**13. Different categories of quantum computer**

### **13.1 Analog quantum computer**

This type of system performs its operation by manipulating the analog values in the Hamiltonian representation. It does not use quantum gates. It includes *quantum annealing, quantum simulation and adiabatic quantum computing*. The quantum annealing is done using some initial set of qubits that gradually changes the energy encountered by the system until the problem parameters are defined by Hamiltonian. This is done in order to get the highest probability final state of the qubits that corresponds to the solution of that problem. The adiabatic quantum computer performs computation using some initial set of qubits in the Hamiltonian ground state and then Hamiltonian is changed slowly enough such that it stays in its ground state or lowest possible energy while the process takes place. It has processing power similar to a gate-based computer but still cannot perform full error correction.

There are three basic types of analog quantum computing. These are divided on the basis of the required amount of processing power (number of qubits) and time to become practically and commercially available.

#### • Quantum Annealing

A basic rule of physics is that everything inclines towards a minimum energy state of a problem. This behavior is also true in the world of quantum physics. Quantum annealing is naturally used for real low-energy solutions such as optimization problems [22]. It is useful where the best solution is needed out of all possible solutions available. However, it is least powerful among all the types available. An example of this demonstrates an experiment to optimize traffic flows in a crowded city. Such an algorithm could successfully decrease traffic by choosing a convenient path. Volkswagen performs this with Google and D-wave system partnership. Such an experiment can be applied on a universal scale for all to get the cost-productive travel. This method can be applied to a collection of industry problems. For example, optimization of the flight route, petroleum price, weather and temperature information and passenger details, developing commercial aircraft.

Quantum annealing is also used for digital modeling, sampling problems and other science fields. This will take only a couple of hours to model all the individual atoms of air flowing over an airplane's wing at every tilts and speeds to formulate an optimized wing design. Using a sampling problem from energy-based distribution, the shape of energy can be characterized and is useful in machine learning problems. The samples improve the model using information about the state of the model for the given parameters.

#### • Quantum Simulation

Quantum simulations examine certain problems in quantum mechanics that are beyond classical physics. Simulating quantum phenomena that are complex in nature is one of the most important applications of quantum computing such as quantum chemistry. It includes modeling of chemical reactions on a large number of quantum subatomic particles. Quantum simulators can be used to simulate the misfolded protein structure [23]. Diseases like Alzheimer's are caused by misfolded proteins. Using random computer simulation, researchers test new treatment drugs and learn reactions. To achieve correctly folded protein structure and study all drug-induced effects, sequential sampling is done which could take more than a

million years. Quantum computers can help evaluate it for making more effective treatments and medicines and it would be a significant healthcare improvement. In the future, quantum simulations will facilitate quick drug designing and testing by evaluating every possible drug combinations of protein.

• Adiabatic Quantum Computing

Adiabatic quantum computing is the most dominant, commonly applicable and hardest to create. A truly adiabatic quantum computer will use over a million of qubits. The maximum qubits we can access is less than 128 today. The basic idea behind this is that the machine can be directed at any complex calculation and obtain an immediate solution. This comprises analyzing the annealing equations, quantum phenomena simulation, etc. [24]. At least fifty unique algorithms other than Shor's and Grover's algorithm have been formulated to run on this quantum computer.

There is a possibility that quantum computers could revolutionize the area of artificial intelligence and machine learning. Some work has been done on algorithms that would operate as building blocks of machine learning but the hardware and software for quantum AI are still not practically accessible.
