Fundamentals of Quantum Computing, Quantum Supremacy, and Quantum Machine Learning

When we aim to demonstrate that a programmable quantum device can solve complex problems which cannot be addressed by classic computers, this fundamental goal is known as quantum supremacy. This concept has changed every fundamental rule of computation. In this chapter, the detailed concept of quantum computing and quantum supremacy is explained along with various open source tools and real-time applications of this technology. The major base concepts, quantum computing, the difference between classical and quantum computer on physical level, programing quantum device, and the experiment-quantum supremacy are explained conceptually. This chapter also includes an introduction of the tools Cirq and OpenFermion plus the applications like quantum simulation, error mitigation technique, quantum machine learning, and quantum optimization, which are explained with illustrations.

Quantum Algorithms

In the last two decades, the interest in quantum computation has increased significantly among research communities. Quantum computing is the field that investigates the computational power and other properties of computers on the basis of the underlying quantum-mechanical principles. The main purpose is to find quantum algorithms that are significantly faster than any existing classical algorithms solving the same problem. While the quantum computers currently freely available to wider public count no more than two dozens of qubits, and most recently developed quantum devices offer some 50-60 qubits, quantum computer hardware is expected to grow in terms of qubit counts, fault tolerance, and resistance to decoherence. The main objective of this chapter is to present an introduction to the core quantum computing algorithms developed thus far for the field of cryptography.

Recent Progress in Quantum Machine Learning

Quantum computing is a new exciting field which can be exploited to great speed and innovation in machine learning and artificial intelligence. Quantum machine learning at crossroads explores the interaction between quantum computing and machine learning, supplementing each other to create models and also to accelerate existing machine learning models predicting better and accurate classifications. The main purpose is to explore methods, concepts, theories, and algorithms that focus and utilize quantum computing features such as superposition and entanglement to enhance the abilities of machine learning computations enormously faster. It is a natural goal to study the present and future quantum technologies with machine learning that can enhance the existing classical algorithms. The objective of this chapter is to facilitate the reader to grasp the key components involved in the field to be able to understand the essentialities of the subject and thus can compare computations of quantum computing with its counterpart classical machine learning algorithms.

Post-Quantum Lattice-Based Cryptography

Quantum cryptography is a branch of cryptography that is a mixture of quantum mechanics and classical cryptography. The study of quantum cryptography is to design cryptographic algorithms and protocols that are against quantum computing attacks. In this chapter, the authors focus on analyzing characteristics of the quantum-proof cryptosystem and its applications in the future internet. Lattice-based cryptography provides a much stronger belief of security, in that the average-case of certain problems is equivalent to the worst-case of those problems. With the increase in cryptanalytic attacks conventional cryptographic schemes will soon become obsolete. As the reality of quantum computing approaches, these cryptosystems will need to be replaced with efficient quantum-resistant cryptosystems. We need an alternate security mechanism which is as hard as the existing number theoretic approaches. In this chapter, the authors discuss the security dimension of lattice-based cryptography whose strength lies in the hardness of lattice problems and also study its application areas.

Quantum Security for IoT to Secure Healthcare Applications and Their Data

Quantum computation has the ability to revolutionize the treatment of patients. Quantum computing can help to detect diseases by identifying and forecasting malfunctions. But there's a threat associated here (i.e., healthcare data among the most popular cybercriminal targets, IoT devices notoriously lacking in effective safeguards, and quantum computers on the brink of an encryption/decryption breakthrough). Health agencies need a security prognosis and treatment plan as soon as possible. Healthcare companies recently worry more about the quantum security threats. The biggest threat of healthcare data breaches has come in the form of identity theft. There should be a strong mechanism to combat the security gaps in existing healthcare industry. If the healthcare data are available on the network, an attacker may try to modify, intercept, or even view this data stream. With the use of quantum security, the quantum state of these photons changes alert the security pros that someone is trying to breach the link.

Advances of Quantum Machine Learning

The basic idea of artificial intelligence and machine learning is that machines have the talent to learn from data, previous experience, and perform the work in future consequences. In the era of the digitalized world which holds big data has long-established machine learning methods consistently with requisite high-quality computational resources in numerous useful and realistic tasks. At the same time, quantum machine learning methods work exponentially faster than their counterparts by making use of quantum mechanics. Through taking advantage of quantum effects such as interference or entanglement, quantum computers can proficiently explain selected issues that are supposed to be tough for traditional machines. Quantum computing is unexpectedly related to that of kernel methods in machine learning. Hence, this chapter provides quantum computation, advance of QML techniques, QML kernel space and optimization, and future work of QML.

Quantum Cryptography for Securing IoT-Based Healthcare Systems

IoT-based healthcare is especially susceptible as many IoT devices are developed without keeping in mind the security issue. In addition, such smart devices may be connected to global networks to access anytime, anywhere. There are some security challenges like mobility, computational limitation, scalability, communication media, dynamic topology, and above all the data confidentiality in storage or in transmission. There are some security protocols and methodology which is used in IoT-based healthcare systems like steganography, AES cryptosystems, and RSA cryptographic techniques. Therefore, it is necessary to use quantum cryptography system to make sure the security, privacy, and integrity of the patient's data received and transmitted from IoT-based healthcare systems. Quantum cryptography is a very fascinating domain in cyber security that utilizes quantum mechanics to extend a cryptosystem that is supposed to be the unbreakable secure system.

The Role of Quantum Computing in Software Forensics and Digital Evidence

Quantum computing has immense computational advantages. It escorts today's world of computing towards qubits universe of computing by the logical superposition technique. Various new technologies will come to reality with replacement of existing problem-solving methodology. The development of quantum computing imposes significant impact on cyber security and digital forensics technologies. Cybercrimes may be dramatically increased and malicious code will get ability to harm speedily. The quantum computing in software forensics methodology needs to develop in order to counter the challenges such as traceability of malicious code automation, sources of malicious code generation, intellectual property right theft issues, source code validation, plagiarism, breach of copyright issues, and an acquisition of digital evidence with quality and quantity with the wings of quantum forensics. This chapter aims to concentrate on the key issues of quantum computing approach in the field of software forensics with ontological aspects.

Image Processing Using Quantum Computing

Image processing via the quantum platform is an emerging area for researchers. Researchers are more interested to move on towards quantum image processing instead of classical image processing. This chapter starts with the review of different quantum image computing-based research papers with a brief idea of the ethics which inspire quantum computing in the background and focus on the current scenario of recent trends of quantum image representation, pitfalls, and summarization of the pros and cons of it, with the limitations of the technologies used and focus on the recent work to be going on and application of it in a different field. In the next, it will focus on the different methods used by the researcher in the previous papers. The next section discussed the different methods based on quantum image representation used. Some different techniques of image storage, retrieval, and representation in a quantum system are discussed. Also, this chapter briefs the pros and cons of using different techniques in quantum systems in comparison to classical systems.

Quantum Cryptography

Cryptography is used for the secure communication in which two parties are involved. The most popular cryptographic issue is the transmission of confidential messages. The privacy is maintained using the cryptographic protocol. The security of quantum cryptography relies more on physics including quantum mechanics and statistics rather than on solving mathematical problems. A well-known application of quantum cryptography is quantum key distribution (QKD) that is used to establish communication by generating cryptographic keys. Moreover, it is based on the Heisenberg uncertainty principle that ensures the security and prevents from eavesdropping. Basically, quantum cryptography with faint laser pulses, polarization coding, phase coding, and frequency coding have been discussed.