Quantum Computing Archives - Page 10 of 13 - IEEE Transactions on Quantum Engineering

Enabling Efficient Real-Time Calibration on Cloud Quantum Machines

June 14, 2023Yiding LiuQuantum ComputingCalibration, Logic gates, Noise measurement, Quantum computing, Qubit, Reliability, Task analysis

Noisy intermediate-scale quantum computers are widely used for quantum computing (QC) from quantum cloud providers. Among them, superconducting quantum computers, with their high scalability and mature processing technology based on traditional silicon-based chips, have become the preferred solution for most commercial companies and research institutions to develop QC. However, superconducting quantum computers suffer from fluctuation […]

Prediction of Solar Irradiance One Hour Ahead Based on Quantum Long Short-Term Memory Network

June 14, 2023Yunjun YuQuantum ComputingAtmospheric modeling, Computational modeling, Data models, Feature extraction, Forecasting, Mathematical models, Predictive models

The short-term forecasting of photovoltaic (PV) power generation ensures the scheduling and dispatching of electrical power, helps design a PV-integrated energy management system, and enhances the security of grid operation. However, due to the randomness of solar energy, the output of the PV system will fluctuate, which will affect the safe operation of the grid. […]

Quantum Topology Optimization via Quantum Annealing

June 14, 2023Zisheng YeQuantum ComputingAnnealing, Computational efficiency, Layout, Linear programming, Logic gates, Optimization, Quantum annealing

We present a quantum annealing-based solution method for topology optimization (TO). In particular, we consider TO in a more general setting, i.e., applied to structures of continuum domains where designs are represented as distributed functions, referred to as continuum TO problems. According to the problem’s properties and structure, we formulate appropriate subproblems that can be […]

Hardness of Braided Quantum Circuit Optimization in the Surface Code

March 30, 2023Kunihiro WasaQuantum ComputingFault tolerance, Fault tolerant systems, Lattices, Logic gates, Optimization, Quantum circuit, Qubit

Large-scale quantum information processing requires the use of quantum error-correcting codes to mitigate the effects of noise in quantum devices. Topological error-correcting codes, such as surface codes, are promising candidates, as they can be implemented using only local interactions in a 2-D array of physical qubits. Procedures, such as defect braiding and lattice surgery, can […]

Teaching Quantum Computing to High-School-Aged Youth: A Hands-On Approach

November 2, 2022Prashanti Priya AngaraOpen Access, Quantum ComputingConferences, Industries, Programming profession, Quantum computing, Quantum mechanics, Qubit, Software

Quantum computing is aninterdisciplinary field that lies at the intersection of mathematics, quantum physics, and computer science, and finds applications in areas including optimization, machine learning, and simulation of chemical, physical, and biological systems. It has the potential to help solve problems that so far have no satisfying method solving them, and to provide significant […]

Pricing Multi-Asset Derivatives by Finite-Difference Method on a Quantum Computer

November 2, 2022Koichi MiyamotoOpen Access, Quantum ComputingComplexity theory, Europe, Frequency division multiplexing, Handheld computers, Pricing, Quantum algorithm, Time complexity

Following the recent great advance of quantum computing technology, there are growing interests in its applications to industries, including finance. In this article, we focus on derivative pricing based on solving the Black–Scholes partial differential equation by the finite-difference method (FDM), which is a suitable approach for some types of derivatives but suffers from the […]

Hash Function Based on Controlled Alternate Quantum Walks With Memory (September 2021)

November 2, 2022Qing ZhouOpen Access, Quantum ComputingHash functions, Process control, Quantum computing, Quantum mechanics, Resistance, Sensitivity, Transforms

We propose a Quantum inspired Hash Function using controlled alternate quantum walks with Memory on cycles (QHFM), where the j th message bit decides whether to run quantum walk with one-step memory or to run quantum walk with two-step memory at the j th time step, and the hash value is calculated from the resulting probability distribution of the […]

Quantum Radon Transforms and Their Applications

November 2, 2022Guangsheng MaOpen Access, Quantum ComputingHandheld computers, Image processing, Image reconstruction, Noise reduction, Quantum algorithm, Quantum computing, Transforms

This article extends the Radon transform, a classical image-processing tool for fast tomography and denoising, to the quantum computing platform. A new kind of periodic discrete Radon transform (PDRT), called the quantum periodic discrete Radon transform (QPRT), is proposed. The quantum implementation of QPRT based on the amplitude encoding method is exponentially faster than the […]

Efficient Construction of a Control Modular Adder on a Carry-Lookahead Adder Using Relative-Phase Toffoli Gates

November 2, 2022Kento OonishiOpen Access, Quantum ComputingAdders, Computers, Costs, Logic gates, Optimization, Quantum computing, Qubit

Control modular addition is a core arithmetic function, and we must consider the computational cost for actual quantum computers to realize efficient implementation. To achieve a low computational cost in a control modular adder, we focus on minimizingKQ (where K is the number of logical qubits required by the algorithm, and Q is the elementary […]

Efficient Quantum State Preparation for the Cauchy Distribution Based on Piecewise Arithmetic

November 2, 2022Ethan T. WangOpen Access, Quantum ComputingApproximation algorithms, Distribution functions, Logic gates, Mathematical models, Monte Carlo methods, Qubit, Registers

The benefits of the quantum Monte Carlo algorithm heavily rely on the efficiency of the superposition state preparation. So far, most reported Monte Carlo algorithms use the Grover–Rudolph state preparation method, which is suitable for efficiently integrable distribution functions. Consequently, most reported works are based on log-concave distributions, such as normal distributions. However, non-log-concave distributions […]