It’s obvious to ask what to do if we are given a quantum computer. Here, we
present a number of quantum tasks which have been performed on a quantum
computer. Some practical applications like quantum cheque, quantum locker and
quantum OTP have been experimentally realized using IBM’s 5 qubit quantum
computer. The above applications can together act as a quantum banking system.
Simulating quantum physics is another area of great interest. Quantum tunneling
has been simulated on the quantum computer to show it’s quantum nature and
potential applications. Quantum devices such as quantum repeater, and quantum
router play key roles in a quantum network for long distance communication.
These devices have been designed using IBM’s quantum computer. Recent
trends like quantum machine learning and quantum artificial intelligence have
significant applications e.g., classification of big data, recognizing hand written
pattern, quantum image processing etc. All the above tasks can be efficiently
simulated in a quantum computer, provided we have a basic background of
quantum circuits and quantum gates.
It’s obvious to ask what to do if we are given a quantum computer. Here, we
present a number of quantum tasks which have been performed on a quantum
computer. Some practical applications like quantum cheque, quantum locker and
quantum OTP have been experimentally realized using IBM’s 5 qubit quantum
computer. The above applications can together act as a quantum banking system.
Simulating quantum physics is another area of great interest. Quantum tunneling
has been simulated on the quantum computer to show it’s quantum nature and
potential applications. Quantum devices such as quantum repeater, and quantum
router play key roles in a quantum network for long distance communication.
These devices have been designed using IBM’s quantum computer. Recent
trends like quantum machine learning and quantum artificial intelligence have
significant applications e.g., classification of big data, recognizing hand written
pattern, quantum image processing etc. All the above tasks can be efficiently
simulated in a quantum computer, provided we have a basic background of
quantum circuits and quantum gates.
Aim. IThe presented study aims to identify current trends and key directions for the introduction of quantum technologies into the management practices of Russian credit institutions to improve their performance in the medium term. Tasks. The authors comparatively analyze current trends in the application of quantum technologies in the banking business; forecast key trends in their development in the medium term in the context of the Russian banking sector. Methods. The authors use the methods of comparative and system analysis, scientific abstraction, and hypothesis. Results. Practical examples of the introduction of modern quantum technologies in the foreign and Russian banking business are provided. As a result, priority directions and areas of their application are identified with an emphasis on the domestic banking sector: credit scoring, assessment of the value and risk of investment, diversification of investment portfolios, financial forecasting, crisis prediction, fraud detection, protection of banking and customer databases from the quantum threat, and increasing the level of banking cybersecurity in general. Conclusions. Despite the novelty of quantum technologies in the banking business, the role they will play in banking in the near future is difficult to overestimate. Quantum computers can significantly improve the performance of banking IT systems by solving computational tasks that even super-powerful conventional computers are not able to tackle on their own. They significantly increase the efficiency of modeling complex systems and processes; accelerate the soultion of problems associated with resource optimization, logistics, finance, and trading; simplify work with big data using artificial intelligence (AI) and machine learning models in neural networks; help to combat cyber attacks and fraud in financial markets.
Quantum computing is an emerging method of computation that promises to accomplish computational tasks and algorithms that are too difficult to perform on existing computing paradigms. Some of the promising applications of quantum computing includes number theory, encryption, search, and scientific computation. Circuits must be designed to apply quantum machines to these applications. This work introduces quantum circuit design. We provide a brief introduction to quantum computing, and then, we illustrate gates used in quantum circuit design. We conclude by introducing quantum circuits for arithmetic operations.
Quantum computing is based on the exploitation of quantum effects to perform some important computations more efficiently than ever possible on classical computers. Well known quantum algorithms are Shors polynomial time factoring algorithm and Grovers data base search scheme. Their potential is due to the parallelism of a quantum computation increasing exponentially with the size of the computing device. IBM has succeeded in building a first working quantum computing device with up to 7 qubits based on NMR technology, however, the construction of large multi-qubit systems appears to become extremely difficult due to the loss of coherence of the quantum states. Like the design process of modern microprocessors which is carried out by means of sophisticated software, modelling and simulation of the entire QC device is even more relevant. Simulation is required in order to understand the functioning of quantum algorithms, to construct novel quantum algorithms, to write quantum programs, to teach quantum computing, to construct real quantum computers, to understand and fight de-coherence and to understand and control the influence of the environment. Since quantum parallelism increases exponentially with the size of the quantum computer, parallel supercomputers are required for realistic simulations. I intend to give an introduction into the simulation of quantum computers using conventional computers. The simulation is based on a generic physical model of quantum computer hardware. Issues like parallel gate level simulators, modelling of QCs, simulators for physical quantum computers are addressed.
Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present the first comprehensive QML study by benchmarking the MedMNIST-a diverse collection of medical imaging datasets on a 127-qubit real IBM quantum hardware, to evaluate the feasibility and performance of quantum models (without any classical neural networks) in practical applications. This study explores recent advancements in quantum computing such as device-aware quantum circuits, error suppression, and mitigation for medical image classification. Our methodology is comprised of three stages: preprocessing, generation of noise-resilient and hardware-efficient quantum circuits, optimizing/training of quantum circuits on classical hardware, and inference on real IBM quantum hardware. Firstly, we process all input images in the preprocessing stage to reduce the spatial dimension due to quantum hardware limitations. We generate hardware-efficient quantum circuits using backend properties expressible to learn complex patterns for medical image classification. After classical optimization of QML models, we perform inference on real quantum hardware. We also incorporate advanced error suppression and mitigation techniques in our QML workflow, including dynamical decoupling (DD), gate twirling, and matrix-free measurement mitigation (M3) to mitigate the effects of noise and improve classification performance. The experimental results showcase the potential of quantum computing for medical imaging and establish a benchmark for future advancements in QML applied to healthcare.
Quantum computation, the ultimate goal of future computing, is an interesting field for researchers.The concept of quantum computation is based on basics of quantum mechanics.A quantum computer is a device for computation that makes direct use of quantum mechanical phenomena such as superposition and entanglement, to perform operations on data.The basic principle behind quantum computation is that quantum properties can be used to represent data and perform operations on these data.A quantum computer operates by manipulating the qubits with a fixed sequence of quantum logic gates.The sequence of gates to be applied is called a quantum algorithm.The field of quantum computation algorithm is fast moving and the scope is vast.Major quantum algorithms are summarized in this paper along with their applications.
Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present the first comprehensive QML study by benchmarking the MedMNIST-a diverse collection of medical imaging datasets on a 127-qubit real IBM quantum hardware, to evaluate the feasibility and performance of quantum models (without any classical neural networks) in practical applications. This study explores recent advancements in quantum computing such as device-aware quantum circuits, error suppression, and mitigation for medical image classification. The proposed methodology is comprised of three stages: preprocessing, generation of noise-resilient and hardware-efficient quantum circuits, optimizing/training of quantum circuits on classical hardware, and inference on real IBM quantum hardware. Firstly, we process all input images in the preprocessing stage to reduce the spatial dimension due to quantum hardware limitations. We generate hardware-efficient quantum circuits using backend properties expressible to learn complex patterns for medical image classification. After classical optimization of QML models, we perform inference on real quantum hardware. We also incorporate advanced error suppression and mitigation techniques in our QML workflow, including dynamical decoupling (DD), gate twirling (Twir), and matrix-free measurement mitigation (M3) to mitigate the effects of noise and improve classification performance. The experimental results showcase the potential of quantum computing for medical imaging and establish a benchmark for future advancements in QML applied to healthcare.