量子和ai人工智能_量子计算将永远改变人工智能的四种方式
量子和ai人工智能
If science were a dating app, quantum physics and machine learning probably wouldn’t be a match. They’re from completely different fields and often require completely different backgrounds and skills. But, throw in a little quantum computing and, suddenly, that science-matchmaking app becomes Tinder and the attraction between the two is palpable.
如果科学是约会应用程序，那么量子物理学和机器学习可能就不会匹配了。 他们来自完全不同的领域，通常需要完全不同的背景和技能。 但是，投入一点量子计算，突然间，科学配对应用程序变成了Tinder，这两者之间的吸引力是显而易见的。
Even though the extent of change that quantum computing will unleash on AI is up for debate, many experts now more than suspect that quantum computing will definitely alter AI at some level. Analysts from bank holding company BBVA, for example, point toward the natural synergy between quantum computing and AI as reasons why quantum machine learning will eventually best classical machine learning.
尽管量子计算将在AI上释放的变化程度尚有待商now，但许多专家现在更多地怀疑量子计算一定会在一定程度上改变AI。 例如，银行控股公司BBVA的分析师指出，量子计算和AI之间的自然协同作用是量子机器学习最终将胜过经典机器学习的原因。
“Quantum machine learning can be more efficient than classic machine learning, at least for certain models that are intrinsically hard to learn using conventional computers,” says Samuel Fernández Lorenzo, a quantum algorithm researcher who collaborates with BBVA’s New Digital Businesses area. “We still have to find out to what extent do these models appear in practical applications.”
“量子机器学习比经典机器学习更有效，至少对于某些使用传统计算机本质上难以学习的模型而言，”与BBVA的New Digital Businesses领域合作的量子算法研究员SamuelFernándezLorenzo说。 “我们仍然必须找出这些模型在实际应用中出现的程度。”
Here are four ways quantum computers could change the future of AI. Forever.
量子计算机可以通过四种方式改变AI的未来。 永远。
1.处理庞大的数据量 (1. Handling HUGE Amounts of Data)
Machine learning and AI eat data. Lots of data. Quantum computers are designed to manage huge amounts of data. With each iteration of quantum computer design and improvements to quantum error-correction code, programmers are able to better master the potential of qubits — quantum bits — to manage exponentially more data, according to Lorenzo.
机器学习和AI吃数据 大量数据。 量子计算机旨在管理大量数据。 Lorenzo认为 ，随着量子计算机设计的每一次迭代以及对量子纠错码的改进，程序员都能够更好地掌握量子比特(量子比特)的潜力，以管理更多的数据。
“In classical computing we know how to solve problems thanks to computer language (AND, OR NOT) used when programming,” said Lorenzo. “Operations that are not feasible in bit computing can be performed with a quantum computer. In a quantum computer all the numbers and possibilities that can be created with N qubits are superimposed (if there are 3 qubits, there will be 8 simultaneous possible permutations.) With 1,000 qubits the exponential possibilities far exceed those that we have in classical computing.”
Lorenzo说：“在经典计算中，由于编程时使用了计算机语言(AND，OR NOT)，我们知道如何解决问题。” 在位计算中不可行的操作可以用量子计算机来执行。 在量子计算机中，可以用N个量子位创建的所有数量和可能性都被叠加(如果有3个量子位，则将同时存在8个可能的排列。)使用1000个量子位，指数可能性远远超过了传统计算中的指数可能性。 ”
2.建立更好的模型 (2. Building Better Models)
Several industries, such as pharmaceutical, life sciences and finance, are nearly at the end of their classical computing rope. These industries require complex models that classical computers just can’t generate. Quantum computers, on the other hand, have the potential processing power to model the most complex situations. If quantum technology can create better models, it may lead to better treatments for disease, decreased risk of financial implosions, and improved logistics.
诸如制药，生命科学和金融等多个行业已接近其经典计算的末端。 这些行业需要经典计算机无法生成的复杂模型。 另一方面，量子计算机具有对最复杂情况进行建模的潜在处理能力。 如果量子技术可以创建更好的模型，则可以带来更好的疾病治疗，降低财务内爆的风险并改善物流。
3.更准确的算法 (3. More Accurate Algorithms)
According to Lorenzo, supervised learning is used for most industrial applications of artificial intelligence, such as image recognition or consumption forecasting. Quantum Machine Learning — QML — researchers are trying to find ways to develop better quantum computer algorithms.
洛伦佐认为，监督学习被用于人工智能的大多数工业应用中，例如图像识别或消费预测。 量子机器学习(QML)研究人员正在设法找到开发更好的量子计算机算法的方法。
“In this area, based on the different QML — quantum machine learning — proposals that have already been set forth, it is likely that we’ll start seeing acceleration – which, in some cases, could be exponential – in some of the most popular algorithms in the field, such as ‘support vector machines’ and certain types of neural networks,” explains Fernández Lorenzo.
“在这一领域中，基于已经提出的不同的QML(量子机器学习)建议，我们很可能会开始看到加速-在某些情况下可能是指数式的-在某些最受欢迎的产品中领域的算法，例如“支持向量机”和某些类型的神经网络。”FernándezLorenzo解释说。
Quantum computing should have an immediate impact on traditional AI models and algorithms, such as non-supervised learning and reinforcement learning, according to the researcher.
研究人员说，量子计算应该对传统的AI模型和算法产生直接影响，例如非监督学习和强化学习。
“Dimensionality reduction algorithms are a particular case. These algorithms are used to represent our original data in a more limited space, but preserving most of the properties of the original dataset,” said Lorenzo.
“降维算法是一种特殊情况。 这些算法用于在更有限的空间中表示我们的原始数据，但保留了原始数据集的大多数属性。” Lorenzo说。
He added that quantum computing’s particular skill will help pinpoint certain global properties in a dataset, not so much specific details…
他补充说，量子计算的特殊技能将帮助查明数据集中的某些全局特性，而不是具体细节。
Reinforcement learning is an AI model that is used to handle complex situations, for example, in videogaming, but many experts suggest that the model’s potential is much greater. The most demanding task here, in terms of computing workload and time consumption, is training the algorithm.“In this context, some theoretical proposals have already been laid out to accelerate this training using quantum computers, which may contribute to developing an extremely powerful artificial intelligence in the future,” said Lorenzo.
强化学习是一种AI模型，用于处理复杂的情况，例如在视频游戏中，但是许多专家认为，该模型的潜力更大。 在计算工作量和时间消耗方面，这里最苛刻的任务是训练算法。“在这种情况下，已经提出了一些理论上的建议，以加速使用量子计算机的这种训练，这可能有助于开发功能非常强大的人工未来的情报。”洛伦佐说。
4.使用多个数据集 (4. Using Multiple Datasets)
The problem often isn’t that there is not enough data, or that there’s too much data, the problem is that the data is placed in a variety of datasets, according to futurist and strategic adviser Bernard Marr. He writes that quantum computers could handle the integration of different datasets for much quicker and easier analysis.
未来主义者和战略顾问伯纳德·马尔(Bernard Marr)表示，问题通常不是数据不足或数据过多，而是数据被放置在各种数据集中。 他写道，量子计算机可以处理不同数据集的集成，从而可以更快，更轻松地进行分析。
“The promise is that quantum computers will allow for quick analysis and integration of our enormous data sets which will improve and transform our machine learning and artificial intelligence capabilities,” he writes.
他写道：“量子计算机有望对我们庞大的数据集进行快速分析和集成，从而改善并改变我们的机器学习和人工智能功能。”
人工智能和量子合作正在发生 (AI and Quantum Collaborations Are Happening Now)
The natural extension that quantum computers offer machine learning and artificial intelligence is not lost on entrepreneurs, who are busy now learning ways to exploit the technical combination. Recently, The Quantum Daily reported on a deal that was signed between C-DAC and Atos, two companies who are investigating the match between quantum computing and AI.
量子计算机提供机器学习和人工智能的自然扩展并没有让企业家们迷失，他们现在正忙于学习利用技术组合的方法。 最近，《量子日报》报道了C-DAC与Atos两家公司之间的一项交易 ，两家公司正在研究量子计算与AI之间的匹配。
量子和ai人工智能