Artificial intelligence can be used to improve the research method of Quantum Mechanics.

Searching for different uses of artificial intelligence has always been a successful journey and among its numerous uses, quantum mechanics stands in a vital position. Artificial Intelligence can be used to predict molecular wave functions and the electronic properties of molecules. The behavior of the electron in the molecule can be observed and the data can be fed to AI algorithm, which would further predict the future behaviors of the electrons in the molecules. The researchers of University of Warwick, the Technical University of Berlin and the University of Luxembourg have together come up with such innovative ways of using AI. Using quantum mechanics, the behavior of an electron in a molecule is still described by a wave function, analogous to the behavior in an atom. Just like electrons around isolated atoms, electrons around atoms in molecules are limited to discrete (quantized) energies. The region of space in which a valence electron in a molecule is likely to be found is called a molecular orbital. Like an atomic orbital, a molecular orbital is full when it contains two electrons with opposite spin.

Making a difference

In general, artificial intelligence can be used in observing and predicting any consistent common behavior. For example, AI is used in predicting the shopping behavior of people and it is done by observing the way the person shops on a regular basis. In a similar way, AI can be used for predicting the quantum states of molecules, so-called wave functions, which determine all properties of molecules. AI is capable of doing this by learning to solve fundamental equations of quantum mechanics. Doing it in the conventional way requires massive high-performance computing resources, which is typically the bottleneck to the computational design of new purpose-built molecules for medical and industrial applications. However, this newly developed AI algorithm will be able to supply accurate predictions within seconds on a laptop or mobile phone.

Details of Research

Dr. Reinhard Maurer from the Department of Chemistry at the University of Warwick stated while talking about this research, “This has been a joint three year effort, which required computer science know-how to develop an artificial intelligence algorithm flexible enough to capture the shape and behavior of wave functions, but also chemistry and physics know-how to process and represent quantum chemical data in a form that is manageable for the algorithm.” The research shows that AI methods can efficiently perform the most difficult aspects of quantum molecular simulations. Within the next few years, AI methods will establish themselves as an essential part of the discovery process in computational chemistry and molecular physics. The team has been brought together during an interdisciplinary 3-month fellowship program at IPAM (UCLA) on the subject of machine learning in quantum physics.

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Quantum computing has the capability to revolutionize computing by being the solution for previously unsolvable problems.

There’s a reason Google, Microsoft, IBM, and governments all over the world continue to invest heavily in quantum computing: they believe it will transform the world by solving issues that traditional computers can’t solve.

Every industry is slowly being disrupted by quantum computers. They’re revolutionizing the way we do business and the security we have in place to protect data, as well as how we battle illness and develop innovations, as well as how we solve health and climate issues.

According to the report, yet the “quantum jungle” of available devices and protocols remains hard to navigate, and researchers still need to work on identifying the most promising paths to quantum technologies that can be societally useful. However, a collaboration between two teams at the University of Arizona—one led by Zheshen Zhang and the other by Quntao Zhuang—shows that quantum entanglement can provide a quantifiable advantage to data classification, which is important in imaging and navigation.

The team identifies data from a network of entangled sensors using a machine-learning algorithm. They demonstrate that entanglement can improve both the accuracy and the speed of classification by comparing their scheme to one that uses traditional data processing. The research sets the door for a wide range of quantum-enhanced classification methods to be made possible by near-future quantum technologies.

Zhang, Zhuang, and colleagues investigate a path for quantum-enhanced data processing that stems from the marriage of quantum machine learning with the most well-established quantum technologies: quantum sensing and metrology.

“The work of Zhang’s and Zhuang’s teams focuses on a particularly intriguing case—a network of quantum sensors. The use of quantum information processing techniques to combine and analyze the quantum outputs of multiple sensors holds tremendous promise for realizing a quantum advantage. The potential gain stems from a fundamental feature of quantum metrology: The advantage provided by the coherent processing of sensor data scales as the square root of the dimension of the so-called Hilbert space that represents the quantum states sensed by the network. Since the dimension of that Hilbert space scales exponentially with the number of analyzed states, the quantum advantage for a quantum sensor network scales exponentially with the number of sensors.”

Zhang, Zhuang, and their colleagues decided to explore the quantum jungle by taking a route that crosses the line between quantum sensing and quantum machine learning. Whatever they found along the way proves that this path is worth exploring further.

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Quantum AI and quantum computing are transformational technologies enabling a revolutionary future.

Quantum AI refers to the use of quantum computing for the computation of machine learning algorithms. With the computational advantages of quantum computing, quantum AI can now achieve results that were not possible with classical computers.

Alan Turing published a paper on Computing Machinery and Intelligence in 1950, and since then computers have come a long way. In the current modern age, computer limitations are gradually fading away, and machine learning has the ability to learn from its experiences. Traditionally, this type of intelligence was only achievable by using multiple computers and complicated machine learning algorithms. However, Nature Nanotechnology journal had a paper published recently where scientists proposed a new method – designing a computer with embedded intelligence and using the atom’s quantum spins to revolutionize computing as we know.

Next-Gen Computing

To understand this concept, let cover the basics of neuromorphic computing. In layman’s language, neuromorphic computing attempts to imitate the way a human brain works. From a technical perspective, neuromorphic computing is concerned with computer engineering where the elements of a computer, both hardware, and software, are wired according to the human nervous system and cerebral system.

Engineers study several disciplines like computer science, biology, mathematics, electronic engineering, and physics to create accurate neural structures. Neuromorphic computing aims to create devices that can learn, retain information, and make logical deductions the way a human brain does, a cognition machine. Alongside, it also attempts to prove how the human brain works by scavenging new information.

As a step forward in artificial intelligence technology, neuromorphic computing allows robots embedded with small computing hardware to make their own decisions in the future.

The Quantum Brain

The Quantum brain is a prime example of neuromorphic computing, the future of computing. Our human brains use signals sent by our neurons to make all kinds of computations. Similarly, the quantum brain uses cobalt atoms on a superconducting black phosphorus surface to imitate the process of human brain signals.

Cobalt atoms have quantum properties like unique spin states which carry information to stimulate ‘neuron firing’ with applied voltages. This helped the atoms to achieve a self-adaptive behavior based on the external stimuli.

Can AI Work With A Quantum Brain?

Artificial intelligence is an evolving technology, but it still has not overcome technological limitations. But with quantum computing, obstacles to achieving artificial general intelligence, AGI, can be discarded. Quantum computing can rapidly train machine learning models to generate optimized algorithms. Quantum computing can power an optimized and steady AI to complete analysis in a short time, as opposed to years of work that would delay any and all technological advancements.

According to researchers, a realistic aim for quantum AI is to replace traditional algorithms with quantum algorithms. These quantum algorithms can have several use cases to further advancements.

• Developing quantum algorithms for traditional learning models can provide possible boosts to the deep learning training process. Quantum computing can help machine learning by presenting the optimal solution set of the weights of artificial neural networks, quickly.

• When traditional decision-making problems are formulated with decision trees, the next course of action to reach the solution sets is by creating branches for a particular point. However, this method becomes complicated when the problem is too complex. Quantum algorithms can solve the problem faster.

Can neuroscience-inspired quantum computing and AI mesh? Yes, says several similarities between the brain and machine learning techniques like deep learning. Is that future near? Yes and no. Right now, the quantum AI industry needs to work to eliminate immaturities in the technology and achieve crucial milestones such as less error-prone and more powerful computing, developing the right AI applications where quantum computing can outperform traditional computing, and creating a widely adopted open-source modeling and training frameworks. These milestones will push quantum AI towards future developments.

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Quantum technologies seem poised to disrupt the world of high-performance computing, but developing – and stabilizing – the technology itself poses serious computing challenges. Researchers at Los Alamos National Laboratory are using machine learning to help manage harmful disruptions in quantum computers, allowing them to perform more reliably in the real world.

Quantum bits (qubits) sometimes interact in strange ways, producing noisy interference that causes the qubits to falter within millionths of a second. Cumulatively, these errors end up causing a significant amount of noise and producing serious losses in accuracy and efficiency. In the past, researchers have ameliorated the errors by, in essence, reducing opportunities for the qubits to interact with one another.

By way of contrast, this method, called noise-aware circuit learning (NACL), uses machine learning to train the quantum circuits to recognize the noise of that specific quantum computer, and thus, teach itself to ignore that noise. The researchers likened the resulting algorithm to a vaccine teaching the body to resist certain pathogens by exposing it to them – but unlike a vaccine, NACL can select from a wide menu of noise reduction techniques and apply them where necessary.

“In this new research, we let the computer discover what’s best,” explained Patrick Coles, a quantum physicist at Los Alamos and lead author of the paper. “In essence, we say, ‘Computer, please find the best strategy for making a resilient circuit.’ We found the computer discovers strategies that make sense to us. … [NACL] will play an important role in the quest for quantum advantage, when a quantum computer solves a problem that’s impossible on a classical computer.”

The researchers tested NACL by first modeling the noise of a selected quantum computer, training the ML model on that data, and testing the results on the actual quantum computer in question.

“Our work automates designing quantum computing algorithms and comes up with the fastest algorithm tailored to the imperfections of a specific hardware platform and a specific task,” added Lukasz Cincio, a quantum physicist at Los Alamos and co-author of the paper. “This will be a crucial tool for using real quantum computers in the near term for work such as simulating a biological molecule or physics simulations relevant to the national security mission at Los Alamos.”

For now, NACL is being tested on and developed for intermediate-scale quantum computers. “For the future, it will be important to figure out how to scale NACL to develop noise-resilient circuits for larger devices,” Coles said. 

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Anish Agarwal, Director, Data & Analytics, India at NatWest Group discussed how quantum computing plays a vital role in the advancement of artificial intelligence.

Machine Learning Developers Summit (MLDS 2021) is one of the biggest gatherings of machine learning developers in India. With more than 1,500 machine learning developers, 60 speakers from around 200 organisations, the conference corrals India’s leading Machine Learning innovators and practitioners to share their ideas about machine learning tools, advanced development and more.

Anish Agarwal, Director, Data & Analytics, India at NatWest Group, talked about “The Interplay between Quantum Theory And Artificial Intelligence” at MLDS 2021.

The session started with an introduction to emerging technologies like artificial intelligence, a brief on quantum computing, different forms of quantum technology used for various military as well as civilian applications, how it is different from the classical computers as well as how quantum computing plays a vital role in the advancement of artificial intelligence. 

In the field of quantum computing, Agarwal discussed the technique of quantum artificial intelligence, how it can be used for computation of machine learning algorithms and what makes this technology unique. 

Quantum AI can help in achieving results that are impossible with classical computers. He said, as per reports, 25 percent of fortune global 500 companies will have a competitive edge from quantum computing by the year 2023. Tech giants like Google, Microsoft are doubling down on quantum computing.

He then explained the possibilities of applying quantum computing in AI:

• Quantum Algorithms for Learning: Specifically for machine learning, quantum algorithms for learning can provide possible speedups and other improvements in a deep learning training process. The contribution of quantum computing to classical machine learning can be achieved by presenting the optimal solution at the base of artificial neural networks. 
• Quantum Search: Quantum search algorithm can be described as a database search algorithm. 
• Quantum Algorithms for Decision Problems: Quantum algorithms based on Hamiltonian time evaluation can solve problems faster than classical algorithms.

He said, “Quantum machine learning (QML) is not one settled homogeneous field. This is because machine learning itself is quite diverse in nature.” He added, “Quantum Machine Learning is simply the field exploring the connections between quantum computing and quantum physics on one hand and machine learning and related fields on the other hand.”

Agarwal then deliberated on Quantum Game Theory and compared it with classical game theory. He said quantum game theory can be used to overcome critical problems in quantum communications.

He also discussed the advantages of quantum AI:

• Quick resolution of complex problems
• Optimisation
• Ability to spot patterns in extremely large datasets
• Integrating data from different datasets

Agarwal concluded the session by touching upon the key applications of quantum artificial intelligence. Lastly, he mentioned some of the critical milestones for quantum AI and busted a few myths related to quantum computing techniques.  

The critical milestones include:

• Less error-prone and more powerful quantum computing systems
• Widely adopted open-source modelling and training frameworks
• Substantial and skilled developer ecosystem
• Compelling AI applications

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Cambridge Quantum Computing ( CQC ) today announced the latest version of tket  (pronounced “ticket”), its high-performance quantum software development kit (SDK), at the all license restrictions on the use of the python module from tket (also known as “pytket”) have been lifted.

Python is an open-source, general-purpose coding language prevalent in quantum computer programming and software development. With this latest version, every Python user who has access to a quantum computer can use the tket SDK in any context, whether commercial or not, and benefit from its capabilities.

Version 0.7 now also enables the execution of quantum circuits on Microsoft Azure Quantum  (public preview version ) and extends the classic control of quantum operations on ion trap systems from Honeywell Quantum Solutions .

“By giving legions of Python users around the world free access to our world-class tket SDK, we hope to accelerate the development of quantum computing research and applications in various industries,” said Mehdi Bozzo-Rey , Head of Business Development, Cambridge Quantum Computing. “By also increasing the number of tket-compatible cloud-based quantum computing platforms, we have made it easier for virtually any programmer to explore quantum algorithm and software development.”

Originally developed and continuously updated by CQC’s team of quantum computing scientists, tket enables researchers, algorithm designers and software developers to build and execute quantum circuits that produce the best results on the most modern quantum devices available. tket translates machine-independent algorithms into executable circuits, optimizes the physical qubit layout and at the same time reduces the number of quantum gates required.

tket supports virtually all quantum hardware devices and quantum programming languages, and enables the user to switch between devices by changing just a single line of code, making a developer’s research program platform independent. tket is used by many quantum hardware vendors and large companies around the world.

Other new functions in version 0.7 of tket are:

• Improved circuit optimization and noise suppression with new methods that make it easier to build quantum circuits;
• Replacing said operations with other operations, boxes, or circuits; and
• Support for mid-circuit measurements on IBM Quantum Premium devices.

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Google recently released TensorFlow Quantum, a toolset for combining state-of-the-art machine learning techniques with quantum algorithm design. This is an essential step to build tools for developers working on quantum applications.

Simultaneously, they have focused on improving quantum computing hardware performance by integrating a set of quantum firmware techniques and building a TensorFlow-based toolset working from the hardware level up – from the bottom of the stack.

The fundamental driver for this work is tackling the noise and error in quantum computers. Here’s a small overview of the above and how the impact of noise and imperfections (critical challenges) is suppressed in quantum hardware. 

Noise And Error: The Chinks In Armor When It Comes To Quantum Computers

Quantum computing combines information processing and quantum physics to solve challenging computer problems. However, a significant issue in quantum computers is susceptibility to noise and error, limiting quantum computing hardware efficiency. Noise refers to all sorts of things that can cause interference, like the electromagnetic signals from the WiFi or disturbances in the Earth’s magnetic field. Most quantum computing hardware can run just a few dozen calculations over much less than 1 ms before requiring a reset due to the noise’s influence. That is about 1024 times worse than the hardware in a laptop.

Many teams have been working to make the hardware resistant to the noise to overcome these weaknesses. Many theorists have also designed a smart algorithm called Quantum Error Correction. QEA can identify and fix errors in the hardware, but it is very slow or incapable of practice. Because the information is to be spread in one qubit over lots of qubits, it may take a thousand or more physical qubits to realize just one error-corrected “logical qubit.”

To overcome this, Q-CTRL’s “quantum firmware” can stabilize the qubits against noise and decoherence without the need for extra resources. This is done by adding the new solutions that improve the hardware’s robustness to the error at the lowest layer of the quantum computing stack.

The protocols described by the Quantum firmware are there to deliver the quantum hardware with augmented performance to higher levels of the abstraction in the quantum computing stack.

In general, quantum computing hardware relies on light-matter interaction, which is made to enact quantum logic operations.

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A new machine learning tool can calculate the energy required to make — or break — a molecule with higher accuracy than conventional methods. While the tool can currently only handle simple molecules, it paves the way for future insights in quantum chemistry.

“Using machine learning to solve the fundamental equations governing quantum chemistry has been an open problem for several years, and there’s a lot of excitement around it right now,” says co-creator Giuseppe Carleo, a research scientist at the Flatiron Institute’s Center for Computational Quantum Physics in New York City. A better understanding of the formation and destruction of molecules, he says, could reveal the inner workings of the chemical reactions vital to life.

Carleo and collaborators Kenny Choo of the University of Zurich and Antonio Mezzacapo of the IBM Thomas J. Watson Research Center in Yorktown Heights, New York, presented their work May 12 in Nature Communications.

The team’s tool estimates the amount of energy needed to assemble or pull apart a molecule, such as water or ammonia. That calculation requires determining the molecule’s electronic structure, which consists of the collective behavior of the electrons that bind the molecule together.

A molecule’s electronic structure is a tricky thing to calculate, requiring the determination of all the potential states the molecule’s electrons could be in, plus each state’s probability.

Since electrons interact and become quantum mechanically entangled with one another, scientists can’t treat them individually. With more electrons, more entanglements crop up, and the problem gets exponentially harder. Exact solutions don’t exist for molecules more complex than the two electrons found in a pair of hydrogen atoms. Even approximations struggle with accuracy when they involve more than a few electrons.

One of the challenges is that a molecule’s electronic structure includes states for an infinite number of orbitals going farther and farther from the atoms. Additionally, one electron is indistinguishable from another, and two electrons can’t occupy the same state. The latter rule is a consequence of exchange symmetry, which governs what happens when identical particles switch states.

Mezzacapo and colleagues at IBM Quantum developed a method for constraining the number of orbitals considered and imposing exchange symmetry. This approach, based on methods developed for quantum computing applications, makes the problem more akin to scenarios where electrons are confined to preset locations, such as in a rigid lattice.

The similarity to rigid lattices was the key to making the problem more manageable. Carleo previously trained neural networks to reconstruct the behavior of electrons confined to the sites of a lattice. By extending those methods, the researchers could estimate solutions to Mezzacapo’s compacted problems. The team’s neural network calculates the probability of each state. Using this probability, the researchers can estimate the energy of a given state. The lowest energy level, dubbed the equilibrium energy, is where the molecule is the most stable.

The team’s innovations made calculating a basic molecule’s electronic structure simpler and faster. The researchers demonstrated the accuracy of their methods by estimating how much energy it would take to pull a real-world molecule apart, breaking its bonds. They ran calculations for dihydrogen (H2), lithium hydride (LiH), ammonia (NH3), water (H2O), diatomic carbon (C2) and dinitrogen (N2). For all the molecules, the team’s estimates proved highly accurate even in ranges where existing methods struggle.

In the future, the researchers aim to tackle larger and more complex molecules by using more sophisticated neural networks. One goal is to handle chemicals like those found in the nitrogen cycle, in which biological processes build and break nitrogen-based molecules to make them usable for life. “We want this to be a tool that could be used by chemists to process these problems,” Carleo says.

Carleo, Choo and Mezzacapo aren’t alone in tapping machine learning to tackle problems in quantum chemistry. The researchers first presented their work on arXiv.org in September 2019. In that same month, a group in Germany and another at Google’s DeepMind in London each released research using machine learning to reconstruct the electronic structure of molecules.

The other two groups use a similar approach to one another that doesn’t limit the number of orbitals considered. This inclusiveness, however, is more computationally taxing, a drawback that will only worsen with more complex molecules. With the same computational resources, the approach by Carleo, Choo and Mezzacapo yields higher accuracy, but the simplifications made to obtain this accuracy could introduce biases.

“Overall, it’s a trade-off between bias and accuracy, and it’s unclear which of the two approaches has more potential for the future,” Carleo says. “Only time will tell us which of these approaches can be scaled up to the challenging open problems in chemistry.”

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Imagine that you met a charming girl in school. She is an excellent student who concerns about the world welfare and is anti-war and anti-nuclear. Which do you think she is most likely to become in the future, a bank counter clerk, or, a bank counter clerk and feminist? Surveys show that most people think it’s easy and choose the latter. Their choice is right. However, according to classical probability theory, the probability of the former is definitely higher than the latter because the former contains the latter. That paradox calls for more modeling to be established to better fit facts.

Prof. ZHANG Xiaochu and his group developed new frameworks to better explain such human decision-making behaviors using the concept of quantum, and their result is published in Nature Human Behaviour in January 2020. They established the quantum reinforcement learning framework for human decision-making applying concepts from quantum probability theory. For example, they chose quantum probability amplitude rather than classical probability to describe the tendency of selecting a specific action. In this way they proposed quantum models that are comparable to the best classical models. Furthermore, they checked the functional magnetic resonance imaging (fMRI) data of human brain playing the Iowa Gambling Task. They were surprised to find that several important internal-state-related variables involved in their models are represented in the medial frontal gyrus (MeFG), which is important for human learning and decision-making. This shows a unique quantum-like neural mechanism for how the internal state is changed due to external information. In other words, this implies that how human brain works is a quantum-like manner, which is worthy of further research.

These models bring people new perspectives on understanding how human brains run. It’s inspired by machine learning development and is possible to elevate the efficiency of machine learning. In the meantime, the quantum-like mechanisms in the brain are still not fully understood. This deserves additional studies and is likely to change the history.

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