Types of Quantum Computing | Quantum Computing Primer — Chapter 3
In chapter 2 we saw the motivations behind the upcoming quantum computing era and in this chapter we are gonna have a quick look at the types of quantum computing.
Types of quantum computing
There are three primary types of quantum computing. Each type differs by the amount of processing power (qubits) needed and number of possible applications, as well as the time required to become commercially viable.
QUANTUM ANNEALING
Quantum annealing is best for solving optimization problems.
In other words, researchers are trying to find the best (most efficient) possible configuration among many possible combinations of variables.
For example, Volkswagen (VW) recently conducted a quantum experiment to optimize traffic flows in the overcrowded city of Beijing, China. The experiment was run in partnership with Google and D-Wave Systems.
The algorithm could successfully reduce traffic by choosing the ideal path for each vehicle, according to VW.
Imagine applying this experiment on a global scale — optimizing every airline route, airport schedule, weather data, fuel costs, and passenger information, etc. for everyone, to get the most cost efficient travel and logistics solutions.
Classical computers would take thousands of years to compute the optimum solution to such a problem. Quantum computers, theoretically, can do it in a few hours or less, as the number of qubits per quantum computer increases.
Annealing applies to an array of industry problems. For example, Airbus — a global aerospace & defense corporation known for developing military and commercial aircraft — established a quantum computing unit at its Newport, UK plant in 2015.
The company is exploring quantum annealing for digital modeling and materials sciences.
While it currently takes engineers years to model the process of air flowing over an aircraft’s wing, a quantum computer could take just a few hours to model every single atom of air flowing over a wing at all angles and speeds to determine the optimum or most efficient wing design.
Quantum annealing is the least powerful and most narrowly applied form of quantum computing.
In fact, experts agree that today’s supercomputers can solve some optimization problems on par with today’s quantum annealing machines.
QUANTUM SIMULATIONS
Quantum simulations explore specific problems in quantum physics that are beyond the capacity of classical systems. Simulating complex quantum phenomena could be one of the most important applications of quantum computing.
Simulating quantum mechanics is known to be a difficult computational problem, especially when dealing with large systems. However, this difficulty may be overcome by using some controllable quantum system to study another less controllable or accessible quantum system, i.e., quantum simulation. Quantum simulation promises to have applications in the study of many problems in, e.g., condensed-matter physics, high-energy physics, atomic physics, quantum chemistry and cosmology.
Quantum simulation could be implemented using quantum computers, but also with simpler, analog devices that would require less control, and therefore, would be easier to construct. A number of quantum systems such as neutral atoms, ions, polar molecules, electrons in semiconductors, superconducting circuits, nuclear spins and photons have been proposed as quantum simulators.•
One area that is particularly promising includes modeling the effect of a chemical stimulation on a large number of subatomic particles — otherwise known as quantum chemistry.
In particular, quantum simulators could be used to simulate protein folding — one of biochemistry’s toughest problems.
Misfolded proteins can cause diseases like Alzheimer’s and Parkinson’s, and researchers testing new treatments must learn which drugs cause reactions for each protein through the use of random computer modeling.
It is said that if a protein were to attain its correctly folded configuration by sequentially sampling all the possible drug-induced effects, it would require a time longer than the age of the universe to arrive at its correct natural state.
A realistic mapping of the protein folding sequence would be a major scientific and healthcare breakthrough that could save lives.
Quantum computers can help compute the vast number of possible protein folding sequences for making more effective medications. In the future, quantum simulations will enable rapid designer drug testing by accounting for every possible protein-to-drug combination.
UNIVERSAL QUANTUM COMPUTING
Universal quantum computers are the most powerful and most generally applicable, but also the hardest to build. A truly universal quantum computer would likely make use of over 100,000 qubits — some estimates put it at 1M qubits. Remember that today, the most qubits we can access is not even 128.
The basic idea behind the universal quantum computer is that you could direct the machine at any massively complex computation and get a quick solution. This includes solving the aforementioned annealing equations, simulating quantum phenomena, and more.
Researchers have been designing algorithms for years that are only possible on a universal quantum computer. The most well-known algorithms are Shor’s algorithm for factoring numbers (to be used for advanced code breaking), and Grover’s algorithm for quickly searching unstructured and massive sets of data (to be used for advanced internet search, etc).
At least 50 other unique algorithms have been developed to run on a universal quantum computer.
Algorithms based on the quantum Fourier transform
Deutsch–Jozsa algorithm
* Bernstein–Vazirani algorithm
* Simon's algorithm
* Quantum phase estimation algorithm
* Shor's algorithm
* Hidden subgroup problem
* Boson sampling problem
* Estimating Gauss sums
* Fourier fishing and Fourier checkingAlgorithms based on amplitude amplification
* Grover's algorithm
* Quantum countingAlgorithms based on quantum walks
* Element distinctness problem
* Triangle-finding problem
* Formula evaluation
* Group commutativityBQP-complete problems
* Computing knot invariants
* Quantum simulation
* Solving a linear systems of equationsHybrid quantum/classical algorithms
* QAOA
* Variational quantum eigensolversource
In the distant future, universal quantum computers could revolutionize the field of artificial intelligence.
Quantum AI could enable machine learning that is faster than that of classical computers.
Recent work has produced algorithms that could act as the building blocks of quantum machine learning, but the hardware and software to fully realize quantum artificial intelligence are still as elusive to us as a general quantum computer itself.
