒛軌蚋纳茖?duì)乳腺癌和其他一些疾病的治療。比如它可以讓醫(yī)生幾天內(nèi)就確定腫瘤是否因?yàn)榛煻s小了,而不用像以往一樣等上幾周甚至幾個(gè)月。

量子計(jì)算機(jī)如果真被生產(chǎn)出來(lái)了,當(dāng)代的所謂超級(jí)計(jì)算機(jī)跟它一比,立即就成了算盤一樣的老古董。最近,有不少研究人員都在嘗試用針對(duì)量子計(jì)算機(jī)設(shè)計(jì)的算法提高目前現(xiàn)有硬件的計(jì)算效率。比如利用量子算法管理電網(wǎng)負(fù)載,改善交通擁擠的城市的快遞配送路線,控制投資組合的風(fēng)險(xiǎn)和回報(bào)等等。

提高醫(yī)學(xué)成像的質(zhì)量和效率

最近,微軟與位于克里夫蘭的凱斯西保留地大學(xué)的科學(xué)家一起展示了雙方的合作成果。凱斯西保留地大學(xué)十分擅長(zhǎng)一種叫做磁共振指紋(MRF)的技術(shù)。像更常見的磁共振成像(MRI)技術(shù)一樣,MRF也是靠強(qiáng)大的磁場(chǎng)和無(wú)線電波來(lái)生成人體內(nèi)部器官和軟組織的影像的。不過(guò)傳統(tǒng)的MRI技術(shù)只能夠識(shí)別明暗區(qū)域,放射科醫(yī)生必須加以一定的主觀推斷,而MRF則可以精確區(qū)分人體組織的類型,從而獲得更詳細(xì)和更容易解讀的影像。

凱斯西保留地大學(xué)的MRF項(xiàng)目負(fù)責(zé)人馬克·格里斯沃爾德將這項(xiàng)技術(shù)比作唱詩(shī)班的合唱,人體內(nèi)的組織好比歌手。在傳統(tǒng)的MRI技術(shù)中,整個(gè)唱詩(shī)班就好像在唱同一首歌,聽眾只能分辨出是否某人的聲音是否比別人略大或略小,音調(diào)是否比別人略高或略低,是否有人跑了調(diào)。而有了MRF技術(shù),整個(gè)唱詩(shī)班好像每個(gè)歌手都在各自唱一首不同的歌曲,聽眾就能夠?qū)⒛骋皇赘枨鷱钠渌曇糁凶R(shí)別出來(lái),并且用它定位到某一個(gè)歌手。

不過(guò),配置一臺(tái)掃描儀來(lái)找到某種特定的組織類型(也就是分離出某首特定的“歌曲”)是非常耗時(shí)的。但凱斯西保留地大學(xué)的科研人員發(fā)現(xiàn),在微軟量子算法的幫助下,他們只需要以前的三分之一到六分之一的時(shí)間就能夠完成掃描,同時(shí)將掃描的精度提高了25%以上。格里斯沃爾德表示:“精確度的提高是非常重要的,因?yàn)樗梢宰屛覀兛吹浇M織中越來(lái)越小的變化。”

微軟近年來(lái)一直在強(qiáng)調(diào)量子算法的潛力,這在一定程度上也是要給未來(lái)的量子計(jì)算機(jī)市場(chǎng)播下種子。同時(shí)微軟也高度重視用量子算法編寫的軟件。畢竟微軟的某些競(jìng)爭(zhēng)對(duì)手已經(jīng)搞出了量子計(jì)算機(jī)的原型機(jī),而微軟雖然在量子計(jì)算機(jī)領(lǐng)域也搞了好幾年的研發(fā),但迄今還沒有搞出什么花哨的硬件可以當(dāng)作噱頭來(lái)展示。

量子計(jì)算的崛起

量子計(jì)算機(jī)利用了量子力學(xué)特性來(lái)表達(dá)和處理信息。在傳統(tǒng)計(jì)算機(jī)中,信息是以二進(jìn)制處理的,又稱為比特,其值為0或1。每個(gè)比特的值獨(dú)立于計(jì)算中使用的其他所有比特。而在量子計(jì)算機(jī)中,信息是用量子比特表示的。量子比特可以用任何數(shù)量的具有量子特性的現(xiàn)象來(lái)創(chuàng)建(比如電子的自旋或光子的偏振)。

與比特不同,量子比特可以同時(shí)表示0和1。在某些情況下,甚至可以是0和1之間的任何值。更重要的是,每個(gè)量子比特的值都會(huì)影響到系統(tǒng)中其他量子比特的值。因此,量子計(jì)算機(jī)不必像傳統(tǒng)計(jì)算機(jī)一樣按順序運(yùn)算,而是具備了瞬間完成運(yùn)算的可能。這兩種特點(diǎn)讓量子計(jì)算機(jī)從理論上對(duì)傳統(tǒng)計(jì)算機(jī)形成了巨大的優(yōu)勢(shì)——量子計(jì)算機(jī)每增加一個(gè)量子比特,其性能就會(huì)呈指數(shù)型增長(zhǎng),而非線性增長(zhǎng)。一臺(tái)足夠大的量子計(jì)算機(jī)所能做的事情,就能夠遠(yuǎn)超當(dāng)今最大的超級(jí)計(jì)算機(jī)——比如找到更節(jié)能的化肥生產(chǎn)流程,或者破解全世界大多數(shù)數(shù)據(jù)的加密保護(hù)程序。

量子計(jì)算機(jī)一度只在科幻小說(shuō)里存在。然而在2011年,加拿大公司D-Wave Systems推出了全球第一臺(tái)商用量子計(jì)算機(jī)。(不過(guò),該量子計(jì)算機(jī)只能用于計(jì)算某些數(shù)學(xué)問(wèn)題的子集。)自此,IBM、谷歌和Rigetti Computing(加州伯克利的一家創(chuàng)業(yè)公司)等公司都研發(fā)了用于通用用途的量子計(jì)算機(jī),顧客已經(jīng)可以通過(guò)互聯(lián)網(wǎng)訪問(wèn)它們。英特爾還推出了幾款量子處理器,不過(guò)它們目前尚未被提供給商用客戶。

到目前為止,這些量子計(jì)算機(jī)都還沒有強(qiáng)大到可以做到傳統(tǒng)計(jì)算機(jī)做不到的事情,不過(guò)很多人認(rèn)為,谷歌可能已經(jīng)接近所謂“量子時(shí)代”的門檻了。不過(guò)即便果真如此,現(xiàn)階段對(duì)于大多數(shù)商業(yè)應(yīng)用來(lái)說(shuō),它的量子計(jì)算機(jī)依然太小,計(jì)算也很容易出錯(cuò)。

量子競(jìng)賽

在過(guò)去一年里,中國(guó)的阿里巴巴宣布將制造量子處理器,亞馬遜也悄悄聘請(qǐng)了一支量子計(jì)算專家團(tuán)隊(duì),表明它也在從事量子計(jì)算機(jī)的研發(fā)。另外,從事量子計(jì)算機(jī)硬件研發(fā)的初創(chuàng)公司少說(shuō)也得有六七家。

微軟的首席執(zhí)行官薩蒂亞·納德拉已經(jīng)將量子計(jì)算稱為三大突破性技術(shù)之一——其他兩個(gè)分別是增強(qiáng)現(xiàn)實(shí)技術(shù)和人工智能技術(shù),這三大技術(shù)也對(duì)微軟未來(lái)的發(fā)展至關(guān)重要。在他的領(lǐng)導(dǎo)下,微軟對(duì)量子技術(shù)也算下了血本。它從全球各地招募了一支由物理學(xué)家、數(shù)學(xué)家、計(jì)算機(jī)科學(xué)家和工程師組成的團(tuán)隊(duì),并任命了一名在工程領(lǐng)域最有經(jīng)驗(yàn)的老將——Xbox游戲機(jī)和HoloLens混合現(xiàn)實(shí)頭盔項(xiàng)目的負(fù)責(zé)人托德·霍姆達(dá)爾來(lái)負(fù)責(zé)量子計(jì)算項(xiàng)目。

不過(guò),微軟為量子計(jì)算機(jī)的量子比特選擇了一種以前從未測(cè)試過(guò)的架構(gòu),該架構(gòu)基于一種非常難以捉摸的亞原子粒子,物理學(xué)家直到2017年前,都不能100%的確定該粒子的存在。與IBM、谷歌和Rigetti公司的架構(gòu)相比,微軟使用的這些亞原子粒子組成了發(fā)辮狀,這種形狀應(yīng)該會(huì)讓它們更加穩(wěn)定,從而更不容易受到周圍電磁力的沖擊干擾。這種沖擊干擾會(huì)導(dǎo)致量子計(jì)算機(jī)產(chǎn)生計(jì)算錯(cuò)誤,必須加以糾正。由于這種架構(gòu)理論上的犯錯(cuò)率更低,微軟的設(shè)計(jì)對(duì)商業(yè)應(yīng)用來(lái)說(shuō)應(yīng)該是更安全的選擇。但是,微軟首先要證明的是,它可以可靠地創(chuàng)造這種辮狀結(jié)構(gòu),然后用它們形成量子比特——不過(guò)這一點(diǎn)它至今還沒有做到。

與此同時(shí),微軟也有一群數(shù)學(xué)家和計(jì)算機(jī)科學(xué)家正在研究針對(duì)量子計(jì)算機(jī)的編程方法。事實(shí)證明,一些利用量子計(jì)算機(jī)的奇異特性開發(fā)的算法,也可以在普通計(jì)算機(jī)上體現(xiàn)出很大的優(yōu)勢(shì)。

定制算法

格里斯沃爾德表示,MRF技術(shù)的難點(diǎn),在于如何控制掃描儀傳輸?shù)臒o(wú)線電脈沖的強(qiáng)度、頻率和角度。找到正確的脈沖模式,是掃描儀識(shí)別不同組織類型的關(guān)鍵——用格里斯沃爾德的比喻來(lái)說(shuō),就是分離出唱詩(shī)班里每個(gè)歌手所唱的歌曲。他表明,有一種數(shù)學(xué)上的優(yōu)化模式,可以讓掃描儀僅提取某一固定的組織類型,精確度甚至可以達(dá)到提取單個(gè)細(xì)胞的級(jí)別。但是要找到這種模式,則涉及太多的變量,靠傳統(tǒng)計(jì)算機(jī)的運(yùn)算能力無(wú)法達(dá)到。因此,研究人員以往基本是完全靠猜測(cè)來(lái)計(jì)劃每次掃描的脈沖模式。不過(guò)即便是用這種不完美的方法,MRF仍然能夠得到比普通的MRI更詳細(xì)的圖像。

格里斯沃爾德表示,為了取得進(jìn)一步的進(jìn)展,他需要超越人的直覺。他的團(tuán)隊(duì)申請(qǐng)了一筆撥款,用于研究如何使用傳統(tǒng)算法技術(shù)優(yōu)化MRF掃描技術(shù),但是申請(qǐng)卻被拒絕了,理由是要解決這樣一個(gè)數(shù)學(xué)難題根本就是不可能的。

后來(lái)格里斯沃爾德聽說(shuō)微軟正在尋找合作伙伴,為量子算法創(chuàng)建類似的演示案例——微軟曾經(jīng)與凱斯西保留地大學(xué)的醫(yī)學(xué)影像專家密切合作,對(duì)它開發(fā)的HoloLens增強(qiáng)現(xiàn)實(shí)眼鏡進(jìn)行測(cè)試。近20年來(lái),格里斯沃爾德本人也一直在密切關(guān)注量子計(jì)算的發(fā)展,而且認(rèn)識(shí)一些為微軟工作的研究人員,他意識(shí)到,自己的機(jī)會(huì)可能來(lái)了。

參與了MRF項(xiàng)目的微軟量子計(jì)算項(xiàng)目研究人員馬蒂亞斯·特羅耶表示:“我們喜歡看似不可能的問(wèn)題?!备匾氖?,優(yōu)化MRF算法的挑戰(zhàn)雖然看似不可能,但是針對(duì)這種問(wèn)題的量子算法卻已經(jīng)被設(shè)計(jì)出來(lái)了。

特羅耶表示,針對(duì)格里斯沃爾德提出的需求,現(xiàn)有的量子算法必須要做出一些調(diào)整。“我們想強(qiáng)調(diào)的是,要想真正充分利用量子優(yōu)化器的功能,就必須定制一個(gè)專門的解決方案?!碧亓_耶表示,對(duì)于MRF來(lái)說(shuō),最難的部分就是從構(gòu)建MRF圖像所涉及的數(shù)千個(gè)變量中,找出算法應(yīng)該優(yōu)化哪些因素的子集。他表示,只要這樣做了,“一開始不可能的事情就開始變?yōu)榭赡??!?/p>

他還表示,雖然在傳統(tǒng)計(jì)算機(jī)上運(yùn)行量子算法,可以顯著提高M(jìn)RF掃描的速度和精度,但如果是在一臺(tái)足夠大的量子計(jì)算機(jī)上運(yùn)行,結(jié)果會(huì)更加令人振奮?!澳菢訒?huì)快得多?!彼f(shuō)。

不過(guò),特羅耶所說(shuō)的那種量子計(jì)算機(jī)需要大約需要100萬(wàn)個(gè)邏輯量子比特。要想造出那么大的量子計(jì)算機(jī),可能還得幾十年甚至更久。(財(cái)富中文網(wǎng))

譯者:樸成奎

computer to enhance the speed and quality of medical imaging.

The advance may one day improve the treatment of breast cancer and other diseases, the company says. For instance, it might allow doctors to determine within days whether a tumor is shrinking in response to chemotherapy, rather than having to wait weeks or months.

The development is one of a number of recent cases in which researchers have used algorithms designed for future quantum computers, machines that would make today’s supercomputers look like abacuses, to improve calculations running on today’s existing hardware. Other examples include using quantum algorithms to find better ways to manage the load across an electrical grid, improve delivery routes in a crowded city, and control risks and returns in an investment portfolio.

Better medical scans, more quickly

In the most recent illustration, Microsoft worked with scientists at Case Western Reserve University in Cleveland, who specialize in a type of medical imaging called magnetic resonance fingerprinting (or MRF.) Like the more familiar magnetic resonance imaging (MRI), the technique uses powerful magnetic fields and radio waves to create images of internal organs and soft-tissue. But while traditional MRIs can only identify areas of light or dark, which a radiologist must then subjectively evaluate, MRF can differentiate precisely between tissue types, allowing for more detailed and interpretable images.

Mark Griswold, a pioneer in MRF at Case Western Reserve who led the project, likes to use the analogy of trying to listen to a choir, where the tissues in the body are the singers: With a conventional MRI, it is as though the entire choir is all singing the same song, and the listener can only determine if one singer is a bit louder or softer than others, a bit higher or lower pitched, and maybe if they are out of tune. With MRF, on the other hand, it is like listening to a choir in which each singer has his or her own unique song, and the listener is able to isolate that song from the other voices in the choir and use it to identify the singer.

Configuring a scanner to find a particular tissue type—to isolate those individual songs—is time-consuming. With help from Microsoft’s quantum algorithm, the Case researchers found they could produce the scans in one third to one sixth of the time it took previously, while simultaneously boosting the precision of the scans by more than 25%. “The increase in precision is really important because it allows us to see smaller and smaller changes in the tissue,” Griswold says.

Microsoft has been highlighting the potential of quantum algorithms in part to seed the market for its future quantum computer. But it has also been emphasizing its quantum-inspired software because, unlike some rivals, it doesn’t yet have any fancy quantum hardware to show off, despite years of development.

The rise of quantum computing

Quantum computers use quantum mechanical properties to represent and manipulate information. In a conventional computer, information is processed in a binary format called bits, which have a value of either 0 or 1. The value of each bit is independent from all the other bits being used in the calculation. In a quantum computer, information is represented using quantum bits, or qubits. These qubits can be created using any number of phenomena that have quantum properties (for instance, the spin of electrons or the polarization of photons).

Unlike bits, qubits can represent both a 0 and a 1 at the same time—or in some cases, any value between 0 and 1. What's more, the value of each qubit affects the value of other qubits in the system, opening the door to nearly instantaneous solutions instead of having to process information in a serial fashion. These two factors, in theory, give quantum computers an enormous advantage over conventional ones: Each additional qubit added to a quantum computer increase its power not linearly, but exponentially. A sufficiently large quantum computer ought to be able to do things that are beyond the ability of even today’s biggest supercomputers—like find much more energy efficient processes for manufacturing fertilizer or break the encryption that protects much of the world’s data.

Quantum computers were once the stuff of sci-fi novels. But in 2011, D-Wave Systems, a Canadian company, debuted the first commercially available quantum computer. (Its machine, however, can only be used for a certain sub-set of mathematical problems.) Since then, IBM, Google, and Rigetti Computing, a Berkeley, Calif.-based startup, have all built more general-purpose quantum computers that customers can access over the Internet. Meanwhile, Intel has unveiled quantum processors, although these are not yet available to commercial customers.

So far, none of these quantum computers are powerful enough to do something a conventional computer can’t, although it is believed Google may be close to crossing this threshold, which is known as “quantum supremacy.” Even when that happens, the quantum machines will still be too small and their calculations too prone to errors to be useful for most commercial applications.

The corporate quantum race

In the past year, Chinese company Alibaba has announced that it would build a quantum processor, Amazon has quietly hired a team of quantum computing experts, signaling it too may be building a machine, while at least a half dozen startups are also working on quantum hardware.

At Microsoft, CEO Satya Nadella has described quantum computing as one of three groundbreaking technologies —along with augmented reality and artificial intelligence—that will be essential to the company’s future. Under his leadership, the company has made a big bet on quantum: hiring a team of physicists, mathematicians, computer scientists and engineers from around the globe and placing one of its most experienced engineering executives, Todd Holmdahl, a veteran of both the Xbox game console and the HoloLens mixed reality headset, in charge of the effort.

The company has chosen an untested architecture for the qubits of its quantum computer, based on an elusive sub-atomic particle physicists weren’t even 100% sure existed until 2017. Those sub-atomic particles form a braid, and this shape should make them much more stable and less susceptible to buffeting interference from surrounding electromagnetic forces than those being used by IBM, Google, and Rigetti. That buffeting creates errors in a quantum computer’s calculations, which then have to be corrected. With a theoretically lower error rate, Microsoft’s design ought to be a safer bet for commercial applications. But, first, the company has to prove it can reliably create these braids and use them to form qubits—something it hasn’t yet done.

In the meantime, Microsoft has a whole group of mathematicians and computer scientists looking at ways to program quantum computers. And, as it turns out, some of the algorithms developed to take advantage of the weird properties of quantum computers can also be used to great advantage on normal ones.

A custom algorithm

With MRF, the trick is figuring out exactly how to tune the strength, frequency, and angle of the radio pulses the scanner transmits, Griswold says. Finding the right pulse pattern is what enables the scanner to identify tissue types—to isolate the song of each singer in the choir to use Griswold’s analogy. There is a mathematically optimal pattern that would allow the scanner to pick up only that tissue type with a precision down to the individual cell—but finding it involves so many variables that it is beyond the computational power of a conventional computer, he says. So researchers have relied almost entirely on educated guesswork to plan the pattern of pulses for each scan, he says. Even with this imperfect method, he says, MRF still results in much more detailed images than a typical MRI.

To get further improvements, Griswold says, he needed to get beyond human intuition. But when his team applied for a grant to research how to optimize the MRF scans using conventional algorithmic techniques, the application was rejected on the grounds that solving such a mathematically-challenging problem was simply impossible.

Then Griswold heard that Microsoft, which had worked closely with medical imaging experts at Case on a test case for its HoloLens augmented reality goggles, was looking for partners to create similar demonstration cases for quantum algorithms. Griswold, who had followed quantum computing developments closely for 20 years and knew some of the researchers now working on Microsoft's efforts, realized this might be his chance.

“We like problems that are seemingly impossible,” Matthias Troyer, Microsoft quantum computing researcher who worked on the MRF project, says. What's more, Troyer, says, MRF was the kind of seemingly impossible problem—an optimization challenge—for which quantum algorithms already existed.

Troyer says the existing quantum algorithm, however, had to be tweaked for Griswold’s exact problem. “What we like to stress it to really get the full power of the quantum optimizer, one really has to make a bespoke solution,” he says. In this case, Troyer says, the hard part was figuring out, from the several thousand variables involved in building an MRF image, which subset of factors the algorithm should try to optimize. Once you do this, he says, “the initially impossible begins to look possible.”

He also says that even though running the quantum algorithm on a conventional computer resulted in a significant increase in the speed and precision of the MRF scans, the results would have been even more impressive on a large-enough quantum computer. “It would have been much faster,” he says.

But the size quantum computer Troyer is talking about would require about one million logical qubits. And machines of that size are still many years, if not a few decades, away.

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