What is Quantum Computing?
Quantum computers, like classical computers, can be used to perform computations — or in simpler terms — to calculate or process information. What makes quantum computing different, however, is the fact that it makes use of quantum phenomena such as superposition and entanglement to perform computations.
Quantum computing is revolutionary; with quantum computing, we can carry out complex calculations efficiently at a much faster rate. Quantum computers allow us to solve problems that are impossible or would take a traditional computer an incredibly long time (e.g. a billion years) to solve. To put into context, here are some examples of how quantum computers can fundamentally change many aspects of our life:
The “Perfect” Encryption?
Those that are familiar with the Diffie-Hellman key exchange algorithm know that it is a computationally secure way to exchange a shared secret key between two parties over public communication channels. There is no computationally effective way for eavesdroppers to obtain that secret key, especially since the numbers used in the algorithm are often extremely large.
This is only one example, but there are more examples of cryptosystems (such as the RSA cryptosystem) with seemingly “perfect” security as there is just simply no efficient way for classical computers to crack the code — that is, unless we use quantum computing.
Quantum computing will be able to effectively crack many of today’s “perfect” encryption techniques in a reasonable amount of time. This could fundamentally change the field of encryption and cryptography. However, it is also important to recognize that while quantum computing can break the current encryption methods, it is also able to provide new quantum encryption techniques that are much more secure and unbreakable!
If you are interested, take a look at Quantum Key Distribution, one the best known and developed applications of quantum cryptography.
Computational Models, Simulations & Predictions
The current modelling or simulations technology do have limitations. For example, in weather forecasts, it is very difficult for classical computers to accurately develop weather and climate prediction models due to the sheer amount of data containing dynamic variables that all interact in some way.
Classical computers are also not fast enough to keep up with the ever-changing climate conditions. This means we are currently unable to produce accurate weather predictions for the next month, next day, or even the next hour. Imagine the benefits of knowing exactly what the weather will be in the next few hours before commuting to work, or planning for a weekend outdoor getaways!
Quantum computing has the potential to boost tracking and predictions of meteorological conditions by handling huge amounts of data containing many variables effectively and efficiently. Quantum machine learning can also help recognize weather patterns more efficiently in the sea of data. All these will result in a more accurate weather prediction globally.
It is not only weather prediction that will hugely benefit from quantum computing though — think about examples of where computer modelling and simulation are used currently (stock prediction, scientific simulations, and many more). With quantum computing, we can get as close to mimicking real life and ensure that the simulations and predictions are as accurate as possible.
Artificial Intelligence and Machine Learning
Quantum computing is not only able to manage exponentially more data than classical computers, it can also carry out better, more complex algorithms that will hugely benefit the field of machine learning. It can, therefore, help to pinpoint patterns or significant features in a complex set of data more effectively.
There are many specific applications of quantum computing in the field of machine learning and artificial intelligence, such as in dimensionality reduction and topological analysis.
The overarching idea, however, is that quantum computing will greatly impact the field of AI and ML in the sense that it can help process more data much more efficiently and effectively. In fact, once we have a stable quantum computer, machine learning will exponentially accelerate, even reducing the time to solve a problem from hundreds of thousands of years to seconds.
The Progress So Far…
Quantum computing was first proposed in the 1980s as a way to improve computations and data processing, and significant progress has been made in these decades. However, there are still some challenges in quantum computing.
For a quantum processor to be stable and work without error, it needs to be kept at temperatures close to absolute zero (-273.15 Celsius or -459.67 Fahrenheit). When we keep the temperature low, we are essentially introducing less energy into the system, meaning that the chances of having a qubit accidentally switch from a state to another (hence causing error) will be reduced.
Quantum computers are also extremely fragile, and any vibration can impact the atoms and cause incoherence. Quantum computers cannot reject noises (small errors). It is very difficult to manage errors caused by both external and internal noises with quantum computers, as qubits are highly sensitive to their environment due to their undetermined states (they can be in 0, 1, or anything in between). External noises could come from stray magnetic fields, and internal noises could come from impurities or unwanted variations from other uncontrolled quantum systems.
As soon as we operate something on qubits, or the qubits are affected in any way, quantum decoherence happens in the sense that qubits will start to lose their original information, causing errors in the outputs. The challenge with managing noises in quantum computers is the exact reason why it is so hard to build large-scale quantum computers, as the more qubits there are, the greater the error rates.
There is also currently no efficient way to input large amounts of classical data and store them in quantum state. Once we measure the state of a quantum data, the large quantum state turns into one single classical data, and we lose the benefit of the quantum computer.
Therefore, to reap the benefits of quantum computing, we must figure out new algorithms that can deal with the unique quantum features such as entanglement and interference before finally arriving at a classical result. Therefore, quantum algorithm development is another critical aspect that we need to focus on.
The last challenge with quantum computing so far is with debugging. The current debugging method relies on memory and the reading of the intermediate machine states. However, as mentioned earlier, quantum information is stored as 0, 1, or anything in between. Therefore, a quantum state cannot simply be observed for debugging purposes, as doing so will collapse the quantum data into normal classical data, stopping the computation.
Implications of Quantum Computers
It is important to recognize that quantum computers are not universally better than classical computers. Functions like desktop publishing, emails, spreadsheets, video productions are all better done on classical computers than quantum computers. Rather, quantum computers simply serve as a different tool to solve a problem that classical computers are not capable of doing. Therefore, quantum computers are not to replace classical computers as a whole, but rather to assist and complement classical computers.
