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量子計算機還沒到來,量子算法已投入使用

Jeremy Kahn 2019年07月21日

不少研究人員都在嘗試用針對量子計算機設計的算法提高目前現有硬件的計算效率。

微軟公司的研究人員最近已經用上了一種專門為目前還不存在的量子計算機設計的算法,以提高醫學成像的速度和質量。

微軟表示,這種技術或將能夠改善對乳腺癌和其他一些疾病的治療。比如它可以讓醫生幾天內就確定腫瘤是否因為化療而縮小了,而不用像以往一樣等上幾周甚至幾個月。

量子計算機如果真被生產出來了,當代的所謂超級計算機跟它一比,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量子計算機設計的算法提高目前現有硬件的計算效率。比如利用量子算法管理電網負載,改善交通擁擠的城市的快遞配送路線,控制投資組合的風險和回報等等。

提高醫學成像的質量和效率

最近,微軟與位于克里夫蘭的凱斯西保留地大學的科學家一起展示了雙方的合作成果。凱斯西保留地大學十分擅長一種叫做磁共振指紋(MRF)的技術。像更常見的磁共振成像(MRI)技術一樣,MRF也是靠強大的磁場和無線電波來生成人體內部器官和軟組織的影像的。不過傳統的MRI技術只能夠識別明暗區域,放射科醫生必須加以一定的主觀推斷,而MRF則可以精確區分人體組織的類型,從而獲得更詳細和更容易解讀的影像。

凱斯西保留地大學的MRF項目負責人馬克·格里斯沃爾德將這項技術比作唱詩班的合唱,人體內的組織好比歌手。在傳統的MRI技術中,整個唱詩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,音調是否比別人略高或略低,是否有人跑了調。而有了MRF技術,整個唱詩班好像每個歌手都在各自唱一首不同的歌曲,聽眾就能夠將某一首歌曲從其它聲音中識別出來,并且用它定位到某一個歌手。

不過,配置一臺掃描儀來找到某種特定的組織類型(也就是分離出某首特定的“歌曲”)是非常耗時的。但凱斯西保留地大學的科研人員發現,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間就能夠完成掃描,同時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,因為它可以讓我們看到組織中越來越小的變化。”

微軟近年來一直在強調量子算法的潛力,這在一定程度上也是要給未來的量子計算機市場播下種子。同時微軟也高度重視用量子算法編寫的軟件。畢竟微軟的某些競爭對手已經搞出了量子計算機的原型機,而微軟雖然在量子計算機領域也搞了好幾年的研發,但迄今還沒有搞出什么花哨的硬件可以當作噱頭來展示。

量子計算的崛起

量子計算機利用了量子力學特性來表達和處理信息。在傳統計算機中,信息是以二進制處理的,又稱為比特,其值為0或1。每個比特的值獨立于計算中使用的其他所有比特。而在量子計算機中,信息是用量子比特表示的。量子比特可以用任何數量的具有量子特性的現象來創建(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,每個量子比特的值都會影響到系統中其他量子比特的值。因此,量子計算機不必像傳統計算機一樣按順序運算,而是具備了瞬間完成運算的可能。這兩種特點讓量子計算機從理論上對傳統計算機形成了巨大的優勢——量子計算機每增加一個量子比特,其性能就會呈指數型增長,而非線性增長。一臺足夠大的量子計算機所能做的事情,就能夠遠超當今最大的超級計算機——比如找到更節能的化肥生產流程,或者破解全世界大多數數據的加密保護程序。

量子計算機一度只在科幻小說里存在。然而在2011年,加拿大公司D-Wave Systems推出了全球第一臺商用量子計算機。(不過,該量子計算機只能用于計算某些數學問題的子集。)自此,IBM、谷歌和Rigetti Computing(加州伯克利的一家創業公司)等公司都研發了用于通用用途的量子計算機,顧客已經可以通過互聯網訪問它們。英特爾還推出了幾款量子處理器,不過它們目前尚未被提供給商用客戶。

到目前為止,這些量子計算機都還沒有強大到可以做到傳統計算機做不到的事情,不過很多人認為,谷歌可能已經接近所謂“量子時代”的門檻了。不過即便果真如此,現階段對于大多數商業應用來說,它的量子計算機依然太小,計算也很容易出錯。

量子競賽

在過去一年里,中國的阿里巴巴宣布將制造量子處理器,亞馬遜也悄悄聘請了一支量子計算專家團隊,表明它也在從事量子計算機的研發。另外,從事量子計算機硬件研發的初創公司少說也得有六七家。

微軟的首席執行官薩蒂亞·納德拉已經將量子計算稱為三大突破性技術之一——其他兩個分別是增強現實技術和人工智能技術,這三大技術也對微軟未來的發展至關重要。在他的領導下,微軟對量子技術也算下了血本。它從全球各地招募了一支由物理學家、數學家、計算機科學家和工程師組成的團隊,并任命了一名在工程領域最有經驗的老將——Xbox游戲機和HoloLens混合現實頭盔項目的負責人托德·霍姆達爾來負責量子計算項目。

不過,微軟為量子計算機的量子比特選擇了一種以前從未測試過的架構,該架構基于一種非常難以捉摸的亞原子粒子,物理學家直到2017年前,都不能100%的確定該粒子的存在。與IBM、谷歌和Rigetti公司的架構相比,微軟使用的這些亞原子粒子組成了發辮狀,這種形狀應該會讓它們更加穩定,從而更不容易受到周圍電磁力的沖擊干擾。這種沖擊干擾會導致量子計算機產生計算錯誤,必須加以糾正。由于這種架構理論上的犯錯率更低,微軟的設計對商業應用來說應該是更安全的選擇。但是,微軟首先要證明的是,它可以可靠地創造這種辮狀結構,然后用它們形成量子比特——不過這一點它至今還沒有做到。

與此同時,微軟也有一群數學家和計算機科學家正在研究針對量子計算機的編程方法。事實證明,一些利用量子計算機的奇異特性開發的算法,也可以在普通計算機上體現出很大的優勢。

定制算法

格里斯沃爾德表示,MRF技術的難點,在于如何控制掃描儀傳輸的無線電脈沖的強度、頻率和角度。找到正確的脈沖模式,是掃描儀識別不同組織類型的關鍵——用格里斯沃爾德的比喻來說,就是分離出唱詩班里每個歌手所唱的歌曲。他表明,有一種數學上的優化模式,可以讓掃描儀僅提取某一固定的組織類型,精確度甚至可以達到提取單個細胞的級別。但是要找到這種模式,則涉及太多的變量,靠傳統計算機的運算能力無法達到。因此,研究人員以往基本是完全靠猜測來計劃每次掃描的脈沖模式。不過即便是用這種不完美的方法,MRF仍然能夠得到比普通的MRI更詳細的圖像。

格里斯沃爾德表示,為了取得進一步的進展,他需要超越人的直覺。他的團隊申請了一筆撥款,用于研究如何使用傳統算法技術優化MRF掃描技術,但是申請卻被拒絕了,理由是要解決這樣一個數學難題根本就是不可能的。

后來格里斯沃爾德聽說微軟正在尋找合作伙伴,為量子算法創建類似的演示案例——微軟曾經與凱斯西保留地大學的醫學影像專家密切合作,對它開發的HoloLens增強現實眼鏡進行測試。近20年來,格里斯沃爾德本人也一直在密切關注量子計算的發展,而且認識一些為微軟工作的研究人員,他意識到,自己的機會可能來了。

參與了MRF項目的微軟量子計算項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題。”更重要的是,優化MRF算法的挑戰雖然看似不可能,但是針對這種問題的量子算法卻已經被設計出來了。

特羅耶表示,針對格里斯沃爾德提出的需求,現有的量子算法必須要做出一些調整。“我們想強調的是,要想真正充分利用量子優化器的功能,就必須定制一個專門的解決方案。”特羅耶表示,對于MRF來說,最難的部分就是從構建MRF圖像所涉及的數千個變量中,找出算法應該優化哪些因素的子集。他表示,只要這樣做了,“一開始不可能的事情就開始變為可能。”

他還表示,雖然在傳統計算機上運行量子算法,可以顯著提高MRF掃描的速度和精度,但如果是在一臺足夠大的量子計算機上運行,結果會更加令人振奮。“那樣會快得多。”他說。

不過,特羅耶所說的那種量子計算機需要大約需要100萬個邏輯量子比特。要想造出那么大的量子計算機,可能還得幾十年甚至更久。(財富中文網)

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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