The frameworks in this book are called the CQ approach, which treats the systems that generate data as classical systems and uses quantum systems as data processing devices.
The circuit that converts the qubits is described by unitary operators, and this quantum computation is described in braket form.
At first glance, it seems as if quantum computers is able to compute in parallel so as to get all results from a function, but in fact, the only one answer can be sampled from qubit. In other words, a random quantum state must be determined by measuring qubits. However, by making good use of quantum circuits, the probability of obtaining the desired values can be increased. Therefore, what Quantum Computer users should do is to increase the probability the will yield the solution users wishes to obtain.
There are candidate implementations of qubits, such as superconducting qubits, light-based qubits, ion traps, and topological properties of heavy particles.
Unlike classical systems, in quantum systems linear combination coefficients can be complex, and quantum systems and their peculiar properties cannot be represented in classical systems.
The Deutsch-Josza algorithm is a general extension of Deutsch’s algorithm to have multiple input values that allows one to obtain information about the global properties of a function. Here, this function is either a constant function or a balanced function. In a classical computer, the function must be evaluated 2^(n-1)+1 times
As of 2020, The Quantum Annealers can only handle quadratic unconstrained binary optimisation problems
In data mining and machine learning field using Quantum Computers, the problem of encoding information is being main topic. There are several encoding schemes such as ’Basis Encoding’, ‘Amplitude Encoding’, ‘Qsample Encoding’, and ‘(Hamiltonian)Dynamic Encoding’. Since the topic is rarely discussed in general quantum computation, it seems worth to refer to this book.
