Reducing the Depth of Linear Reversible Quantum Circuits

Background. Quantum computing is a technology that has great potential to revolutionize various scientific disciplines with its extraordinary computing capabilities. Multiple fields, such as cryptography, optimization, and complex simulation, have begun to explore quantum computing applications. However, a deep understanding of how these technologies can be used effectively in cross-disciplinary informatics still needs to be improved. Purpose. This research aims to develop a theoretical framework that explains the role of quantum computing in advancing interdisciplinary informatics. The main focus is to identify potential applications and challenges faced in applying this technology in various fields and evaluate its impact on the development of science and technology. Method. This research uses a qualitative approach, a literature review, and theoretical analysis methods. Data were collected from various academic sources, including journals, books, and conferences relevant to quantum computing and interdisciplinary informatics. Analysis was conducted to identify patterns, themes, and relationships between quantum computing and cross-disciplinary applications. Results. The research results show that quantum computing has the potential to solve complex computational problems in various fields, including cryptography, optimization, and physical simulation. Quantum computing can provide more efficient and faster solutions than classical computing. However, the research also identified several significant challenges, such as the need for quantum-specific algorithms and sophisticated technological infrastructure. Conclusion. Quantum computing is essential in advancing interdisciplinary informatics by providing superior computing capabilities. The theoretical framework developed in this research can be used to further study and develop quantum computing applications in various fields. The identified challenges require special attention to ensure effective and efficient implementation of this technology.

Exotic particles called Majorana fermions have potential applications in quantum computing, but their existence has yet to be definitively confirmed. Two groups have now glimpsed these particles. Exotic particles called Majorana fermions have potential applications in quantum computing, but their existence has yet to be definitively confirmed. Two groups have now glimpsed these particles.

In this serie of lectures we present some of the basic issues in semiconductor spintronics at an introductory level. We start with a short overview of low dimensional semiconductor structures and their applications. We then move on to discuss the notions of spin generation, injection, relaxation and detection in spintronics, with an emphasis in the application of diluted magnetic semiconductors for spin generation and injection. Finally, we discuss the manipulation of spin in semiconductor nanostructures like quantum dots, and its applications in quantum computation.

Reversible circuits have widespread applications in quantum computing, low power circuit design, nanotechnology, cryptography etc. The paper addresses the fault diagnosis problem in reversible logic circuit for missing gate fault model. The key idea of this paper is to extract the fault signature for each of the missing gate faults in a reversible circuit. Fault signatures are generated using the successive tree form of Multi-Input Signature Analyzer (MISA). Fault diagnosis is performed by verifying the signature of the circuit under test. The proposed method rapidly identifies the fault locations and the experimental results for benchmark circuits exhibit the efficiency of the technique.

Quantum Computing has inspired scientists as well as hardware and software developers and is fundamentally changing the landscape of information technology. Quantum computing systems are built from the bottom up reaching the limits of what can be classically simulated. The IBM Quantum Development Roadmap describes our vision of creating a quantum computing ecosystem delivering quantum applications through the cloud. This requires developing the entire quantum computing stack starting from the qubit and quantum processor technology, control electronics to software, algorithms and applications for quantum computing, implemented in the cloud and integrated with high performance computing. Though today's noisy near-term quantum computing systems are still limited, the roadmap targets to achieve an inflection point benefiting of continuous improvements across the stack driving quantum performance described by more qubits, higher quantum volume and speed.

Reversible logic has received a great deal of attention from many researchers over recent years for its enormous potential for application in quantum computing and nanotechnology due to its ability to reduce power consumption, which is the main requirement in low power VLSI design. In this study, first, we have presented new reversible blocks. These circuits can be used for the design of a large and complex combinational circuit. Then, we presented optimised designs for the various reversible components: multiplexers, registers, and shift registers. We will put forward the design and evaluation of optimised reversible division hardware to submit an application of reversible logic design. The comparative results show that the proposed designs individually have less hardware complexity, garbage outputs, constant inputs, quantum cost and significantly better scalability than the existing works. We have presented some lower bounds on the cost-metrics for designing the reversible components of the divider circuit.

Over the last few decades, research in reversible logic has increasingly become very popular and it is gaining greater momentum in the present word. Reversible logic has started finding concert applications in quantum computing, optical computing, nano-technology based system, low-power CMOS design, VLSI design. The principal objective of this work is to argue for quantum implementation of various reversible logic gates by using C-NOT, Controlled-V and Controlled-V+ gates. The present work presents Binary coded decimal adder (BCD) in terms of number of gates, garbage outputs, quantum cost, delay and hardware complexity compared to existing design.

Reversible logic has extensive applications in quantum computing, low power VLSI design, quantum dot cellular automata and optical computing. While several researchers have investigated the design of reversible logic elements, there is not much work reported on reversible binary subtractors. In this paper, we propose the design of a new reversible gate called TR gate. Further, we investigate the design of reversible binary subtractors based on the proposed TR gate. The proposed TR gate is better for designing reversible binary subtractor compared to such gates discussed in literature in terms of quantum cost, garbage outputs and complexity of gates.

In this study, we examine an innovative framework towards implementing quantum decision trees utilizing a laser-driven four-level system. We discuss a diamond-shaped atomic configuration, in which we apply Lie-algebraic formalisms to analyze the dynamics of the system. The system is perturbed by a Stokes pulse, represented as β j (t) (for j = 1, 2), which interacts with the atomic states ∣0〉, ∣3〉 and ∣1〉, ∣2〉. In addition, a pump laser, denoted as α j (t), couples the states ∣0〉, ∣1〉 and ∣2〉, ∣3〉. By employing pulse profiles that possess identical temporal behavior but differ in amplitude, one can effectively redistribute the population from the initial ground state to the other energy levels. This technique facilitates the mimicry of a quantum decision tree. We highlight that the proposed methodology is scalable to N-level systems, enhancing its adaptability and potential utility in quantum computing and various decision-making applications. We introduce a novel framework for implementing quantum decision trees using a four-level laser-driven atomic system. Employing a diamond-shaped energy configuration, we analyze system dynamics through Lie-algebraic methods. The system’s interactions are mediated by Stokes pulses, β j (t) (for j = 1, 2), coupling states ∣0〉 ↔ ∣3〉 and ∣1〉 ↔ ∣2〉, alongside pump pulses, α j (t), facilitating transitions between ∣0〉 ↔ ∣1〉 and ∣2〉 ↔ ∣3〉. Using pulse profiles with identical temporal structures but varying amplitudes, we achieve controlled population redistribution among energy levels, effectively simulating a quantum decision tree. This methodology is scalable to systems of N levels, offering potential applications in quantum computing and decision-making processes.

Fault-tolerant quantum reversible full adder/subtractor: Design and implementation

Laser cooling and trapping1,2, and magneto-optical trapping methods in particular2, have enabled groundbreaking advances in science, including Bose-Einstein condensation3-5, quantum computation with neutral atoms6,7 and high-precision optical clocks8. Recently, magneto-optical traps (MOTs) of diatomic molecules have been demonstrated9-12, providing access to research in quantum simulation13 and searches for physics beyond the standard model14. Compared with diatomic molecules, polyatomic molecules have distinct rotational and vibrational degrees of freedom that promise a variety of transformational possibilities. For example, ultracold polyatomic molecules would be uniquely suited to applications in quantum computation and simulation15-17, ultracold collisions18, quantum chemistry19 and beyond-the-standard-model searches20,21. However, the complexity of these molecules has so far precluded the realization of MOTs for polyatomic species. Here we demonstrate magneto-optical trapping of a polyatomic molecule, calcium monohydroxide (CaOH). After trapping, the molecules are laser cooled in a blue-detuned optical molasses to a temperature of 110 μK, which is below the Doppler cooling limit. The temperatures and densities achieved here make CaOH a viable candidate for a wide variety of quantum science applications, including quantum simulation and computation using optical tweezer arrays15,17,22,23. This work also suggests that laser cooling and magneto-optical trapping of many other polyatomic species24-27 will be both feasible and practical.

The canonical decomposition for two-qubit operators has proven very useful for applications in quantum computing. This decomposition generates equivalence classes up to local quantum gates. We provide a variety of complete, explicit decompositions of given two-qubit operators in terms of single, double, and triple controlled-NOT (CNOT) gates. By analytically addressing the needed pre- and post-tensor product factors, we demonstrate that exact results are possible, even when a parameter is included. The examples given are of interest to superconducting qubit, spin-based, dipolar molecule, and other quantum information processing systems.

In recent years, zinc oxide (ZnO) quantum dots (QDs) have gained much attention due to their quantum and piezoelectric properties, which make them viable candidates for optoelectronic applications and nano-sensing devices. The piezoelectric properties of ZnO QDs are intimately linked to their optoelectronic behavior in response to mechanical stress and applied external forces. First-principle calculations of the electronic properties of the QDs have been performed using density functional theory (DFT) to compute their electronic structure, density of states (DOS), charge density, phonon behavior, and the photoluminescence properties of the material. Moreover, the size-dependent quantum confinement effect can be seen through the discrete energy levels in the band structure, which results in defined peaks in the DOS and blue shift in the photoluminescence. The bandgap energy of ZnO QDs determines the conductivity of the material and is dependent on the size of the QDs. This property in turn allows for tunability of the electronic properties of the QDs as a function of their size and is thereby appropriate for optoelectronic, photonic, and sensing applications. The nano-scale sizes and quantum properties of ZnO QD qubits also make them suitable for applications in quantum computing and data storage.

Quantum computing (QC) is not a futuristic notion in agriculture, though its full potential has yet to be realized. QC is an emerging field at the intersection of physics and computer science that holds immense potential to revolutionize various sectors, including agriculture production and artificial intelligence (AI) modeling. While QC is still in the early stages of development and practical applications within agriculture are not yet widespread, researchers are actively exploring its potential benefits in various agricultural domains, including crop optimization, livestock breeding, and environmental monitoring. QC harnesses the principles of quantum mechanics to perform computations using quantum bits or qubits, which can exist in multiple states simultaneously. Unlike classical computers, which rely on binary bits representing 0 or 1, quantum computers exploit phenomena such as superposition and entanglement to process information in parallel, potentially offering exponential speedup for certain types of problems. In agriculture production, particularly in animal science, QC offers promising avenues for optimizing processes and enhancing productivity. Quantum algorithms can analyze vast amounts of genomic data to improve breeding programs, leading to the development of more resilient and productive livestock breeds. Furthermore, QC can facilitate precision farming techniques by modeling complex environmental factors and animal behavior to optimize feeding strategies, disease management, and overall farm management practices. Moreover, QC can significantly benefit AI modeling by accelerating computations and enabling more efficient training of AI models. Quantum algorithms can enhance the performance of AI algorithms in various tasks, including pattern recognition, natural language processing, and predictive analytics. By leveraging quantum-enhanced optimization algorithms, AI models can achieve better convergence and accuracy, leading to more effective decision-making and problem-solving capabilities. While hybrid intelligent models also represent a novel frontier in agriculture, QC has the potential to expedite the merging of mechanistic and AI modeling paradigms, facilitating a more holistic understanding of complex systems in agriculture and beyond. By integrating mechanistic models, which describe the underlying physical processes, with AI models, which learn patterns from data, quantum computing can enable comprehensive simulations and predictions of agricultural systems. This fusion of modeling paradigms can lead to more accurate and robust predictions of crop yields, livestock performance, and environmental impacts, facilitating informed decision-making for farmers and policymakers. The application of QC in agriculture, however, requires interdisciplinary collaborations between physicists, computer scientists, agronomists/animal scientists, and AI researchers. These collaborations can drive the development of quantum algorithms tailored to agricultural applications, the integration of quantum-enhanced AI techniques into existing modeling frameworks, and the deployment of QC resources in real-world agricultural systems. Ultimately, harnessing the power of QC holds the potential to revolutionize agriculture production practices, including regenerative agriculture, and advance AI modeling capabilities, paving the way for a more sustainable and efficient agricultural industry.

Simulation of the dynamics of quantum materials is emerging as a promising scientific application for noisy intermediate-scale quantum (NISQ) computers. Due to their high gate-error rates and short decoherence times, however, NISQ computers can only produce high-fidelity results for those quantum circuits smaller than some given circuit size. Dynamic simulations, therefore, pose a challenge as current algorithms produce circuits that grow in size with each subsequent time-step of the simulation. This underscores the crucial role of quantum circuit compilers to produce executable quantum circuits of minimal size, thereby maximizing the range of physical phenomena that can be studied within the NISQ fidelity budget. Here, we present two domain-specific (DS) quantum circuit compilers for the Rigetti and IBM quantum computers, specifically designed to compile circuits simulating dynamics under a special class of time-dependent Hamiltonians. The compilers outperform state-of-the-art general-purpose compilers in terms of circuit size reduction by around 25%–30% as well as wall-clock compilation time by around 40% (dependent on system size and simulation time-step). Drawing on heuristic techniques commonly used in artificial intelligence, both compilers scale well with simulation time-step and system size. Code for both compilers is open-source and packaged into a full-stack quantum simulation software with tutorials included for ease of use for future researchers wishing to perform dynamic simulations of quantum materials on quantum computers. As our DS compilers provide significant improvements in both compilation time and simulation fidelity, they provide a building block for accelerating progress toward physical quantum supremacy.