Quantum Computing involves the use of several quantum phenomena to perform computations. One of these is entanglement. These phenomena help speed up exponentially the computational power, thus bringing computing to the next level as quantum computers operate at much higher speeds than classical computers. It also allows them to use less energy in performing the same operations as a classical one.
Decoherence and quantum computing
Quantum computers are very powerful, but they are also very fragile. When qubits interact with their environment, they decay and ultimately disappear in a process called decoherence.
Decoherence is caused by a range of factors, including light, heat, sound, vibration, radiation, and even the act of measuring a qubit itself.
While supercooled fridges and vacuum chambers are used to shield qubits from the outside world, errors still creep into quantum calculations. Technology is not yet sufficiently advanced to create a stable quantum computer that is broadly useful.
Why does quantum computing matter?
The need for knowledge has always engulfed humans and is the major driver of technological evolutions. From what started as an abacus and turned into high-end calculators for everyday use, computers have seen a similar advancement. Within three decades, computing powers changed from a mere five thousand addition problems (ENIAC) to millions of complex problems in a matter of seconds.
This exponential advancement has not hindered the progress that humans still dream of achieving. Technological changes have always occurred whenever there has been a problem to solve. The work of decades upon decades in different technological areas has left little room for improvements. However, whenever existing technology was unable to solve the tasks at hand, humans have tried to resolve the issue with further advancement.
One similar case has been that of Quantum Computing. When complexities ensued and classical computers could not answer the underlying questions, quantum computers were invented. Hence, a new era of advancement followed.
Understanding quantum computing
Modern computers encode information in bits that have a binary value. That is, the information can only take a value of 1 or 0.
Quantum computers, on the other hand, utilize subatomic particles called quantum bits (qubits). Qubits possess some strange quantum properties. Connected qubits provide a significant increase in processing power when compared to the equivalent number of bits in a modern computer.
The quantum properties responsible for this increased performance are:
Superposition – defined as the ability to exist in multiple states. Qubits can represent numerous possible combinations of 1 and 0 simultaneously. This enables quantum computers to rapidly assess a vast number of potential outcomes. Once a result has been calculated, the quantum state of qubits reverts to a binary state of either 1 or 0.
Entanglement. Qubits are said to be entangled when two members of a pair exist in a single quantum state. In other words, changing the state of one qubit will instantaneously change the state of the other. Scientists do not understand how or why entanglement occurs but adding entangled qubits to a quantum computer produces an exponential increase in computational power.
How does quantum computing work?
As the name suggests, Quantum Computing involves the use of several quantum phenomena to perform computations. One of these is entanglement. Quantum entanglement is basically a phenomenon that occurs when a group or a pair of particles interact or are in the same proximity but their quantum state cannot be determined independently of each other.
Similarly, another phenomenon that is part of Quantum Computing is superposition. Superposition states that any two quantum states can be added or “superposed”. The result will be another quantum state. In the same way, this also entails that every quantum state is a sum of other quantum states which can be two or more in number. Quantum Computing uses these phenomena to perform faster computations than classical computers such as integer factorization.
It is widely argued that whichever problems that quantum computers solve, can also be solved by classical computers. Alternatively, whichever problems can be solved by classical computers as well. The difference that exists between the two is the time that both take while solving the problems. This advantage of quantum computers over classical computers is known as “quantum supremacy”. Just like classical computers store information in the form of bits (0 or 1), quantum computers use what are known as “qubits”.
As mentioned before, using phenomena like superposition and entanglement quantum computers are able to allow subatomic participles in more than one state. This means that at the same time it could be a 1 or a 0. This makes quantum computers operate at much higher speeds than classical computers. It also allows them to use less energy in performing the same operations as a classical one.
Commercial applications for quantum computing
Quantum Computing has a wide array of applications, which makes it one of the most exciting technologies to look forward to. Within the healthcare industry, it cannot only be used for research purposes but diagnostics and treatment as well. Since quantum computers have high processing power, it will enable researchers to use them in order to simulate interactions between different proteins of the human genome and drugs.
This will allow them to evaluate drugs based on their interactions and can lead to pharmacological advancements. In diagnostics, MRI machines can be made to operate at higher levels and provide greater detail which will help the doctors in identifying medical issues. Similarly, treatments like radiotherapy can be further enhanced due to the use of quantum computing as it will be able to withstand complex simulations and provide answers in a timely manner.
In the field of Finance, quantum computing can help in detecting fraud based on pattern recognition. Coupled with machine learning, neural networks can be trained timely and thereby improving the detection rate immensely. From a Marketing perspective, quantum computing can be used to process and analyze large amounts of data which can be used to put forward targeted advertisements at potential customers based on their behavior.
The same can be done through classical computers, but quantum computing certainly has an edge in providing better and timely service due to the data being in large amounts. Optimization problems, which are encountered by companies like delivery services or arranging flight schedules, can be solved using quantum computers. It has uses in almost all avenues, whether they are public projects or advancements in data handling. What would normally take unimaginable amounts of time can be solved through the use of quantum computers.
Major advantages of quantum computing
The major advantage that quantum computers hold is that they are equipped to find optimal solutions to problems that have infinitely many variables. Due to their high processing power, quantum computers are able to run millions of simulations to test whatever theories that users might have. This gives it an ultimate advantage over other systems.
Quantum computers at extremely cold temperatures. The temperatures required are near absolute zero. To achieve such a cold temperature, the chip is required to be cooled down. This is achieved through liquified helium, which makes the chip very cold. To achieve superconductivity, such low temperatures are essential for quantum computing.
Research is being conducted to make quantum computing possible at higher temperatures, but no such significant improvement is expected in the near future.
Scientists are developers are constantly in the run to make quantum computing possible given the large number of applications that it entails. Machine learning will benefit the most when stability is achieved in terms of quantum computations. Technology giants like Google and IBM are in the constant run to achieve quantum supremacy, with each taking steps to ensure the world witnesses a stable quantum computer in the next few years.
What’s the major drawback (for now) of quantum computing?
One of the issues that quantum computers encounter is any disturbance in the computer’s surroundings. Since they are very fragile, vibrations in the surroundings can impact the atoms, and decoherence will be caused. Despite their high demands, quantum computers will actually reduce the power consumption to operate. This is achieved through a process known as “quantum tunneling.” The possibilities are endless, and researchers are in a rush to make it happen.
Other potential applications for quantum computing
The potential applications for quantum computing are understandably vast. But in the short term, some of the most promising applications include:
Simulating the behavior of matter at the molecular level. Volkswagen and Daimler AG are using quantum computers to simulate the chemical composition of electric-vehicle batteries. The auto-makers hope that these simulations will highlight new ways of making battery technology more efficient. Pharmaceutical companies are using similar chemical simulations to assess compounds that could be used in new drugs.
Optimization. An obvious application of quantum computing is any scenario where a large amount of data must be analyzed in a timely fashion. Airbus is using the technology to help determine the most fuel-efficient ascent and descent paths for their range of aircraft. Volkswagen is also using quantum computing to calculate routes that avoid congestion for taxis in large cities.
Key takeaways:
Quantum computing uses elements of quantum mechanics to create high-performance computers that analyze large amounts of data rapidly.
Quantum computing is based on qubits and the two quantum properties of superposition and entanglement. Qubits offer significant benefits over traditional binary computers because they can exist in multiple states simultaneously.
Quantum computing is still in its infancy because qubits tend to decay to a non-quantum state when exposed to disturbances. Nevertheless, they are currently being used in the transport and pharmaceutical industries to drive innovation and performance.
Large language models (LLMs) are AI tools that can read, summarize, and translate text. This enables them to predict words and craft sentences that reflect how humans write and speak.
Prompt engineering is a natural language processing (NLP) concept that involves discovering inputs that yield desirable or useful results.
Like most processes, the quality of the inputs determines the quality of the outputs in prompt engineering. Designing effective prompts increases the likelihood that the model will return a response that is both favorable and contextual.
Developed by OpenAI, the CLIP (Contrastive Language-Image Pre-training) model is an example of a model that utilizes prompts to classify images and captions from over 400 million image-caption pairs.
AIOps is the application of artificial intelligence to IT operations. It has become particularly useful for modern IT management in hybridized, distributed, and dynamic environments. AIOps has become a key operational component of modern digital-based organizations, built around software and algorithms.
Machine Learning Ops (MLOps) describes a suite of best practices that successfully help a business run artificial intelligence. It consists of the skills, workflows, and processes to create, run, and maintain machine learning models to help various operational processes within organizations.
The business intelligence models have transitioned to continuous intelligence, where dynamic technology infrastructure is coupled with continuous deployment and delivery to provide continuous intelligence. In short, the software offered in the cloud will integrate with the company’s data, leveraging on AI/ML to provide answers in real-time to current issues the organization might be experiencing.
That is a process that requires a continuous feedback loop to develop a valuable product and build a viable businessmodel. Continuous innovation is a mindset where products and services are designed and delivered to tune them around the customers’ problems and not the technical solution of its founders.
Technological modeling is a discipline to provide the basis for companies to sustain innovation, thus developing incremental products. While also looking at breakthrough innovative products that can pave the way for long-term success. In a sort of Barbell Strategy, technological modeling suggests having a two-sided approach, on the one hand, to keep sustaining continuous innovation as a core part of the businessmodel. On the other hand, it places bets on future developments that have the potential to break through and take a leap forward.
OpenAI has built the foundational layer of the AI industry. With large generative models like GPT-3 and DALL-E, OpenAI offers API access to businesses that want to develop applications on top of its foundational models while being able to plug these models into their products and customize these models with proprietary data and additional AI features. On the other hand, OpenAI also released ChatGPT, developing around a freemium model. Microsoft also commercializes opener products through its commercial partnership.
OpenAI and Microsoft partnered up from a commercial standpoint. The history of the partnership started in 2016 and consolidated in 2019, with Microsoft investing a billion dollars into the partnership. It’s now taking a leap forward, with Microsoft in talks to put $10 billion into this partnership. Microsoft, through OpenAI, is developing its Azure AI Supercomputer while enhancing its Azure Enterprise Platform and integrating OpenAI’s models into its business and consumer products (GitHub, Office, Bing).
Stability AI is the entity behind Stable Diffusion. Stability makes money from our AI products and from providing AI consulting services to businesses. Stability AI monetizes Stable Diffusion via DreamStudio’s APIs. While it also releases it open-source for anyone to download and use. Stability AI also makes money via enterprise services, where its core development team offers the chance to enterprise customers to service, scale, and customize Stable Diffusion or other large generative models to their needs.
Gennaro is the creator of FourWeekMBA, which reached about four million business people, comprising C-level executives, investors, analysts, product managers, and aspiring digital entrepreneurs in 2022 alone | He is also Director of Sales for a high-tech scaleup in the AI Industry | In 2012, Gennaro earned an International MBA with emphasis on Corporate Finance and Business Strategy.
