This chapter introduces both the requirements and challenges for an efficient use of formal methods in quantum computing and the current most promising research directions. While the recent progress in quantum hardware opens the door for significant speedup in cryptography as well as additional key areas (biology, chemistry, optimization, machine learning, etc), quantum algorithms are still hard to implement right, and the validation of quantum programs is a challenge. As an alternative strategy, formal methods are prone to play a decisive role in the emerging field of quantum software. The chapter also introduces several existing solutions for the formal verification of quantum compilation and the equivalence of quantum program runs. The vast majority of quantum algorithms are described within the context of the quantum co-processor model, i.e. an hybrid model where a classical computer controls a quantum co-processor holding a quantum memory.
This chapter introduces both the requirements and challenges for an efficient use of formal methods in quantum computing and the current most promising research directions. While the recent progress in quantum hardware opens the door for significant speedup in cryptography as well as additional key areas (biology, chemistry, optimization, machine learning, etc), quantum algorithms are still hard to implement right, and the validation of quantum programs is a challenge. As an alternative strategy, formal methods are prone to play a decisive role in the emerging field of quantum software. The chapter also introduces several existing solutions for the formal verification of quantum compilation and the equivalence of quantum program runs. The vast majority of quantum algorithms are described within the context of the quantum co-processor model, i.e. an hybrid model where a classical computer controls a quantum co-processor holding a quantum memory.
While recent progress in quantum hardware open the door for significant speedup in certain key areas (cryptography, biology, chemistry, optimization, machine learning, etc), quantum algorithms are still hard to implement right, and the validation of such quantum programs is achallenge. Moreover, importing the testing and debugging practices at use in classical programming is extremely difficult in the quantum case, due to the destructive aspect of quantum measurement. As an alternative strategy, formal methods are prone to play a decisive role in the emerging field of quantum software. Recent works initiate solutions for problems occurring at every stage of the development process: high-level program design, implementation, compilation, etc. We review the induced challenges for an efficient use of formal methods in quantum computing and the current most promising research directions.
Over the past 27 years, quantum computing has seen a huge rise in interest from both academia and industry. At the current rate, quantum computers are growing in size rapidly backed up by the increase of research in the field. Significant efforts are being made to improve the reliability of quantum hardware and to develop suitable software to program quantum computers. In contrast, the verification of quantum programs has received relatively less attention. Verifying programs is especially important in the quantum setting due to how difficult it is to program complex algorithms correctly on resource-constrained and error-prone quantum hardware. Research into creating verification frameworks for quantum programs has seen recent development, with a variety of tools implemented using a collection of theoretical ideas. This survey aims to be a short introduction into the area of formal verification of quantum programs, bringing together theory and tools developed to date. Further, this survey examines some of the challenges that the field may face in the future, namely the development of complex quantum algorithms.
Quantum computing is an emerging paradigm that leverages the principles of quantum mechanics to solve computational problems beyond the reach of classical computers. This article provides an overview of the fundamental concepts of qubits, the distinctive features of quantum mechanics such as superposition and entanglement, and the challenges of building scalable, fault-tolerant systems. It surveys key quantum algorithms and their potential applications in fields including cryptography, optimization, finance, chemistry, and machine learning. Additionally, it highlights the importance of verification frameworks for ensuring the reliability of quantum programs. A literature review of significant contributions is presented, drawing insights from recent surveys on quantum algorithms, qubit technologies, and software verification approaches. The article concludes by discussing ongoing challenges, such as error correction overhead, hardware scalability, and verification complexity, and suggests directions for future research.
Quantum algorithms have emerged as powerful computational tools that leverage the principles of quantum mechanics to address complex problems beyond the scope of classical computing. This chapter explores the foundational concepts, classifications, implementation challenges, and future prospects of quantum algorithms. Key breakthroughs such as Grover's and Shor's algorithms are explored alongside recent developments in quantum machine learning and optimization. The chapter also compares quantum and classical algorithmic complexity, highlighting areas of potential speedup. Practical limitations including hardware constraints, error correction, and resource estimation are critically analyzed. Finally, the chapter outlines emerging research directions and ethical considerations essential for responsible quantum computing development.
Quantum computing has emerged as a paradigm-shifting computational model capable of addressing problems intractable for classical systems. This chapter explores the foundational principles, hardware implementations, and algorithmic approaches that define the field of quantum computing. Key quantum mechanical phenomena – such as superposition, entanglement, and interference – are examined in relation to their computational applications. The discussion extends to qubit realization technologies including superconducting circuits, trapped ions, photonic systems, and emerging platforms like topological qubits. Core quantum algorithms, including Shor’s and Grover’s, are analyzed to demonstrate their operational mechanics and performance advantages. The chapter also presents current challenges in quantum error correction, coherence preservation, and hardware scalability, particularly in the context of noisy intermediate-scale quantum devices. Further, it highlights applications across domains such as cryptography, optimization, quantum chemistry, and machine learning. The content is supported by illustrative diagrams, comparative tables, and up-to-date research references, providing a comprehensive overview of the state of quantum computing and its future directions.
Abstract While recent progress in quantum hardware open the door for significant speedup in certain key areas, quantum algorithms are still hard to implement right, and the validation of such quantum programs is a challenge. In this paper we propose Qbricks, a formal verification environment for circuit-building quantum programs, featuring both parametric specifications and a high degree of proof automation. We propose a logical framework based on first-order logic, and develop the main tool we rely upon for achieving the automation of proofs of quantum specification: PPS, a parametric extension of the recently developed path sum semantics. To back-up our claims, we implement and verify parametric versions of several famous and non-trivial quantum algorithms, including the quantum parts of Shor’s integer factoring, quantum phase estimation (QPE) and Grover’s search.