In recent years, interest in quantum computing has increased due to technological advances in quantum hardware and algorithms. Despite the promises of quantum advantage, the applicability of quantum devices has been limited to few qubits on hardware that experiences decoherence due to noise. One proposed method to get around this challenge is distributed quantum computing (DQC). Like classical distributed computing, DQC aims at increasing compute power by spreading the compute processes across many devices, with the goal to minimize the noise and circuit depth required by quantum devices. In this paper, we cover the fundamental concepts of DQC and provide insight into where the field of DQC stands with respect to the field of chemistry -- a field which can potentially be used to demonstrate quantum advantage on noisy-intermediate scale quantum devices.
In recent years, interest in quantum computing has increased due to technological advances in quantum hardware and algorithms. Despite the promises of quantum advantage, the applicability of quantum devices has been limited to few qubits on hardware that experiences decoherence due to noise. One proposed method to get around this challenge is distributed quantum computing (DQC). Like classical distributed computing, DQC aims at increasing compute power by spreading the compute processes across many devices, with the goal to minimize the noise and circuit depth required by quantum devices. In this paper, we cover the fundamental concepts of DQC and provide insight into where the field of DQC stands with respect to the field of chemistry -- a field which can potentially be used to demonstrate quantum advantage on noisy-intermediate scale quantum devices.
Quantum chemistry plays a crucial role in advancing fundamental understanding of chemical processes and driving innovation in energy, medicine, and materials science. Recent progress in quantum computing has opened new possibilities for molecular simulations that are beyond the practical limits of classical approaches. Academic studies have demonstrated hybrid Density Functional Theory (DFT) and Variational Quantum Eigensolver (VQE) benchmarks on small transition-metal systems, such as iron porphyrin and heme analogues, using current noisy intermediate-scale quantum (NISQ) hardware. Although full-scale simulations of complex biological systems such as the complete Cytochrome P450 (CYP450) active site remain a long-term goal due to limitations in qubit numbers, coherence times, and error correction, the underlying methodologies are now well established. At present, quantum chemistry applications in drug discovery remain largely experimental, and widespread practical medical impact is expected to require further technological advances over the next 5–10 years. Nevertheless, quantum software and algorithm development for chemical applications is progressing rapidly. This perspective summarizes recent advances in quantum computing algorithms, hardware, and software relevant to chemistry, and critically discusses the remaining challenges and opportunities for applying quantum computing to chemical problems, with particular emphasis on drug discovery and development.
Abstract Quantum computing has been attracting public attention recently. This interest is driven by the advancements in hardware, software, and algorithms required for its successful usage and the promise that it entails the potential acceleration of computational tasks compared to classical computing. This perspective article presents a short review on quantum computing, how this computational approach solves problems, and three fields that quantum computing can potentially impact the most while relevant to chemical engineering: computational chemistry, optimization, and machine learning. Here, we present a series of chemical engineering applications, the developments, potential improvements with respect to classical computing, and challenges that quantum computing faces for each of these fields. This article intends to provide a clear picture of the challenges and potential advantages that quantum technology may yield for chemical engineering, together with an invitation for our colleagues to join us in the adoption and development of quantum computing.
Recent advances in quantum technologies are rapidly pushing the boundaries of computational capabilities, with the promise of significant speedups across a variety of applications. However, building large-scale monolithic quantum processors remains an immense engineering challenge, motivating the pursuit of alternative paradigms to realize practical quantum advantage. One such paradigm is Distributed Quantum Computing (DQC), which interconnects multiple quantum nodes via quantum networks to collectively perform complex computations. While Circuit-Based Quantum Computing (CBQC) has traditionally been the dominant model, Measurement-Based Quantum Computing (MBQC), with its one-way computation framework and pre-shared entangled cluster states, introduces distinctive benefits for distributed architectures. Importantly, within DQC, MBQC offers enhanced security over CBQC by naturally supporting protocols such as Blind Quantum Computation (BQC) and secure delegated processing. This study offers an application-oriented perspective on the role of MBQC in DQC, highlighting concrete industrial use cases alongside key implementation challenges. By bridging theoretical potential with practical deployment considerations, it aims to accelerate progress toward secure and scalable distributed quantum systems.
In recent years, the emergence of the Noisy Intermediate-Scale Quantum (NISQ) era has led to the development of a wide variety of quantum devices, marking a dynamic phase in the field of quantum computing. Despite these advancements, practical applications of quantum computers still face significant challenges, one of the most prominent being the limited connectivity between physical quantum bits (qubits). This limitation requires quantum circuits to be recompiled through layout synthesis before they can be executed on quantum devices. Layout synthesis determines the specific placement of quantum gates within the circuit on the physical hardware. When gates do not align with the connectivity constraints of the device, additional quantum gates must be inserted to remap the qubits, which can negatively affect the circuit’s efficiency and performance. To address this issue, the primary goal of this paper is to enhance the mapping process within layout synthesis, aiming to optimize both the layout and the circuit depth. Experimental results demonstrate that the proposed method effectively reduces the quantum circuit depth, outperforming other existing approaches in terms of efficiency.
The exploration of distributed quantum computing (DQC) represents a significant frontier in quantum information science, aiming to harness the unique properties of quantum mechanics to solve complex computational problems effectively. This paper discusses the diverse architectures and models that facilitate the distribution of quantum computing tasks across multiple quantum nodes, thereby addressing the limitations inherent in single quantum devices. DQC systems exploit the principles of superposition and entanglement to enhance computational capabilities beyond what classical computing systems can achieve. We examine various DQC architectures, including both quantum-classical hybrid systems and fully quantum distributed systems, highlighting their respective benefits and challenges. Key issues such as coherence, communication overhead, and error correction are discussed in detail, and we analyze specific use cases where DQC demonstrates superior performance relative to classical computing approaches, including optimization problems in logistics and finance, as well as quantum simulations for material science. Furthermore, we identify future research directions aimed at overcoming existing barriers to the practical implementation of DQC systems. By assessing the current landscape and future possibilities of DQC, this paper underscores the transformative potential of distributed quantum computing in various fields, paving the way for realizing more complex quantum algorithms and applications.
The development and use of quantum computers has the potential for revolutionary impact on the waycomputing supports chemical applications. Quantum computation for chemical applications has received enormous attention in recent years with rapid progress in both theoretical and experimental fronts. The development of quantum algorithms for quantum chemistry is stimulated by the greater availability of more capable quantum computing devices. But major challenges remain with one key example the development of quantum algorithms for solving chemical problems thought to be intractable for classical computers. Other challenges include understanding the role of quantum entanglement, coherence, and superposition in complex chemical processes such as photosynthesis.Theoretical chemists have encountered and analyzed quantum effects in physical chemistry for decades.Combining the results of the chemical physics community with insights from the quantum informationcommunity can lead to a fresh understanding of important chemical processes and the emergence of thenovel many-body formulations amenable for quantum computing. This Research Topic will invite leading scientists from all over the world who work in the fields of quantum information and computation to report their recent research results.