Quantum computers are regarded as the future of computing, as they are believed to be capable of solving extremely complex problems that are intractable on conventional digital computers. However, near-term quantum computers are prone to a plethora of noise sources that are difficult to mitigate, possibly limiting their scalability and precluding us from running any […]
Quantum machine learning (QML) has received increasing attention due to its potential to outperform classical machine learning methods in problems, such as classification and identification tasks. A subclass of QML methods is quantum generative adversarial networks (QGANs), which have been studied as a quantum counterpart of classical GANs widely used in image manipulation and generation […]
Collateral optimization refers to the systematic allocation of financial assets to satisfy obligations or secure transactions while simultaneously minimizing costs and optimizing the usage of available resources. This involves assessing the number of characteristics, such as the cost of funding and quality of the underlying assets to ascertain the optimal collateral quantity to be posted […]
Presents corrections to the article “The Present and Future of Discrete Logarithm Problems on Noisy Quantum Computers”. For more about this article see link below. https://ieeexplore.ieee.org/document/10185464 For the open access PDF link of this article please click.
We apply quantum error mitigation (QEM) techniques to a variety of benchmark problems and quantum computers to evaluate the performance of QEM in practice. To do so, we define an empirically motivated, resource-normalized metric of the improvement of error mitigation, which we call the improvement factor, and calculate this metric for each experiment we perform. […]
The vehicle routing problem (VRP) is an NP-hard optimization problem that has been an interest of research for decades in science and industry. The objective is to plan routes of vehicles to deliver goods to a fixed number of customers with optimal efficiency. Classical tools and methods provide good approximations to reach the optimal global […]
For most practical applications, quantum algorithms require large resources in terms of qubit number, much larger than those available with current noisy intermediate-scale quantum processors. With the network and communication functionalities provided by the quantum Internet, distributed quantum computing (DQC) is considered as a scalable approach for increasing the number of available qubits for computational […]
Quantum computing can enable novel algorithms infeasible for classical computers. For example, new material synthesis and drug optimization could benefit if quantum computers offered more quantum bits (qubits). One obstacle for scaling up quantum computers is the connection between their cryogenic qubits at temperatures between a few millikelvin and a few kelvin (depending on qubit […]
Toeplitz matrix reconstruction algorithms (TMRAs) are one of the central subroutines in array processing for wireless communication applications. The classical TMRAs have shown excellent accuracy in the spectral estimation for both uncorrelated and coherence sources in the recent era. However, TMRAs incorporate the classical eigenvalue decomposition technique for estimating the eigenvalues of the Toeplitz-structured covariance […]
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 […]