Search Results (8)

Qudits allow the encoding and manipulation of additional quantum information compared to that stored to a two-level qubit system. Although manipulations of qudit states are generally more complex and can introduce extra sources of noise, qudits can still be used in a number of applications when this error can be kept sufficiently low. One such application is the case of the Bucket Brigade Algorithm for realizing a Quantum RAM (QRAM), which inherently uses qutrits for encoding the state of address switches. In this paper, we study a methodology for qutrit manipulation that leverages efficient encoding techniques and pulse calibration methods for the case of transmon systems. The methodology employs an encoding scheme that allows the execution of controlled operations, using the subspace spanned by the two lowest levels of the transmon; we show how this scheme can be used for generating one- and two-qutrit gates by leveraging the Qiskit and Boulder Opal frameworks to compute the parameters of pulses that implement the quantum gates that are used by the BBA. For this type of gate, simulations show that the pulses perform the required operations with a low infidelity when errors introduced by the qutrit Hamiltonian dynamics are considered. Full article

Battlefields contain complex networks of electromagnetic (EM) systems, owned by adversary/allied military forces and civilians, communicating intentionally or unintentionally. Attacker’s strategies may include Intentional EM Interference (IEMI) to adversary target systems, although transmitted signals may additionally degrade/disrupt allied/civilian systems (called victims). To aid decision-making processes relating to IEMI attacks, Risk Assessment (RA) is performed to determine whether interference risks to allied/civilian systems are acceptable. Currently, there is no formalized Quantitative RA Method (QRAM) capable of calculating victim risk distributions, so a novel approach is proposed to address this knowledge gap, utilizing an Electromagnetic Warfare (EW) IEMI RA method modeling scenarios consisting of interacting EM systems within complex, dynamic, diverse, and uncertain environments, using Systems-of-Systems (SoS) theory. This paper aims to address this knowledge gap via critical analysis utilizing a case study which demonstrates the use of an Acknowledged SoS-based model as input to a QRAM capable of calculating victim risk distributions within EW IEMI RA-associated scenarios. Transmitter operators possess only uncertain/fuzzy knowledge of victim systems, so it is proposed that a Moot Acknowledged System-of-Fuzzy-Systems applies to EW IEMI RA scenarios. In summary, a novel SoS description feeding a novel QRAM (supported by a systematic literature review of RA mathematical modeling techniques)is proposed to address the knowledge gap. Full article

This work introduces a quantum K-Nearest Neighbor (K-NN) classifier algorithm. The algorithm utilizes angle encoding through a Quantum Random Access Memory (QRAM) using n number of qubit addresses with $\mathrm{O}(log(\mathrm{n}\left)\right)$ space complexity. It incorporates Grover’s algorithm and the quantum SWAP-Test to identify similar states and determine the nearest neighbors with high probability, achieving $\mathrm{O}\left(\sqrt{m}\right)$ search complexity, where m is the qubit address. We implement a simulation of the algorithm using IBM’s Qiskit with GPU support, applying it to the Iris and MNIST datasets with two different angle encodings. The experiments employ multiple QRAM cell sizes (8, 16, 32, 64, 128) and perform ten trials per size. According to the performance, accuracy values in the Iris dataset range from 89.3 ± 5.78% to 94.0 ± 1.56%. The MNIST dataset’s mean binary accuracy values range from 79.45 ± 18.84% to 94.00 ± 2.11% for classes 0 and 1. Additionally, a comparison of the results of this proposed approach with different state-of-the-art versions of QK-NN and the classical K-NN using Scikit-learn. This method achieves a 96.4 ± 2.22% accuracy in the Iris dataset. Finally, this proposal contributes an experimental result to the state of the art for the MNIST dataset, achieving an accuracy of 96.55 ± 2.00%. This work presents a new implementation proposal for QK-NN and conducts multiple experiments that yield more robust results than previous implementations. Although our average performance approaches still need to surpass the classic results, an experimental increase in the size of QRAM or the amount of data to encode is not achieved due to limitations. However, our results show promising improvement when considering working with more feature numbers and accommodating more data in the QRAM. Full article

A RAM cell, one of the components that greatly affects the performance of quantum computing, outputs mostly stored values on quantum-dot cellular automata (QCA) as they are. Currently, a problem is that the stored value may be initialized according to the selection input. To solve this problem, circuits that separate the stored value from the output value have recently been designed, but most of them have long latency, large areas, and many plane structure intersections, resulting in unstable signals. Therefore, in this paper, we propose a new secure QRAM (QCA-based RAM) cell logic by analyzing and modifying the existing cell logic in nanotechnology. We initially propose 2-to-1 multiplexers based on cell interaction, and a QRAM cell is proposed based on our multiplexer and an optimized QRAM cell logic diagram. Compared with existing designs, the proposed circuits produce superior results in terms of circuit performance and energy dissipation. Additionally, the operation of our multiplexers is verified mathematically using physical proof. The secure QRAM cell proposed in this paper does not have the initialization problem based on the selection input that is present in some existing circuits, thus it is very easy to design an extension to N × N RAM, and it has high signal stability, reliability, connectivity, and scalability because there is no intersection. Full article

Quantum Random Access Memory (QRAM) has the potential to revolutionize the area of quantum computing. QRAM uses quantum computing principles to store and modify quantum or classical data efficiently, greatly accelerating a wide range of computer processes. Despite its importance, there is a lack of comprehensive surveys that cover the entire spectrum of QRAM architectures. We fill this gap by providing a comprehensive review of QRAM, emphasizing its significance and viability in existing noisy quantum computers. By drawing comparisons with conventional RAM for ease of understanding, this survey clarifies the fundamental ideas and actions of QRAM. QRAM provides an exponential time advantage compared to its classical counterpart by reading and writing all data at once, which is achieved owing to storage of data in a superposition of states. Overall, we compare six different QRAM technologies in terms of their structure and workings, circuit width and depth, unique qualities, practical implementation, and drawbacks. In general, with the exception of trainable machine learning-based QRAMs, we observe that QRAM has exponential depth/width requirements in terms of the number of qubits/qudits and that most QRAM implementations are practical for superconducting and trapped-ion qubit systems. Full article

In the last 20 years, several approaches to quantum programming have been introduced. In this survey, we focus on the QRAM (Quantum Random Access Machine) architectural model. We explore the twofold perspective (theoretical and concrete) of the approach and we list the main problems one has to face in quantum language design. Moreover, we propose an overview of some interesting languages and open-source platforms for quantum programming currently available. We also provide the higher-order encoding in the functional languages $\mathsf{qPCF}$ and $\mathsf{IQu}$ of the well known Deutsch-Jozsa and Simon’s algorithms. Full article

(1) Background: Salmonella infections are a major cause of illnesses in the United States. Each year around 450 people die from the disease and more than 23,000 people are hospitalized. Salmonella outbreaks are commonly associated with eggs, meat and poultry. In this study, a quantitative risk assessment model (QRAM) was developed to determine Salmonella infections in broiler chicken. (2) Methods: Data of positive Salmonella infections were obtained from the United States Department of Agriculture (USDA) and the Centers for Disease Control and Prevention (CDC) Foodborne Disease Outbreak Surveillance System, in addition to published literature. The Decision Tools @RISK add-in software was used for various analyses and to develop the QRAM. The farm-to-fork pathway was modeled as a series of unit operations and associated pathogen events that included initial contamination at the broiler house (node 1), contamination at the slaughter house (node 2), contamination at retail (node 3), cross-contamination during serving and cooking (node 4), and finally the dose–response model after consumption. (3) Results: QRAM of Salmonella infections from broiler meat showed highest contribution of infection from the retail node (33.5%). (4) Conclusions: This QRAM that predicts the risk of Salmonella infections could be used as a guiding tool to manage the Salmonella control programs Full article

The prospect of Line-of-Business Services (LoBSs) for infrastructure of Emerging Sensor Networks (ESNs) is exciting. Access control remains a top challenge in this scenario as the service provider’s server contains a lot of valuable resources. LoBSs’ users are very diverse as they may come from a wide range of locations with vastly different characteristics. Cost of joining could be low and in many cases, intruders are eligible users conducting malicious actions. As a result, user access should be adjusted dynamically. Assessing LoBSs’ risk dynamically based on both frequency and threat degree of malicious operations is therefore necessary. In this paper, we proposed a Quantitative Risk Assessment Model (QRAM) involving frequency and threat degree based on value at risk. To quantify the threat degree as an elementary intrusion effort, we amend the influence coefficient of risk indexes in the network security situation assessment model. To quantify threat frequency as intrusion trace effort, we make use of multiple behavior information fusion. Under the influence of intrusion trace, we adapt the historical simulation method of value at risk to dynamically access LoBSs’ risk. Simulation based on existing data is used to select appropriate parameters for QRAM. Our simulation results show that the duration influence on elementary intrusion effort is reasonable when the normalized parameter is 1000. Likewise, the time window of intrusion trace and the weight between objective risk and subjective risk can be set to 10 s and 0.5, respectively. While our focus is to develop QRAM for assessing the risk of LoBSs for infrastructure of ESNs dynamically involving frequency and threat degree, we believe it is also appropriate for other scenarios in cloud computing. Full article