It was only a matter of time until the topic of what quantum mechanics meant to information processing arose, since quantum theory inspired physicists to consider all elements of their subject from a fresh viewpoint. In some ways, this has already been a feature of quantum mechanics' early days, with nonlocality disputes and Einstein's "spooky action at a distance." Quantum computing research, on the other hand, did not become a separate sub-discipline until the late 1980s.
What is quantum information, and how does it work? These are the core problems of this field. Is it possible to create a new kind of computer based on quantum systems? On such devices, how can we write algorithms? What does quantum theory signify in terms of computing's limits? What sets quantum computers apart from traditional computers? Quantum computing is still touted as the cure-all in the media (and potentially by researchers themselves looking for money), as shown in the following statement from a machine learner's perspective:
- Quantum computing's image has been damaged by great claims and few real outcomes, much like artificial intelligence's in its early days.
- Quantum computers are frequently accompanied with promises of polynomial-time solutions to NP-Hard problems and other similar unrealistic calls to hope.
It's true that, despite more than 30 years of developing an independent research discipline, there's still no definitive solution to many of the problems raised. We still don't know how a quantum Turing machine compares to a conventional Turing machine, for example. Nonetheless, we know a great deal more. We know, for example, that quantum algorithms get slower in runtime as the amount of the input increases, compared to known conventional methods handling the same issue.
Quantum algorithms have been shown to be quicker than any imaginable classical algorithm when compared to a black-box function or oracle. Another significant finding is that every classical algorithm can be implemented on a quantum computer with just polynomial overhead, implying that quantum computers are at least as good as conventional computers in terms of asymptotic complexity.
There has been formulated a vast landscape of quantum computational models and procedures.
Shor's factorisation method and Grover's search algorithm were two significant quantum algorithms.
In 2009, Harrow, Hassidim, and Lloyd published a quantum method for linear systems of equations, which became a watershed moment. Quantum machine learning formed its own sub-discipline of quantum computing about 2013.
Although there are many computational models that formalize the concept of quantum computation, the majority of them are based on the concept of a qubit, which is a quantum system connected with two measurable events, similar to a random bit or biased coin flip as we saw in the introduction. Many prominent theories of quantum information say that quantum computers' strength comes from the fact that qubits can be in a linear combination of 0 and 1, allowing them to take on "any states in between."
However, a traditional random bit (e.g., a traditional coin toss) has this quality to some extent.
As a result, sampling algorithms are frequently the best rivals for quantum procedures. One significant distinction is that in the quantum scenario, the coefficients of the linear combination—the amplitudes—can be complex values. We learned how a given development might cause amplitudes to cancel out or interfere with one another. This characteristic, together with additional complexities, can result in non-classical measurement statistics, which cannot be recreated by classical systems.
A quantum computer is a physical implementation of n qubits (or other fundamental quantum systems) with precise control over the state development. A quantum algorithm is the controlled manipulation of a quantum system followed by a measurement to extract information from it. A quantum computer may be thought of as a specific form of sampling device in this sense: we pick certain experimental configurations—such as the power of a laser beam or the magnetic field—and read out samples from a distribution specified by the quantum state and the observable.
Any quantum development (consider manipulating a physical system in the lab) may be mimicked by a sequence of only a few simple manipulations, known as quantum gates, which only act on one or two qubits at a time. Quantum algorithms are commonly formulated as quantum circuits of these simple gates as a result of this knowledge. As a result, much as conventional computers are constructed on a restricted number of logic gates, a universal quantum computer only has to know how to conduct a tiny set of operations on qubits. The number of quantum gates required to implement the whole quantum method is frequently counted in runtime considerations.
Efficient quantum algorithms are built on evolutions whose breakdown into a circuit of gates grows polynomially with the problem's input size. A qubit can be used as a model of many possible physical quantum systems, much as a classical bit is an abstract model of a binary system that can have many different physical realisations in principle. Superconducting qubits, photonic setups, ion traps, and topological features of quasi-particles are some of the current options for implementation. Each has advantages and disadvantages, and future designs are likely to incorporate a combination of these technologies.