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Underdog Technologies Gain Ground in Quantum-Computing Race

2023-02-18
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The race to build practical quantum computers might be entering a new phase. Some of the front-runner technologies are now facing size constraints, and others are rapidly coming up from behind.

For years, two leading approaches have enabled physicists to make progress partly by cramming devices with more and more qubits, the quantum equivalent of a computer’s memory bits. One of those methods encodes qubits as currents running on superconducting loops. The other uses excited states of individual ions trapped in a vacuum by electromagnetic fields.

But in the past two years, qubits that consist of single neutral atoms — as opposed to ions — and are held with ‘tweezers’ made of laser light have suddenly become competitive. And other techniques that are at an even earlier stage of development could yet catch up.

“Superconducting qubits and trapped-ion qubits have done the most-advanced experiments, with the most qubits under control,” says Barbara Terhal, a theoretical physicist at QuTech, a quantum-research institute at the Delft University of Technology in the Netherlands. “However, this is no guarantee that these platforms will stay in the lead.”

The quest for qubits

Quantum computers promise to solve problems that are out of reach for classical machines by harnessing phenomena such as quantum superposition, in which an object can exist in two simultaneous states — spinning both clockwise and anticlockwise, for example. Physicists call such states qubits to distinguish them from ordinary bits, which can be only ‘0’ or ‘1’.

Quantum states are notoriously fragile. In a quantum computer, the information they carry — which can extend across several qubits to form ‘entangled’ states — tends to degrade or get lost as a calculation progresses. To preserve the states for as long as possible, qubits must be kept isolated from the environment. But they cannot be too isolated from one another because they must interact to perform calculations.

This — among other factors — makes building a useful quantum computer is challenging. But the field has come further than QuTech director of research Lieven Vandersypen would have expected ten years ago. “The progress is actually impressive.”

Google made headlines in 2019 when it claimed that a machine made of 54 superconducting qubits had performed the first quantum computation that would have taken impossibly long on a classical computer, an achievement that researchers call quantum advantage. The technology company IBM, which has invested heavily in superconducting qubits, expects to reach a milestone in the next few months, when it will unveil a quantum chip named Condor, the first to breach the 1,000-qubit barrier.

Last November, the company announced its previous chip, the 433-qubit Osprey — a follow-up to the 127-qubit Eagle, which set a record in 2021. “We really wanted to lay a road map like you would expect from the semiconductor industry,” says Jerry Chow, who leads the quantum-computer programme at the IBM Thomas J. Watson Research Center in Yorktown Heights, New York.

Quality and quantity

Chow says that IBM’s aim is not only to scale up the number of qubits, but also to improve their quality. Some of the company’s superconducting elements can hold their quantum states for more than 300 microseconds, he says — a record for the technology. In another crucial measure, 99.9% of operations involving two qubits are now error-free.

Scaling up becomes impractical once the number of superconducting qubits on a chip goes much beyond 1,000, because each qubit needs to be individually wired to external circuits for control and readout. IBM will therefore take a modular approach. Starting in 2024, each further step on its road map will aim not to increase the number of qubits on a chip, but to link multiple chips into one machine — something that is not straightforward if the connection has to carry the quantum states unharmed or help to entangle qubits on separate chips. The chips are at the hearts of massive contraptions encased in cryogenic systems that keep the chips close to 0 kelvin.

Trapped-ion computers could have even more-stringent size constraints than superconducting ones, partly because they require a separate laser device to control each ion. Typically, that has meant limiting the traps to rows of around 32 ions per chip. But IonQ, a start-up company spun off from the University of Maryland in College Park, says its approach enables it to pack multiple rows of ions into a single chip, perhaps reaching as many as 1,024 qubits. To go beyond that, IonQ also plans to move to a modular approach, connecting multiple chips. In laboratory experiments, trapped ions have reached fidelities as high as 99.99%, according to a spokesperson for the company.

Tweezer tech

Another technique — which, until a few years ago, was barely on the radar — might soon break the 1,000-qubit barrier as well. It traps neutral atoms using tightly focused laser beams, called optical tweezers, and encodes qubits in the electronic states of the atoms or in the spins of atoms’ nuclei. The approach has been developing gradually for more than a decade, but now it’s “booming”, says Giulia Semeghini, a physicist at Harvard University in Cambridge, Massachusetts.

To assemble multiple qubits, physicists split a single laser beam into many, for example by passing it through a screen made of liquid crystals. This can create arrays of hundreds of tweezers, each trapping their own atom. The atoms are typically a few micrometres away from their neighbours, where they can persist in a quantum state for several seconds or more. To make the atoms interact, physicists point a separate laser at one of them to tickle it into an excited state, in which an outer electron orbits much farther away from the nucleus than normal. This boosts the atom’s electrostatic interactions with a neighbour.

Using tweezers, researchers have built arrays of more than 200 neutral atoms, and they are rapidly combining new and existing techniques to turn these into fully working quantum computers.

One major advantage of the technique is that physicists can combine multiple types of tweezers, some of which can move around quickly — with the atoms they carry. “Every time you want two of them to interact, you bring them together,” says Harvard physicist Dolev Bluvstein. This makes the technique more flexible than other platforms such as superconductors, in which each qubit can interact only with its direct neighbours on the chip. A team including Semeghini and Bluvstein demonstrated this flexibility in an April 2022 paper.

The tweezer-based qubits should soon be 99% error-free, although further improvements will take substantial work, Semeghini says.

The pace of improvement in neutral atoms has surprised the quantum-computing community. “The path to scale to thousands of atomic qubits is clear and will likely happen within two years,” says physicist Chao-Yang Lu at the University of Science and Technology of China (USTC) in Hefei.

Spin control

Other qubit technologies are still in their infancy, but advancing steadily. One method encodes information in the spin of individual electrons trapped by electric fields inside conventional semiconductors such as silicon. Last year, Vandersypen and his collaborators demonstrated a fully working six-qubit machine of this kind2. As in the case of optical tweezers, the electron spins can be shuttled around the device to bring them next to others on demand. But just like other types of qubit, a major difficulty is keeping the spins from influencing each other when they are not supposed to, in what physicists call crosstalk.

The benefit of semiconductor-based qubits would be the ability to make chips in the same type of factory where current computer chips are produced, although a team led by physicist Michelle Simmons at the University of New South Wales in Sydney, Australia, assembles the devices atom by atom using the tip of an automated scanning tunnelling microscope. “Everything is patterned with sub-nanometre precision,” she says.

Yet another approach is still at the conceptual stage, but it has received substantial investment, by Microsoft in particular. The technique aims to exploit ‘topological states’ to make qubits robust to degradation, just like a knotted string that can be twisted and pulled but not untied. In 2020, researchers observed the basic physical mechanism for one kind of topological protection, and they are now working on demonstrating the first topological qubits.

“Every platform that is pursued today has some promise, but developing it can require really novel ideas that you can’t predict,” says Vandersypen. Pan Jian-Wei, a physicist who works on multiple quantum-computing approaches at USTC, agrees. When it comes to the race to develop quantum computers, “it is still too early to say which candidate will win”.

This article is reproduced with permission and was first published on February 6 2023.

参考译文
不被看好的技术在量子计算竞赛中取得了进展
建造实用量子计算机的竞赛可能会进入一个新阶段。一些领先的技术现在正面临规模限制,而另一些则迅速后来居上。多年来,两种领先的方法使物理学家在设备中塞满越来越多的量子位(量子相当于计算机的内存位),从而取得了一定的进展。其中一种方法是将量子位编码为在超导环上运行的电流。另一种是利用电磁场在真空中捕获单个离子的激发态。但在过去的两年里,由单个中性原子(与离子相反)组成、由激光制成的“镊子”握住的量子比特突然变得具有竞争力。而其他处于更早期发展阶段的技术也有可能迎头赶上。荷兰代尔夫特理工大学量子研究所QuTech的理论物理学家芭芭拉·泰哈尔(Barbara Terhal)说:“超导量子比特和困离子量子比特已经完成了最先进的实验,控制了最多的量子比特。”“然而,这并不能保证这些平台将保持领先地位。”量子计算机有望通过利用量子叠加等现象来解决经典机器无法解决的问题,在量子叠加中,一个物体可以同时存在两种状态——例如,顺时针和逆时针旋转。物理学家称这种状态为量子位,以区别于普通比特,普通比特只能是“0”或“1”。量子态是出了名的脆弱。在量子计算机中,它们携带的信息——可以跨越几个量子比特形成“纠缠”状态——随着计算的进行往往会退化或丢失。为了尽可能长时间地保持状态,量子位必须与环境隔离。但它们彼此之间不能过于孤立,因为它们必须相互作用才能进行计算。除了其他因素外,这使得构建一个有用的量子计算机具有挑战性。但这一领域的发展已经超出了QuTech研究主管Lieven Vandersypen十年前的预期。“进展实际上令人印象深刻。”谷歌在2019年上了头条新闻,当时它声称一台由54个超导量子比特组成的机器完成了第一次量子计算,这在经典计算机上不可能花费很长时间,研究人员将这一成就称为量子优势。在超导量子比特领域投入巨资的科技公司IBM预计将在未来几个月达到一个里程碑,届时它将推出一款名为秃鹰(Condor)的量子芯片,这是首个突破1000个量子比特大关的芯片。去年11月,该公司宣布了其上一款433量子比特的芯片“鱼鹰”,这是2021年创下纪录的127量子比特“鹰”的后续产品。位于纽约约克城高地的IBM托马斯·j·沃森研究中心量子计算机项目负责人杰瑞·周说:“我们真的想为半导体行业制定一份路线图,就像你所期望的那样。”Chow说,IBM的目标不仅是扩大量子位的数量,而且还要提高它们的质量。他说,该公司的一些超导元素可以保持其量子态超过300微秒,这是该技术的一项记录。在另一项关键指标中,99.9%涉及两个量子比特的操作现在是无错误的。 一旦芯片上的超导量子比特数量远远超过1000个,扩大规模就变得不切实际了,因为每个量子比特都需要单独连接到外部电路进行控制和读取。因此,IBM将采用模块化方法。从2024年开始,其路线图上的每一步都不是为了增加芯片上的量子比特数量,而是为了将多个芯片连接到一台机器上——如果连接必须无损地传输量子态或帮助将量子比特纠缠在单独的芯片上,这就不是一件简单的事情。这些芯片位于大型装置的核心,这些装置被包裹在低温系统中,使芯片保持在接近0开尔文的温度。捕获离子计算机可能比超导计算机有更严格的尺寸限制,部分原因是它们需要一个单独的激光设备来控制每个离子。通常情况下,这意味着每个芯片上只能有32个离子。但是,从马里兰大学帕克分校分离出来的初创公司IonQ表示,他们的方法可以将多行离子打包到一个芯片中,最多可能达到1024个量子比特。除此之外,IonQ还计划采用模块化方法,连接多个芯片。据该公司发言人称,在实验室实验中,被捕获的离子保真度高达99.99%。另一项技术——直到几年前还几乎不为人知——可能很快也会突破1000个量子比特的障碍。它使用紧密聚焦的激光束(称为光镊)捕获中性原子,并在原子的电子状态或原子核的自旋中编码量子位。马萨诸塞州剑桥市哈佛大学的物理学家Giulia Semeghini说,这种方法已经逐步发展了十多年,但现在它正在“蓬勃发展”。为了组装多个量子比特,物理学家将一束激光分成许多束,例如通过液晶屏幕。这可以形成由数百个镊子组成的阵列,每个镊子捕获自己的原子。这些原子通常与它们的邻居相距几微米,在那里它们可以保持数秒或更长时间的量子态。为了使原子相互作用,物理学家将一个单独的激光对准其中一个原子,使其进入激发态,在这种状态下,外层电子的轨道离原子核比正常情况下远得多。这促进了原子与相邻原子的静电相互作用。使用镊子,研究人员已经建立了200多个中性原子的阵列,他们正在迅速结合新的和现有的技术,将这些技术转变为完全工作的量子计算机。这项技术的一个主要优势是,物理学家可以将多种类型的镊子与它们携带的原子结合起来,其中一些镊子可以快速移动。哈佛大学物理学家Dolev Bluvstein说:“每次你想让它们相互作用时,你就把它们放在一起。”这使得该技术比超导体等其他平台更加灵活,在超导体平台上,每个量子比特只能与芯片上的直接邻居相互作用。包括Semeghini和Bluvstein在内的团队在2022年4月的一篇论文中展示了这种灵活性。Semeghini说,基于镊子的量子比特很快就会达到99%的零错误,尽管进一步的改进还需要大量的工作。中性原子的进步速度令量子计算界感到惊讶。位于合肥的中国科学技术大学(USTC)的物理学家陆朝阳(Chao-Yang Lu)说:“将量子比特扩展到数千个原子的路径是明确的,很可能在两年内实现。” 其他量子比特技术仍处于起步阶段,但正在稳步发展。其中一种方法是在硅等传统半导体内部被电场捕获的单个电子的自旋中编码信息。去年,范德西彭和他的合作者展示了一台完全工作的六量子比特机器。就像光学镊子一样,电子自旋可以在设备周围穿梭,根据需要将它们带到其他设备旁边。但就像其他类型的量子比特一样,一个主要的困难是在不应该相互影响的情况下,防止自旋相互影响,物理学家称之为串扰。基于半导体的量子位的好处是能够在生产当前计算机芯片的工厂中制造芯片,尽管澳大利亚悉尼新南威尔士大学的物理学家米歇尔·西蒙斯(Michelle Simmons)领导的团队使用自动扫描隧道显微镜的尖端一个原子一个原子地组装这些设备。她说:“每样东西的图案都是亚纳米级的精度。”还有一种方法仍处于概念阶段,但它已经得到了大量投资,尤其是微软的投资。该技术旨在利用“拓扑状态”使量子比特具有抗退化的鲁棒性,就像一根打结的弦,可以扭曲和拉伸,但不能解开。在2020年,研究人员观察到一种拓扑保护的基本物理机制,他们现在正致力于演示第一个拓扑量子比特。范德西彭说:“如今人们追求的每一个平台都有一定的前景,但开发它可能需要你无法预测的非常新颖的想法。”在中国科技大学从事多种量子计算方法研究的物理学家潘建伟对此表示赞同。谈到开发量子计算机的竞赛,“现在说哪位候选人会胜出还为时过早”。本文经许可转载,首次发表于2023年2月6日。
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