Quantum computing and machine learning are effective tools in fluid dynamics
Quantum support vector machines classify flow separation better than classical counterparts
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A. FLOW WITHOUT FLOW SEPARATION AND WITH FLOW SEPARATION. B. PRESSURE DISTRIBUTION TREND WITHOUT FLOW SEPARATION AND WITH FLOW SEPARATION.
view moreCREDIT: XI-JUN YUAN ET AL.
To prevent aircraft stalls, engineers have long studied the flow of air over airfoils such as airplane wings to detect the angles when flow separation occurs. Recently, a team of researchers at Shanghai Jiao Tong University including Xi-Jun Yuan and Zi-Qiao Chen investigated the use of quantum computing in connection with machine learning as a more accurate way of solving such problems. Their research was published Nov. 21 in Intelligent Computing, a Science Partner Journal.
The use of a quantum support vector machine rather than a classical support vector machine increased the accuracy of classification of flow separation from 81.8% to 90.9% and increased the accuracy of classification of the angle of attack from 67.0% to 79.0%. These results help show that using quantum computing methods for fluid dynamics problems could be faster and more accurate than using classical computing methods, especially because the datasets in such contexts are large. Potential applications of quantum support vector machines in addition to aircraft design include underwater navigation and target tracking.
The researchers performed two classification tasks. The first was a binary classification on a small dataset to detect whether or not flow separation had occurred. A small dataset was chosen because it is difficult to achieve high-accuracy classification for small datasets. Data for this task were collected from pressure sensors on an airfoil in a wind tunnel with different airspeeds and angles of attack. The dataset consists of 45 multidimensional points: 27 cases without flow separation and 18 cases with flow separation. This dataset was divided into 34 points for training and 11 points for testing.
The second task was more complex. It classified the angle of attack of the airfoil after flow separation into one of four classes. To achieve this, the problem was broken into four one-against-all classification problems, with a binary in-or-out classifier for each of the four classes. Data for this task were created by simulation. The dataset consists of 63 multidimensional points obtained by sampling. This dataset was divided into 43 points for training and 20 for testing. The training and testing process was repeated 10 times with different combinations of training and test data, and the average accuracy of 10 tests was obtained.
The particular type of classification algorithm chosen by the researchers is a quantum-annealing-based supervised machine learning algorithm called a support vector machine. The quantum annealer they used was the D-Wave Advantage 4.1 system, a physical quantum computing device.
Quantum annealing implementations of support vector machines have demonstrated better performance than their classical counterparts, which are structurally simple and robust, but have high storage and computation costs and thus do not scale up easily.
Quantum annealing is an optimization process that uses quantum fluctuations to look for a global minimum among a set of solutions. Because the process generates multiple good candidates for the global minimum, it can achieve more accurate results than other optimization algorithms, which are more likely to get stuck at a local minimum.
Results of the flow classification problem.
A. and B. Decision plane obtained by classical and quantum support vector machines for the same dataset. The color represents the value of the training data decision function and the black dotted line marks the decision boundary. Test set data are represented by circles. C. Comparison of the performance of quantum and classical classifiers on flow separation tasks.
CREDIT
Xi-Jun Yuan et al.
JOURNAL
Intelligent Computing
METHOD OF RESEARCH
Experimental study
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Quantum Support Vector Machines for Aerodynamic Classification
Experiment could test quantum nature of large masses for the first time
An experiment outlined by a UCL-led team could test whether relatively large masses have a quantum nature, resolving the question of whether quantum mechanical description works at a much larger scale than that of particles and atoms.
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TECHNICIANS INSPECT THE "FIRST CONTACT" COATING ON ONE OF LIGO'S INPUT TEST MASSES (MIRRORS). HTTPS://WWW.LIGO.CALTECH.EDU/PAGE/OPTICS
view moreCREDIT: CALTECH/MIT/LIGO LAB
An experiment outlined by a UCL (University College London)-led team of scientists from the UK and India could test whether relatively large masses have a quantum nature, resolving the question of whether quantum mechanical description works at a much larger scale than that of particles and atoms.
Quantum theory is typically seen as describing nature at the tiniest scales and quantum effects have not been observed in a laboratory for objects more massive than about a quintillionth of a gram, or more precisely 10^(-20)g.
The new experiment, described in a paper published in Physical Review Letters and involving researchers at UCL, the University of Southampton and the Bose Institute in Kolkata, India, could in principle test the quantumness of an object regardless of its mass or energy.
The proposed experiment exploits the principle in quantum mechanics that the act of measurement of an object can change its nature. (The term measurement encompasses any interaction of the object with a probe – for instance, if light shines on it, or if it emits light or heat).
The experiment focuses on a pendulum-like object oscillating like a ball on a string. A light is shone on one half of the area of oscillation, revealing information about the location of the object (i.e., if scattered light is not observed, then it can be concluded that the object is not in that half). A second light is shone, showing the location of the object further along on its swing.
If the object is quantum, the first measurement (the first flash of light) will disturb its path (by measurement induced collapse -- a property inherent to quantum mechanics), changing the likelihood of where it will be at the second flash of light, whereas if it is classical then the act of observation will make no difference. Researchers can then compare scenarios in which they shine a light twice to ones where only the second flash of light occurs to see if there is a difference in the final distributions of the object.
Lead author Dr Debarshi Das (UCL Physics & Astronomy and the Royal Society) said: “A crowd at a football match cannot affect the result of the game simply by staring strongly. But with quantum mechanics, the act of observation or measurement itself changes the system.
“Our proposed experiment can test if an object is classical or quantum by seeing if an act of observation can lead to a change in its motion.”
The proposal, the researchers say, could be implemented with current technologies using nanocrystals or, in principle, even using mirrors at LIGO (Laser Interferometer Gravitational-Wave Observatory) in the United States which have an effective mass of 10kg.
The four LIGO mirrors, which each weigh 40kg but together vibrate as if they were a single 10kg object, have already been cooled to the minimum-energy state (a fraction above absolute zero) that would be required in any experiment seeking to detect quantum behaviour.
Senior author Professor Sougato Bose (UCL Physics & Astronomy) said: “Our scheme has wide conceptual implications. It could test whether relatively large objects have definite properties, i.e., their properties are real, even when we are not measuring them. It could extend the domain of quantum mechanics and probe whether this fundamental theory of nature is valid only at certain scales or if it holds true for larger masses too.
“If we do not encounter a mass limit to quantum mechanics, this makes ever more acute the problem of trying to reconcile quantum theory with reality as we experience it.”
In quantum mechanics, objects do not have definite properties until they are observed or interact with their environment. Prior to observation they do not exist in a definite location but may be in two places at once (a state of superposition). This led to Einstein’s remark: “Is the moon there when no one is looking at it?”
Quantum mechanics may seem at odds with our experience of reality but its insights have helped the development of computers, smartphones, broadband, GPS, and magnetic resonance imaging.
Most physicists believe quantum mechanics holds true at larger scales, but is merely harder to observe due to the isolation required to preserve a quantum state. To detect quantum behaviour in an object, its temperature or vibrations must be reduced to its lowest possible level (its ground state) and it must be in a vacuum so that nearly no atoms are interacting with it. That is because a quantum state will collapse, a process called decoherence, if the object interacts with its environment.
The new proposed experiment is a development of an earlier quantum test devised by Professor Bose and colleagues in 2018. A project to conduct an experiment using this methodology, which will test the quantum nature of a nanocrystal numbering a billion atoms, is already underway, funded by the Engineering and Physical Sciences Research Council (EPSRC) and led by the University of Southampton.
That project already aims for a jump in terms of mass, with previous attempts to test the quantum nature of a macroscopic object limited to hundreds of thousands of atoms. The newly published scheme, meanwhile, could be achieved with current technologies using a nanocrystal with trillions of atoms.
The new paper was co-authored by Dr Das and Professor Bose at UCL along with Professor Dipankar Home of India’s Bose Institute (who also co-authored the 2018 paper) and Professor Hendrik Ulbricht of the University of Southampton.
JOURNAL
Physical Review Letters
METHOD OF RESEARCH
Computational simulation/modeling
SUBJECT OF RESEARCH
Not applicable
ARTICLE TITLE
Mass-independent scheme to test the quantumness of a massive object
ARTICLE PUBLICATION DATE
16-Jan-2024
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