What is a Quantum Computer? What are the Challenges in the Development of Quantum Computing?
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What is a Quantum Computer? What are the Challenges in the Development of Quantum Computing?

The real power of quantum computers is not the speed of computing, but the ability to process problems in parallel. By harnessing the uncertainty of quantum physics, it could revolutionize medicine, accelerate artificial intelligence, and upend cryptography.
Published: Sep 22, 2022
What is a Quantum Computer? What are the Challenges in the Development of Quantum Computing?

Quantum computers, with the potential of their powerful computing speed and power, have attracted global scientific and industrial circles. Technological breakthroughs in recent years seem to bring the computing power of quantum computers closer to commercial applications. In today's era of the knowledge economy and globalized competition, computing power is the competitiveness of countries and enterprises. Therefore, quantum computers have been regarded as a key development technology by various countries in recent years.

What is Quantum Computing?

A quantum bit (qubit) is the most basic operation unit of a quantum computer. For a qubit to be used, the quantum must reach quantum superposition and quantum entanglement, a single quantum must be in two physical states at the same time, and the two quanta must be in between. A connection is formed so that even if the two quanta are not in the same space, they can instantly influence each other so that they can be used as the basic unit of quantum computing. Quantum can be electrons, ions, or photons. If it can achieve superposition and entanglement, it can be used as qubits. The reading and writing of qubits can be transmitted through microwaves, magnetic pulses, or lasers. At present, there are five main quantum computing methods: silicon spin quantum, ion trap, superconducting loop, diamond vacancy, and topological quantum.

Qubits and Probabilities:

Traditional computers store and process information in bits, which can be either 1 or 0, but not both. The equivalent of quantum operations is qubits. A qubit is any quantum system in which two quantum states (0 and 1) can be superimposed. Each possible quantum state has an associated probability magnitude. Only after the qubit is measured, the state of the qubit collapses to either a 0 state or a 1 state, depending on the associated probability, so a certain probability will take one of the possible states. The probability of which way the qubit will collapse is determined by quantum interference. Quantum interference affects the state of a qubit to interfere with the probability of a particular outcome during measurement, and this probability state is the remarkable capability of quantum computing.

Quantum Simulation:

Quantum mechanisms are the fundamental operating systems of the universe, which describe how the fundamental building blocks of nature behave. Essential behaviors, such as chemical reactions, biological reactions, and material formations, often involve quantum interactions of many agents. Qubits can be used to represent the natural state of the problem if the quantum-mechanical system is modeled in essence. Examples of quantum systems that can be modeled include photosynthesis, superconductivity, and complex molecular formation.

Quantum Acceleration:

One of the goals of quantum computing research is to study what problems quantum computers can solve faster than conventional computers, and how fast they can be accelerated. Shor's algorithm executed on a quantum computer could disrupt traditional cryptographic configurations, such as the Rivest–Shamir–Adleman (RSA) configuration, which is widely used in e-commerce for secure data transfer. This mechanism uses traditional algorithms to decompose prime numbers according to the actual difficulty of decomposing prime numbers. Quantum encryption ensures information security by applying fundamental physics rather than complexity assumptions.

Quantum Machine Learning:

Machine learning on traditional computers has revolutionized the scientific and business worlds. But the computational cost of finalizing the model hinders its development and field of application. The field of quantum machine learning explores how to design and implement quantum software so that machine learning can perform faster than conventional computers. The Quantum Development Kit (QDK) comes with a quantum machine learning library to perform hybrid quantum/traditional machine learning experiments. This library contains examples and tutorials and provides the necessary tools to implement new hybrid quantum and traditional algorithms (circuit-centric quantum classifiers) for supervised classification problems.

Why are Quantum Computers More Powerful than Traditional Computers?

Unlike traditional computers, quantum computers are limited by the number of bits in the operation steps. There is a linear relationship between the number of possible groups of numbers, and when the possibilities to operate grow exponentially, even a supercomputer will take too long to solve the problem. Due to its special quantum characteristics, quantum operations can directly obtain solutions in 16 possible situations after 4 operations when faced with the problem of 4-bit combination numbers, and 20-bit combinations can be found after 1000 operations. One million possible sets of specific numbers, and the number of operations only needs to be the square root of the total number of possible cases, which satisfies the exponential complex operational requirements.

Compared with traditional digital computers, quantum computers process information in a completely different way. When dealing with specific problems, not only will they have more computing power, but also faster computing speeds. That's because quantum computers can process multiple qubits in parallel, unlike digital computers that can only operate sequentially.

What Technical Challenges will there be in Developing Quantum Computing?

  • Maintenance of stable quantum state:
    Delicate quantum states are very susceptible to disturbances by vibrational or electromagnetic fields, or even general thermal disturbances, so today's quantum computers need to operate at ultra-low temperatures close to absolute zero. In addition to increasing the number of qubits, the main technical bottleneck at present is how to maintain a stable quantum state, so that the quantum remains in a certain quantum state for a long enough time to complete the operation and increase the accuracy of the operation. Using the braided tangle method makes the qubits resistant to external interference and makes the qubits as stable as the knots. In this way, the computing power of the quantum computer will no longer be wasted on correcting errors.
  • Scalability of qubits:
    Another key to enabling quantum computers to enter practical applications is the scalability of qubits. Silicon spin quantum, one of the current mainstream quantum computing technologies, is due to the use of already mature semiconductor technology. It is easy to expand upwards in the future, attracting intel and other researchers to invest in R&D. The Princeton university lab recently made a key technological breakthrough in silicon quantum components, producing silicon-based components that can accurately control the quantum behavior between two electrons with extremely low error rates. This ground-breaking qubit logic gate, made of highly ordered silicon crystals lined with tens of nanometers of aluminum oxide wires, delivers voltage and traps two electrons separated by energy levels. Specific quantum dots, and then use the short-term lowering of the energy level gap, enable two electrons to exchange information with each other and achieve a state of quantum entanglement. This research is the first time that quantum entanglement has been successfully achieved in silicon. Researchers can use magnetic fields to control the behavior of qubits. At present, the stability of control electron quantum states is 99%, and the reliability of logic gates is 75%. In addition to the scalability of this technology, the error rate may decrease in the future.
  • Quantum software research and development:
    To make quantum computers truly effective, experts believe that quantum software should be developed in parallel. The complexity and difficulty of quantum computing programs originate from the nature of quantum computers, and there will be a certain degree of noise during operations. Therefore, the physical principles and bit limitations of quantum computers must be considered when programming and noise need to be pre-built mock the model to deal with the correctness of operations. However, since the computing hardware design of the early developed quantum computers has not yet been unified, they will have nuances of different properties, and the software needs to be customized to a certain extent. The high complexity of operations will also drive the demand for new algorithms and development tools. Quantum computer software designers need to have profound knowledge of physics, mathematics, and software engineering. The cultivation of talents with in-depth knowledge in various fields will be an important task. The key to software research and development, and many quanta software have open-source community development platforms, to accelerate early software development with collective efforts and resources.

The Impact of Quantum Computers on Society?

  • Combination of a quantum computer and artificial intelligence:
    The strengths of quantum computers lie in generating random numbers, finding the minimum value of an unsorted sequence, solving node connection problems in graph theory, and matching characteristics. Scientists have designed a variety of quantum algorithms to solve problems that are difficult for traditional computers to solve. Quantum algorithms can quickly solve multi-degree-of-freedom, huge linear algebra problems, and machine learning relies heavily on this type of linear algebra operation. Therefore, experts soon began to try to combine quantum algorithms and machine learning. Machine learning is a handful of fields that can find niches in the early days of quantum computing. Although traditional machine learning will still start to be practically applied in the transportation, medical and financial markets in the short term. Quantum systems will have a greater advantage in generating real random numbers and processing non-traditional binary data, such as traditional common applications. Monte Carlo machine learning algorithms for financial markets need to generate real random numbers to perform optimally. At this time, the strengths of quantum computers can be demonstrated. Many quantum machine learning startups have begun to develop how to use quantum systems to accelerate machine learning, and their market potential has attracted a lot of capital.
  • Application of quantum computers in chemistry and pharmacy:
    General experts generally believe that chemistry will be the strongest and most immediate application of quantum computing. Quantum computers will be used to help design catalysts for clean energy, understand enzymes in living organisms, and discover new materials for solar cells or high-temperature superconductors. Its advantage is that it exceeds the powerful computing power of existing traditional computers, which is enough to simulate and create complex interaction models of electrons and molecules. Generally, when simulating chemical reactions, it is necessary to understand the interaction of atoms contained in each molecule, and it is necessary to calculate each electron interaction energy of an atom, including the positions and energy levels of all electrons. When the existing traditional computer has 125 orbits, it needs a memory that exceeds the number of all atoms in the universe to store all the information, and cannot process such a large amount of complex data and calculations. Therefore, when current quantum chemists build models, they often must deliberately omit some behavioral characteristics of electrons, especially in the case of strong interactions between electrons. This approximation algorithm is acceptable when simulating organic chemical molecules, but in metal molecules in this example of many electrons squeezed into a small space, the strong interaction between electrons is its essence. If it is ignored, it is impossible to truly understand the actual chemical principle. Examples of similar traditional methods that cannot be simplified include high-temperature superconductor materials, Metal-containing enzyme active sites, etc. However, the superposition characteristics of qubits enable quantum computers to easily perform such operations, making great contributions to the research and development of new drugs and new materials. Once the technology is mature, the early stage of new drug research and development will be able to simulate the structure of compounds and enzymes in vivo through quantum computers. The interaction of receptors, better prediction of efficacy and side effects, reduced development time and cost, and manufacturers who are familiar with and understand how to use quantum computing will have an advantage in new drug design.
  • Threats of quantum computers to the security of the Bitcoin market and blockchain:
    The security of the virtual currency bitcoin and other applications using blockchain technology lies in its high encryption strength, which is not easy to be cracked by traditional computers. When quantum computer technology that is good at complex operations and password cracking gradually matures, will it Threats to these applications? For example, the current Bitcoin protocol uses the generation of a specific random number (nonce) as one of the necessary conditions for the generation of a new blockchain, and the generation of this random number requires a lot of computing power. Miner’s mining is to provide computing power and obtain Bitcoin as a reward. However, occasionally two groups of miners will announce two different blocks. At this time, the Bitcoin protocol will focus on the block that has completed more operations and discard the other backward block, which will lead to most operations in the network. The capable miners always get the next block and become the masters who control the Bitcoin ledger. If quantum computers join the mining ranks and demonstrate overwhelming computing power beyond other miners, the entire bitcoin market could collapse. Comparing the prediction of the computing power of quantum computers in the next ten years with the growth of the computing power of circuits currently used for mining. It is found that the existing hardware can still have an advantage in speed in the next ten years, and quantum computers will dominate Bitcoin mining. The situation should not happen immediately.
    However, another security protocol feature of Bitcoin, the elliptic curve signature, may expose weaknesses faster under quantum computing. The owner of Bitcoin will hold a private key and release a public key. In the case of, use the public key to prove that you are the owner of the bitcoin, and the public key can be easily generated from the private key, but not vice versa. Although it is difficult for traditional computers to calculate the private key from the public key, it is easy for quantum computers, and it is estimated that this security agreement may be broken. Although Bitcoin and other virtual currencies have not yet become popular, their transaction popularity and market acceptance are increasing day by day. When the financial market gradually begins to accept and try to build a digital trading platform based on password protection, quantum computing technology has a great impact on the blockchain. The threat and the impact on the future financial market cannot be underestimated. In the foreseeable future, this battle between the spear and the shield will become more and more intense with the gradual maturity of quantum computing and the popularization of blockchain technology.
Published by Sep 22, 2022 Source :narlabs

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