Gate-based computers use “quantum bits” as their base unit of information. These quantum bits are comparable to the “bits” of classical computers. Instead of “logic gates,” they use “quantum gates,” and they perform computation by changing the states of quantum bits.
Quantum bits, which are composed of by applying quantum phenomena, can have a state that seems to contain a “0” and a “1” at the same time, unlike a classical computer bit, which is only a “0” or a “1.” By arranging sequences of quantum bits, which contain an even greater amount of information, gate-based quantum computers can contain an overwhelmingly larger number of states than a classical computer. Furthermore, quantum gates can simultaneously change the diverse states within a quantum bit, which gives them a far higher level of parallelism than a classical computer. Because of this, it is hoped that they will drive a tremendous leap in computing performance.
The gate approach makes it possible to create various programs by appropriately lining up quantum gates, so it is considered a “general purpose” approach. However, algorithms must be crafted with care in order to be able to rapidly extract useful information from the varied states of the bits. Shor’s algorithm is a famous algorithm used in prime factorization, which could lead to a compromise of the encryption systems, but there are still few effective algorithms for gate approach.
Another challenge facing systems that use the gate approach is hardware scalability. They function by applying quantum phenomena, so it is vital that they maintain stable quantum states. This requires advanced cooling technologies, so it is expected to take quite some time before we see Fault Tolerant Quantum Computers (FTQCs), large-scale fault-tolerant quantum computers. Therefore, efforts are being made to find practical applications for medium-sized quantum computers known as Noisy Intermediate-Scale Quantum devices, or NISQ devices, but even here, there is a challenge to find effective algorithms.
Now let’s look at another type of quantum computer: the Ising machine. These machines are named after 20th century German physicist Ernst Ising. Ising developed the Ising model to explain the properties of magnetism. Computers that use this model to express and rapidly solve combinatorial optimization problems are called Ising machines. Unlike systems that use the gate approach, which is envisioned as being used for general purpose problems, Ising machines are quantum computers that are designed exclusively to solve combinatorial optimization problems.
Ising machines are sometimes called “annealers.” They are based on simulated annealing algorithms that were developed to use software to simulate and optimize annealing, a process of heat treatment used to improve the properties of metals. The idea of applying these algorithms in quantum systems to solve combinatorial optimization problems was first proposed by Dr. Hidetoshi Nishimori of the Tokyo Institute of Technology in 1998, and it was called “quantum annealing.” Ising machines and annealers have now come to mean almost the exact same thing, and in this article we will refer to them as “Ising machines.”
Currently, for technical reasons, it is expected to take quite some time before either gate or Ising machine approaches can be used in actual quantum computers that leverage quantum mechanics on a practical scale. This is why, as we look toward the future creation of full-fledged quantum computers, people have proposed simulators which use software running on classical computers to simulate the behavior of quantum computers. This will make it possible to test quantum computer applications in advance.
For now, simulators are used to apply these techniques to various applications, with the belief that when full-fledged quantum computers become available in the future, we will be able to replace the simulators with hardware. By using a simulator to provide an environment with the same interfaces, all kinds of application ideas can be sought from around the world, even before there is any practical hardware to implement them on.
Several state vector methods have been proposed for gate-based simulators, but even supercomputer simulations are limited to roughly 50 quantum bits, so they are not yet feasible.
On the other hand, Ising machines are known to be able to produce practical and meaningful solutions on simulators, even for large-scale problems that cannot be solved using modern hardware. This is why they are being applied in simulators before large-scale hardware becomes available.
Quantum computer technologies are therefore divided into general-purpose systems and systems designed specifically for combinatorial optimization problems. They are also divided into hardware and software (simulator) systems, producing four categories: “gate-based hardware,” “gate-based simulators,” “Ising machine hardware,” and “Ising machine simulators” (Fig. 2).