量子计算的主要期望是有效地解决某些对经典计算机来说极其昂贵的问题。经过验证的量子优势的大多数问题涉及重复使用一个黑盒，其结构编码解决方案。算法性能的一个度量是查询复杂性，例如在给定概率下找到解决方案所需的 黑盒 调用的数量。量子算法的多比特的验证，如Deutsch–Jozsa 及 Grover算法，已在不同的物理系统，如核磁共振、离子阱、光学系统及超导电路中 实现 。然而，在小范围内，这些问题可以通过一些 黑盒 查询得到经典解决，从而限制了所获得的优势。在这里，我们解决了一个基于 黑盒 的问题，在一个五比特的超导处理器上 基于噪声 环境下的学习 校验 。使用相同的 黑盒 执行经典和量子算法，我们观察到查询计数中的一个大的差距，有利于量子处理。我们发现，这种差距是错误率和问题大小的函数，呈数量级增长。这一结果表明，虽然通用量子计算需要复杂的容错架构，但现有的存在噪声的系统已经显现出显著的量子优势。 （2017年9月25日） 量子机器学习入门： http://blog.sciencenet.cn/blog-3364677-1096172.html 量子最快入门教程： http://blog.sciencenet.cn/home.php?mod=spaceuid=3364677do=blogid=1084559from=space Demonstration of quantum advantage in machine learning 来源： https://www.nature.com/articles/s41534-017-0017-3 The main promise of quantum computing is to efficiently solve certain problems that are prohibitively expensive for a classical computer. Most problems with a proven quantum advantage involve the repeated use of a black box, or oracle, whose structure encodes the solution. One measure of the algorithmic performance is the query complexity, i.e., the scaling of the number of oracle calls needed to find the solution with a given probability. Few-qubit demonstrations of quantum algorithms, such as Deutsch–Jozsa and Grover, have been implemented across diverse physical systems such as nuclear magnetic resonance, trapped ions, optical systems, and superconducting circuits. However, at the small scale, these problems can already be solved classically with a few oracle queries, limiting the obtained advantage. Here we solve an oracle-based problem, known as learning parity with noise, on a five-qubit superconducting processor. Executing classical and quantum algorithms using the same oracle, we observe a large gap in query count in favor of quantum processing. We find that this gap grows by orders of magnitude as a function of the error rates and the problem size. This result demonstrates that, while complex fault-tolerant architectures will be required for universal quantum computing, a significant quantum advantage already emerges in existing noisy systems.
量子计算正处在一个重要的拐点。多年来，量子计算机的高级算法已经显示出相当大的希望，而量子器件制造方面的最新进展提供了实用的希望。然而，量子计算算法的硬件尺寸和可靠性要求与未来十年预测的物理机器之间仍然存在着差距。为了弥补这一差距，量子计算机需要合适的软件来翻译和优化应用程序（工具流程）和抽象层。考虑到量子计算中严格的资源限制，软件和实现层之间传递的信息将与经典计算明显不同。量子工具流程必须揭露层与层之间更多的物理细节，因此面临的挑战是当隐藏了足够的复杂性时要找到揭露关键细节的抽象。 （2017年9月23日） Programming languages and compiler design for realistic quantum hardware 来源：http://www.nature.com/nature/journal/v549/n7671/full/nature23459.html Quantum computing sits at an important inflection point. For years, high-level algorithms for quantum computers have shown considerable promise, and recent advances in quantum device fabrication offer hope of utility. A gap still exists, however, between the hardware size and reliability requirements of quantum computing algorithms and the physical machines foreseen within the next ten years. To bridge this gap, quantum computers require appropriate software to translate and optimize applications (toolflows) and abstraction layers. Given the stringent resource constraints in quantum computing, information passed between layers of software and implementations will differ markedly from in classical computing. Quantum toolflows must expose more physical details between layers, so the challenge is to find abstractions that expose key details while hiding enough complexity.
标签: 量子计算
