Quantum computers outperform classical ones, with results you can trust

量子计算机超越经典计算机，且结果值得信赖

There are many algorithms for which it has been mathematically proven that a quantum computer can generate results that would take a classical computer an unreasonable amount of time to generate. Unfortunately, today’s quantum computers either can’t run those algorithms or can only run simplified versions that classical computers can also handle. This has left the field facing a challenging question: Can we demonstrate the promise of quantum computers on today’s noisy, limited hardware? 许多算法在数学上已被证明，量子计算机生成其结果所需的时间，经典计算机可能需要耗费不合理的时间才能完成。遗憾的是，当前的量子计算机要么无法运行这些算法，要么只能运行经典计算机也能处理的简化版本。这使得该领域面临一个严峻的问题：我们能否在当今噪声大、性能有限的硬件上展示量子计算机的前景？

That’s a more difficult question than it may first appear. If you generate a result that’s out of reach of today’s regular computers, it may not be possible to verify that you got the right result. And given that today’s quantum computers are somewhat error-prone, getting the wrong result is a distinct possibility. Further, in the absence of a mathematical proof of the capabilities of quantum hardware, it’s possible that a better classical algorithm could outperform the quantum hardware. 这个问题比初看起来要困难得多。如果你生成了一个当今常规计算机无法企及的结果，你可能无法验证该结果是否正确。考虑到当今的量子计算机容易出错，得到错误结果的可能性很大。此外，在缺乏量子硬件能力数学证明的情况下，更好的经典算法完全有可能超越量子硬件。

These issues inspired IBM to launch a quantum advantage tracker. On Thursday, the company announced three new entries that it says clearly show a quantum advantage, each using a different approach to overcoming errors and validating quantum results. “Trusted computing when you can do classical simulations is irrelevant,” IBM’s Jay Gambetta told Ars. “Trusted computing when you can’t do classical simulations is a big deal.” None of the results are immediately useful, but they hint that we might be heading in the right direction. 这些问题促使 IBM 推出了“量子优势追踪器”（Quantum Advantage Tracker）。周四，该公司宣布了三项新成果，称其清晰地展示了量子优势，且每项成果都采用了不同的方法来克服错误并验证量子结果。IBM 的 Jay Gambetta 对 Ars 表示：“当你可以进行经典模拟时，‘可信计算’是无关紧要的；而当你无法进行经典模拟时，‘可信计算’就至关重要了。”虽然这些结果目前还没有直接的实用价值，但它们暗示我们可能正朝着正确的方向前进。

Trust, but verify

信任，但要验证

At this point, there have been many claims of quantum advantage, and at least one has the potential to be useful. But in a number of high-profile cases, algorithm developers have developed optimized algorithms that have severely reduced the advantage, bringing classical computers back up to par. Another issue is verification. If your quantum computer is generating a statistical pattern by re-running variations on a single set of operations, a systemic error could potentially bias the output, and you couldn’t use a classical computer to check. 目前，关于量子优势的声明层出不穷，其中至少有一项具有潜在的实用价值。但在许多备受瞩目的案例中，算法开发者通过优化算法大幅削弱了这种优势，使经典计算机重新追平了差距。另一个问题是验证。如果你的量子计算机通过对同一组操作进行多次变体运行来生成统计模式，系统性错误可能会导致输出偏差，而你无法使用经典计算机进行核对。

These issues are typically handled by performing simplified calculations using fewer qubits and verifying the results on classical hardware. If that works, it’s assumed that the algorithm will continue to work when it is run with more qubits. But that’s not the only option. Some algorithms could produce results that are difficult to calculate but easy to verify—for example, factoring the product of multiplying two large primes. Unfortunately, if anyone has identified a calculation that could be run on today’s hardware, I’m not aware of it. 这些问题通常通过使用较少的量子比特进行简化计算，并在经典硬件上验证结果来处理。如果这种方法有效，人们通常会假设该算法在运行更多量子比特时依然有效。但这并非唯一选择。有些算法可以产生难以计算但易于验证的结果——例如，对两个大质数的乘积进行因式分解。遗憾的是，据我所知，目前还没有人找到能在当今硬件上运行此类计算的方法。

Computer scientists, therefore, have had to get creative. And really, that’s what today’s announcement is about: three creative ways to handle the fact that today’s processors are error-prone. 因此，计算机科学家们不得不发挥创造力。事实上，这正是今天公告的核心所在：针对当今处理器容易出错这一事实，提出了三种创造性的应对方案。

One of the new efforts was a collaboration among IBM, RIKEN in Japan, and a small company called Qedma, which develops software that helps mitigate errors in current quantum processors. The work focuses on modeling a Floquet process, in which a system oscillates while subjected to an external force that gradually alters its behavior. Think of a pendulum that gradually slows down due to friction. These sorts of processes can also occur in quantum systems, and the Qedma team modeled something called an Ising model, which you can think of as a hypothetical two-dimensional grid of magnets, where the orientation of each can affect its neighbors. 其中一项新成果是 IBM、日本理化学研究所（RIKEN）以及一家名为 Qedma 的小型公司之间的合作，该公司开发了有助于减轻当前量子处理器错误的软件。这项工作专注于模拟“弗洛凯过程”（Floquet process），即系统在受到逐渐改变其行为的外力作用时发生振荡。想象一个因摩擦而逐渐减速的钟摆。这类过程也会发生在量子系统中，Qedma 团队模拟了一种所谓的“伊辛模型”（Ising model），你可以将其想象成一个假设的二维磁体网格，其中每个磁体的方向都会影响其邻居。

After setup, the orientations will gradually undergo periodic flips as they try to find a low-energy configuration in which neighboring magnets have opposite orientations. The complexity of modeling the intervening states of the system during these flips increases as you add more magnets. So, Qedma chose to model something that wasn’t too large to fit into existing quantum hardware, then worked with RIKEN to run two different classical algorithms on the Fugaku supercomputer, which was the world’s most powerful computer five years ago. 设置完成后，磁体方向会逐渐经历周期性翻转，试图找到一种低能态配置，使相邻磁体的方向相反。随着磁体数量的增加，模拟这些翻转过程中系统中间状态的复杂性也会随之增加。因此，Qedma 选择了一个规模适中、能适配现有量子硬件的模型，然后与 RIKEN 合作，在五年前全球最强大的超级计算机“富岳”（Fugaku）上运行了两种不同的经典算法。

The classical algorithms clearly showed that something was wrong, as they produced answers that diverged over time (one showing net magnetism decreasing smoothly, the other showing it increasing). When run on an IBM quantum processor with Qedma’s error-mitigation software, the algorithm showed something else entirely: a gradual decrease in magnetism, with periodic oscillations. One classical algorithm got the oscillations but showed magnetism increasing, while the other showed it decreasing much faster without the oscillations. To demonstrate that this wasn’t the product of some consistent error in the IBM hardware, the team turned to a Quantinuum processor and confirmed its output. They also identified a problem in at least one of the quantum algorithms: to boost performance, it truncates some terms needed to produce the oscillatory behavior. 经典算法清楚地表明出了问题，因为它们产生的答案随时间推移而产生分歧（一个显示净磁性平稳下降，另一个显示其增加）。当在配备 Qedma 纠错软件的 IBM 量子处理器上运行时，该算法显示出完全不同的结果：磁性逐渐下降，并伴有周期性振荡。其中一个经典算法捕捉到了振荡，但显示磁性在增加；另一个则显示磁性下降得更快，且没有振荡。为了证明这不是 IBM 硬件中某种一致性错误的结果，团队转向了 Quantinuum 处理器并确认了其输出。他们还发现至少一个量子算法存在问题：为了提高性能，它截断了产生振荡行为所需的一些项。

Exponentially hard

指数级难度

Another new manuscript comes from a collaboration between IBM and researchers at the University of Chicago. It’s similar in principle to one of the algorithms mentioned above: It repeats variations on an algorithm multiple times to get a sample of results that give a sense of the statistics of different outputs. Because the algorithm is run on a quantum computer, different outcomes can interfere with each other, making it challenging to simulate on classical hardware. It’s possible to run it a few times on classical machines, but not enough to get decent statistics on the output. In this case, the team made a couple of key variations. The first is that it mostly performed what are called Clifford gates, which are relatively easy to simulate on classical hardware. But it sprinkled in a few non-Clifford gates (specifically T gates) of a specific type chosen in part because they are less prone to error. 另一篇新论文来自 IBM 与芝加哥大学研究人员的合作。其原理与上述算法之一类似：通过多次重复算法的变体来获取结果样本，从而了解不同输出的统计规律。由于该算法在量子计算机上运行，不同的结果会相互干扰，使得在经典硬件上进行模拟极具挑战性。在经典机器上运行几次是可能的，但不足以获得可靠的输出统计数据。在这种情况下，团队做了几个关键的变体。首先，它主要执行所谓的“克利福德门”（Clifford gates），这些门在经典硬件上相对容易模拟。但它加入了一些非克利福德门（特别是 T 门），选择这种特定类型的部分原因是它们更不容易出错。