Technology
The Basics of Quantum Machine Learning
Quantum computing depends on the effects of mechanics to compute problems that would be out of reach for classical computers. A quantum computer uses qubits, like regular bits in a computer, however, with the added capacity to be put into a superposition and share entanglement. Quantum machine learning explores how quantum computers can be used for data-driven prediction and decision-making.
Quantum is any of the tiny packages or increments into which many forms of energy are subdivided, as well as the smallest amount or unit of something, especially energy. It is also any of the small subdivisions of a quantized physical magnitude, such as magnetic moment.
It refers to the combination of quantum algorithms within machine learning programs. The term indicates that machine learning algorithms for analyzing classical data are executed on a quantum computer, i.e., quantum-enhanced machine learning. A computer encodes information into computers and conducts its operations through quantum states in order to get the results it needs by solving the problems.
While machine learning algorithms are used to calculate vast quantities of data, quantum machine learning applies quantum and qubits operations or specific quantal systems to enhance computational speed.
This includes hybrid methods that involve both quantum and classical processing, where computationally difficult subroutines are outsourced to a device.
One example is Grover’s search algorithm depreciation, in which a subroutine uses Grover’s search algorithm to locate an aspect less than some previously defined element. This process can be achieved with an oracle that decides whether or not a state with a corresponding part is less than the predefined one.
Then, Grover’s algorithm will find an element to meet our condition. The diminishment is initialized by some random belongings in the data set and iteratively does this subroutine to find the lowest part in the data set. These habits can be more complicated and executed faster on a quantum computer.
Moreover, supervised learning with quantum computers is used through algorithms. Those algorithms can be used to analyze allocation states instead of classical data. The term “quantum machine learning” is also linked with classical machine learning methods applied to data generated from experiments, such as creating new experiments or learning the phase transitions of a system.
For example, numerical techniques and mathematics from physics apply to deep classical learning and vice versa. Furthermore, researchers investigate more abstract notions of learning theory concerning information, sometimes referred to as “quantum learning theory.” The theory follows a mathematical analysis of the generalizations of the possible speed-ups and classical learning models or other improvements they may provide.
Another example of how data science and quantum computing work is Google’s quantum beyond-classical experiment that used 53 noisy qubits. Those qubits determine if they might be able to complete a calculation in around 200 seconds on a quantum computer that will take 10,000 years on the giant classical computer using existing algorithms.
Quantum algorithms for unsupervised and supervised machine learning mark the beginning of the Noisy Intermediate-Scale Quantum (NISQ) computing era, which can be linked to the basics of quantum computing, machine learning and IoT. In the coming years, quantum devices with tens to hundreds of noisy qubits will become a reality.
Machine Learning with Quantum Computers
Quantum machine learning is built on two concepts: quantum data and hybrid quantum-classical models.
“Hybrid quantum computing” is the idea of a quantum computer and a classical computer working jointly to solve problems. In addition, classical computers perform deterministic classical operations or can emulate probabilistic processes using simple methods.
By manipulating entanglement and superposition, computers can perform operations that are difficult to imitate at scale with classical computers such as:
- Logistics Optimization: It will allow one to improve your supply chain efficiency, while tracking goods from their origin until it reaches them. The procedure can properly manage sensitive and fragile products such as glass objects.
- Financial Modelling: For a finance industry to find the right mix for fruitful investments based on expected returns, the risk associated and other factors are essential to survive in the market. To achieve that, the technique of ‘Monte Carlo’ simulations is continually being run on conventional computers, which, in turn, consumes an enormous amount of computer time.
- Cybersecurity & Cryptography: The online security space has been powerless due to the increasing number of cyber-attacks occurring across the globe daily. Although companies establish the necessary security framework in their organizations, the process becomes daunting and impractical for classical digital computers.
- Drug Design & Development: Developing and designing a drug is the most challenging problem in quantum computing.
Quantum Data
Quantum data is considered any data source that arises in an artificial quantum system or natural one. This can be data rendered by a quantum computer, as the samples assembled from the Sycamore processor for Google’s demonstration of quantum supremacy. The processor completes a calculation in around 200 seconds on a computer that will take 10,000 years on the giant classical computer using existing algorithms.
Also, data displays entanglement and superposition, leading to standard possibility allocations that could need an exponential amount of classical computational resources to represent or store.
The data induced by NISQ processors are noisy and generally involved just before the measurement occurs. Heuristic machine learning techniques can create models that maximize the extraction of useful classical information from noisy entangled data. Heuristics is a method of problem-solving whose purpose is to deliver a working solution within a reasonable period.
Heuristics are used in AI and machine learning when it’s unusable to solve a problem with a step-by-step algorithm. This process is present because a heuristic approach highlights that speed is more important than accuracy and is usually merged with optimization algorithms to improve results.
Quantum Computing Artificial Intelligence
One more concept that needs to be defined is Quantum computing and artificial intelligence, which are transformational technologies and are expected to involve quantum computing to accomplish considerable progress.
While AI produces functional applications with classical computers, it is limited by the computational capabilities of classical computers; Quantum computing can offer a computation increase to AI, allowing it to challenge more complex problems and Artificial General Intelligence (AGI).
Therefore, Quantum AI is the use of quantum computing for the computation of machine learning algorithms – it can help achieve results that are impossible to achieve with classical computers, thanks to the computational advantages of quantum computing and with the help of the principles of quantum artificial intelligence (quantum Fourier transform and Grover search). If you’re interested in learning more about the benefits of this type of computing, we’ve written a piece about how Quantum AI helps companies with data driven innovations.
Summary
Quantum machine learning has evolved quickly throughout the past 20 years, reaching an exceedingly prominent level that is changing the way AI operates while facilitating the process of quantum theory. This has also led to the invention of recent modern technologies that, in one way or another, shall do the job of many humans combined, which will be marked as a historic achievement.
Inside Telecom provides you with an extensive list of content covering all aspects of the Quantum industry. Keep an eye on our Quantum news space to stay informed and updated with our daily articles.
What is biometric identification? It is the process through which unique biological characteristics are used in biometric identification to recognize and validate a person. These characteristics can be found in facial traits, eye structures, DNA, fingerprint patterns, and even handwriting. All this data that can be gathered on an individual inevitably brings up the ethical issues in biometrics that need to be addressed.
Facial recognition and fingerprint scans were previously the domain of security, and they were utilized for identification and law enforcement. However, increasingly more business and civil applications are using biometrics authentication. Due to this increased use, it is crucial to address the moral and ethical issues in biometrics when used in the creation of new application and technology.
Applied biometrics and corporate ethics have received little empirical study despite the widespread use of biometric technologies. As a result, there is plenty of room for future study to help us better comprehend the moral consequences of adopting this technology.
This article examines the ethical concerns associated with the use of biometric technology on the use of biometrics for non-security applications as well as the moral ramifications for business.
Ethical Issues in Biometrics and Privacy
Biometric data takes privacy concerns to a whole new level that typical data gathering only touches on. Digital identities can be forged, and anonymity on the web can be maintained to a degree if you know how. Also, digital data can be deleted – if we forget that Meta (Facebook) has been revealed to store deleted data, which is an ethical concern in itself.
The problem with biometric data is that it is unforgeable, unconcealable, and permanent, You can’t change your eye structure, your fingerprint, or your facial features. Big tech companies already have your face, fingerprints, and your voice recorded and stored via current biometric authentication systems – mainly used for unlocking your phone. That data will be there forever, and there is little that people can do about it besides avoid it In the first place.
While many companies still give users the option of using a good old fashion passcode to unlock their phones, as technology as a whole tends to do, the biometric alternative is slowly gaining more traction, and will one day become the norm, as it is hard to argue its convenience and security. You can’t accidentally leave your eyeball at home or forget your fingerprint. But in the same sense, you can’t change it. If your childish curiosity while browsing the web put a black spot on your record, there’s no changing that.
Therein lies the question of who if anyone should have access to such data. Today’s tech giants have all our data in their hands, that won’t change with the spread of biometric verification methods. Indeed, it will only increase targeting capabilities.
While the collection of biometric data on its own does not necessarily mean it will be shared, monetized, or abused in any way, it never the less requires clear and strict guidelines and regulations to be implemented around it. There are too many things that can be abused by the wrong party when the ethical issues in biometrics are not taken seriously, and if such limitations are not applied stringently.
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The Reno line of smartphones has always had a fantastic camera and an attractive design. The Reno 8 and 8 Pro include a 120Hz AMOLED display, the newest Dimensity chipsets, 80W fast charging, a 32-megapixel front camera, and triple 50-megapixel back cameras, among other notable features that make the Oppo Reno 8 and Reno 8 Pro high-quality yet affordable option for anyone.
Here we compare the two latest Oppo phone series iterations and see what to expect from the flagship Oppo smartphone, and how the Oppo Reno 8 and the Oppo Reno Pro stack up against each other.
Oppo Reno 8 Specifications
The Oppo Reno 8 has a smaller 6.43-inch full-HD+ AMOLED display with a resolution of 1,080 by 2,400 pixels, a 90Hz refresh rate, and Corning Gorilla Glass 5 protection. It also runs Android 12 with ColorOS 12.1 on top. Additionally, the display has an 800 nits maximum brightness and a 20:09 aspect ratio. An octa-core MediaTek Dimensity 1300 SoC, up to 8GB of LPDDR4x RAM, and up to 256GB of UFS 3.1 storage are all found within the Oppo smartphone.
The Reno 8 has a triple back camera configuration for pictures and movies, with a 50-megapixel main sensor and an f/1.8 lens as its focal point. Additionally, there is a 2-megapixel macro camera with a 112-degree field of view and an 8-megapixel sensor coupled with an f/2.2 ultra-wide angle lens.
The Reno 8 offers 5G, 4G LTE, Wi-Fi 6, Bluetooth v5.3, GPS/A-GPS, NFC, and a USB Type-C connector as connection options. An accelerometer, light, gyroscope, magnetometer, and a proximity sensor are among the sensors on board. For biometric verification, the phone also has a fingerprint sensor hidden behind the display. A 4,500mAh battery inside the Oppo Reno 8 supports 80W Super Flash Charge quick charging. The phone weighs 179 grams and has dimensions of 160 x 73.4 x 7.67mm.
Oppo Reno 8 Pro Specifications
A 6.7-inch full-HD+ (1,080×2,412) AMOLED display with up to 120Hz refresh rate and Corning Gorilla Glass 5 protection is included with the Oppo Reno 8 Pro. Running on top of Android 12 is ColorOS 12.1. Additionally, the display supports HDR10+ and is certified by Netflix HD, Amazon HDR, SGS Low Motion Blur, and SGS Low Blue Light. The MediaTek Dimensity 8100-Max SoC, which has eight cores, and up to 12GB of LPDDR5 RAM power the smartphone. Storage on the Oppo Reno 8 Pro is UFS 3.1 capable of up to 256GB.
The Oppo Reno 8 Pro boasts a triple rear camera arrangement with a 50-megapixel Sony IMX766 main sensor and an f/1.8 lens, an 8-megapixel sensor and an ultra-wide lens with an f/2.2 aperture and a 112-degree field of view, and a 2-megapixel macro camera with an f/2.4 aperture. Along with the MariSilicon X NPU, the phone’s camera will offer generally better performance in both bright and dim light. It has a 32-megapixel front-facing camera sensor with an f/2.4 lens for taking selfies and making video calls.
The phone has 5G, 4G LTE, Wi-Fi 6, Bluetooth v5.3, GPS/A-GPS, NFC, and a USB Type-C connector as connectivity options. An accelerometer, a light meter, a gyroscope, a magnetometer, and a proximity sensor are among the sensors built into the phone. For biometric authentication, the phone also contains a fingerprint sensor underneath the touchscreen. The smartphone has a 4,500mAh battery that supports rapid charging at 80W Super Flash Charge. The phone weighs 183 grams and has dimensions of 161 x 74.2 x 7.34mm.
Both the Oppo Reno 8 and Reno 8 Pro are prime smartphones that can fit any user’s preferences and can be considered premium smartphones on the cheaper side. If you like photography, you will be satisfied with the level of quality overall, but keep in mind that neither phone is particularly water resistant, so be careful where you take it.
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Many prefer the rumbling ‘vroom’ of a gasoline car to the soft hum of an electric vehicle, while others would rather just be satisfied in the knowledge that they are lessening their carbon footprint. Let’s look at the cold hard facts however, and compare electric cars vs gas cars
Electric Cars vs Gas Cars: Components
The amount of moving components is an important distinction between electric and gasoline-powered automobiles. While a gasoline-powered car contains hundreds of moving components, an electric vehicle just has one, the motor. Another significant distinction is that the electric car has fewer moving components. The electric car is more dependable and needs less routine maintenance. The maintenance needed for a gasoline-powered car ranges from routine oil changes, filter replacements, tune-ups, and exhaust system repairs to less frequent component replacements including water and fuel pumps, alternators, and so on.
Electric Cars vs Gas Cars: Maintenance
The expenses associated with maintaining an electric car are reduced since there are less maintenance requirements. The shaft, the only moving component of the electric motor, is extremely dependable and needs little to no maintenance. The controller and charger are electrical devices that require minimal to no maintenance because they have no moving components. Modern sealed lead acid batteries are maintenance free and utilized in electric cars nowadays. However, these batteries have a finite lifespan and will eventually need to be replaced. New batteries are being developed to increase the range of electric cars as well as the battery pack’s lifespan, perhaps eliminating the need to replace the battery pack throughout the course of the vehicle’s lifespan.
Electric Cars vs Gas Cars: Efficiency
In addition to being simpler and less expensive to repair than gasoline-powered vehicles, electric vehicles are also more cost-effective to run. The electric car will drive roughly 43 miles for $1 based on the efficiency of 3 miles/kwhr and the cost of power at 7 cents per kwhr. The gasoline-powered car will go roughly 18 miles based on the average gas mileage of 22 miles per gallon and the price of gasoline at $1.25 per gallon. As a result, an electric car may drive more than twice as far on a dollar’s worth of petrol.
Where Electric Falls Behind
There are still a number of difficulties for the owner of an electric car despite the fact that it will be less expensive to operate and maintain.
The primary issue is the restricted range offered by existing battery technology. The amount of driving distance possible utilizing the current batteries is between 50 and 150 miles. These ranges are being extended by new battery technologies, and prototypes of these batteries have shown ranges of up to 200 miles between recharging. Solar powered charging stations are being implemented in numerous places to decrease range anxiety towards electric vehicles
The lack of qualified service professionals to repair and maintain electric cars is another issue that faces their owners. A two-year associate degree program has been developed to train high school graduates to become proficient electric vehicle technicians, and training programs are being developed and made available to upgrade the conventional automotive technician with the skills needed to maintain an electric vehicle.
Infrastructure to recharge the batteries is also required. The most important component of the infrastructure for recharging already exists: electric power is accessible practically everywhere. To support the electric car, it is still necessary to guarantee that charging stations with the right sorts of service (i.e., maximum voltage and current) are placed in important areas. Plans must also be made.
Inside Telecom provides you with an extensive list of content covering all aspects of the tech industry. Keep an eye on our Technology sections to stay informed and up-to-date with our daily articles.
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Ian Terblanche, Global Strategic Sales & Channel Director at Sigfox
