Search Results (37)

Quantum mechanics (QM) revolutionizes drug discovery by providing precise molecular insights unattainable with classical methods. This review explores QM’s role in computational drug design, detailing key methods like density functional theory (DFT), Hartree–Fock (HF), quantum mechanics/molecular mechanics (QM/MM), and fragment molecular orbital (FMO). These methods model electronic structures, binding affinities, and reaction mechanisms, enhancing structure-based and fragment-based drug design. This article highlights the applicability of QM to various drug classes, including small-molecule kinase inhibitors, metalloenzyme inhibitors, covalent inhibitors, and fragment-based leads. Quantum computing’s potential to accelerate quantum mechanical (QM) calculations is discussed alongside novel applications in biological drugs (e.g., gene therapies, monoclonal antibodies, biosimilars), protein–receptor dynamics, and new therapeutic indications. A molecular dynamics (MD) simulation exercise is included to teach QM/MM applications. Future projections for 2030–2035 emphasize QM’s transformative impact on personalized medicine and undruggable targets. The qualifications and tools required for researchers, including advanced degrees, programming skills, and software such as Gaussian and Qiskit, are outlined, along with sources for training and resources. Specific publications on quantum mechanics (QM) in drug discovery relevant to QM and molecular dynamics (MD) studies are incorporated. Challenges, such as computational cost and expertise requirements, are addressed, offering a roadmap for educators and researchers to leverage quantum mechanics (QM) and molecular dynamics (MD) in drug discovery. Full article

Quantum computing is emerging as a key enabler of digital transformation in data science and STEM education. This study investigates how quantum computing can be meaningfully integrated into higher education by combining a dual approach: a structured assessment of the specialized literature and a practical evaluation of educational tools. First, a science mapping study based on 281 peer-reviewed publications indexed in Scopus (2015–2024) identifies growth trends, thematic clusters, and international collaboration networks at the intersection of quantum computing, data science, and education. Second, a comparative analysis of widely used educational platforms—such as Qiskit, Quantum Inspire, QuTiP, and Amazon Braket—is conducted using pedagogical criteria including accessibility, usability, and curriculum integration. The results highlight a growing convergence between quantum technologies, artificial intelligence, and data-driven learning. A strategic framework and roadmap are proposed to support the gradual and scalable adoption of quantum literacy in university-level STEM programs. Full article

This article presents a novel approach to the decomposition of unitary operations for 3-qubit systems by 28 controlled rotations and no permutations. The QR decomposition is described, which is based on the concept of the discrete signal-induced heap transform (DsiHT) and its quantum analogue. This transform is generated by a given signal and may use different paths, or orders, of processing the data, and, among them, one can find paths that allow one to construct efficient quantum circuits for implementing multi-qubit unitary gates. The case of real unitary matrices is considered. The proposed approach is described in detail, and quantum circuits are presented for computing 3-qubit operations. This approach allowed us to write simple Qiskit codes to implement the decomposition of 3-qubit operations. Examples with quantum circuits for the quantum 3-qubit quantum cosine and Hartley transforms are described. Full article

In this work, we present a training method for a Fully Quantum Neural Network (FQNN) based entirely on quantum circuits. The model processes data exclusively through quantum operations, without incorporating classical neural network layers. In the proposed architecture, the roles of classical neurons and weights are assumed, respectively, by qubits and parameterized quantum gates: input features are encoded into quantum states of qubits, while the network weights correspond to the rotation angles of quantum gates that govern the system’s state evolution. The optimization of gate parameters is performed using directional gradient estimation, where gradients are numerically approximated via finite differences, eliminating the need for analytic derivation. The training objective is defined as the quantum-state fidelity, which measures the similarity between the network’s output state and a reference state representing the correct class. Experiments were conducted using the Qiskit AerSimulator, which allows for the accurate simulation of quantum circuits on a classical computer. The proposed approach was applied to the classification of the Iris dataset. The experimental results demonstrate that the FQNN is capable of effectively learning to distinguish between classes based on input features, achieving stable test accuracy across runs. These findings confirm the feasibility of constructing fully quantum classifiers without relying on hybrid quantum—classical architectures. The FQNN architecture consists of multiple quantum layers, each incorporating parameterized rotation operations and entanglement between qubits. The number of layers is determined by the ratio of quantum parameters (weights) to the number of input features. Each layer functions analogously to a hidden layer in a classical neural network, transforming the quantum-state space into a richer feature representation through controlled quantum operations. As a result, the network is capable of dynamically modeling dependencies among input features without the use of classical activation functions. Full article

Quantum walks are a powerful framework for simulating complex quantum systems and designing quantum algorithms, particularly for spatial search on graphs, where the goal is to find a marked vertex efficiently. In this work, we present efficient quantum circuits that implement the evolution operator of continuous-time quantum-walk-based search algorithms for three graph families: complete graphs, complete bipartite graphs, and hypercubes. For complete and complete bipartite graphs, our circuits exactly implement the evolution operator. For hypercubes, we propose an approximate implementation that closely matches the exact evolution operator as the number of vertices increases. Our Qiskit simulations demonstrate that even for low-dimensional hypercubes, the algorithm effectively identifies the marked vertex. Furthermore, the approximate implementation developed for hypercubes can be extended to a broad class of graphs, enabling efficient quantum search in scenarios where exact implementations are impractical. Full article

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

In this paper, electrical discharge images are classified using AI with quantum machine learning techniques. These discharges were originated in dielectric mineral oils and were detected by a high-resolution optical sensor. The captured images were processed in a Scikit-image environment to obtain a reduced number of features or qubits for later training of quantum circuits. Two quantum binary classification models were developed and compared in the Qiskit environment for four discharge binary combinations. The first was a quantum variational model (QVM), and the second was a conventional support vector machine (SVM) with a quantum kernel model (QKM). The execution of these two models was realized on three fault-tolerant physical quantum IBM computers. The novelty of this article lies in its application to a real problem, unlike other studies that focus on simulated or theoretical data sets. In addition, a study is carried out on the impact of the number of qubits in QKM, and it is shown that increasing the number of qubits in this model significantly improves the accuracy in the classification of the four binary combinations studied. In the QVM, with two qubits, an accuracy of 92% was observed in the first discharge combination in the three quantum computers used, with a margin of error of 1% compared to the simulation obtained on classical computers. Full article

We present the first step toward the quantum computing (QC) formulation of the electron nuclear dynamics (END) method within the variational quantum simulator (VQS) scheme: END/QC/VQS. END is a time-dependent, variational, on-the-flight, and non-adiabatic method to simulate chemical reactions. END represents nuclei with frozen Gaussian wave packets and electrons with a single-determinantal state in the Thouless non-unitary representation. Within the hybrid quantum/classical VQS, END/QC/VQS currently evaluates the metric matrix M and gradient vector V of the symplectic END/QC equations on the QC software development kit QISKIT, and calculates basis function integrals and time evolution on a classical computer. To adapt END to QC, we substitute the Thouless non-unitary representation with Fukutome unitary representation. We derive the first END/QC/VQS version for pure electronic dynamics in multielectron chemical models consisting of two-electron units with fixed nuclei. Therein, Fukutome unitary matrices factorize into triads of one-qubit rotational matrices, which leads to a QC encoding of one electron per qubit. We design QC circuits to evaluate M and V in one-electron diatomic molecules. In log2-log2 plots, errors and deviations of those evaluations decrease linearly with the number of shots and with slopes = −1/2. We illustrate an END/QC/VQS simulation with the pure electronic dynamics of H₂⁺ We discuss the present results and future END/QC/QVS extensions. Full article

In this paper, we use a simplified quantum counter to implement Grover’s algorithm-based quantum circuits to tackle the NP-hard exact cover problem (ECP). The ECP seeks a subcollection of sets such that every element is covered by exactly one set. Leveraging Grover’s algorithm, our quantum circuits achieve a quadratic speedup, querying the oracle O($\sqrt{N}$) times, compared to O(N) for classical methods, where $N=2^n$ is the total number of unstructured input instances and n is the number of input (quantum) bits. For the whole quantum circuit, the simplified quantum counter saves $(4mb-4m)\lfloor \pi /4\sqrt{N/M}\rfloor$ quantum gates and reduces the quantum circuit depth by $\left(2mb\right)\lfloor \pi /4\sqrt{N/M}\rfloor$ compared to Heidari et al.’s design, where $b=\lfloor logn\rfloor +1$ is the number of counting qubits used in a counter. Experimental results obtained using IBM Qiskit packages confirm the effectiveness of our quantum circuits. Full article

In the wake of quantum computing advancements and quantum algorithmic progress, quantum algorithms are increasingly being employed to address a myriad of combinatorial optimization problems. Among these, the Independent Domination Problem (IDP), a derivative of the Domination Problem, has practical implications in various real-world scenarios. Despite this, existing classical algorithms for the IDP are plagued by high computational complexity, and quantum algorithms have yet to tackle this challenge. This paper introduces a Quantum Approximate Optimization Algorithm (QAOA)-based approach to address the IDP. Utilizing IBM’s qasm_simulator, we have demonstrated the efficacy of the QAOA in solving the IDP under specific parameter settings, with a computational complexity that surpasses that of classical methods. Our findings offer a novel avenue for the resolution of the IDP. Full article

In this work, we studied the use of Quantum Machine Learning (QML) algorithms for binary classification and compared their performance with classical Machine Learning (ML) methods. QML merges principles of Quantum Computing (QC) and ML, offering improved efficiency and potential quantum advantage in data-driven tasks and when solving complex problems. In binary classification, where the goal is to assign data to one of two categories, QML uses quantum algorithms to process large datasets efficiently. Quantum algorithms like Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) exploit quantum parallelism and entanglement to enhance performance over classical methods. This study focuses on two common QML algorithms, Quantum Support Vector Classifier (QSVC) and QNN. We used the Qiskit software and conducted the experiments with three different datasets. Data preprocessing included dimensionality reduction using Principal Component Analysis (PCA) and standardization using scalers. The results showed that quantum algorithms demonstrated competitive performance against their classical counterparts in terms of accuracy, while QSVC performed better than QNN. These findings suggest that QML holds potential for improving computational efficiency in binary classification tasks. This opens the way for more efficient and scalable solutions in complex classification challenges and shows the complementary role of quantum computing. Full article

Flexi-grid technology has revolutionized optical networking by enabling Elastic Optical Networks (EONs) that offer greater flexibility and dynamism compared to traditional fixed-grid systems. As data traffic continues to grow exponentially, the need for efficient and scalable solutions to the routing and spectrum assignment (RSA) problem in EONs becomes increasingly critical. The RSA problem, being NP-Hard, requires solutions that can simultaneously address both spatial routing and spectrum allocation. This paper proposes a novel quantum-based approach to solving the RSA problem. By formulating the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model, we employ the Quantum Approximate Optimization Algorithm (QAOA) to effectively solve it. Our approach is specifically designed to minimize end-to-end delay while satisfying the continuity and contiguity constraints of frequency slots. Simulations conducted using the Qiskit framework and IBM-QASM simulator validate the effectiveness of our method. We applied the QAOA-based RSA approach to small network topology, where the number of nodes and frequency slots was constrained by the limited qubit count on current quantum simulator. In this small network, the algorithm successfully converged to an optimal solution in less than 30 iterations, with a total runtime of approximately 10.7 s with an accuracy of $78.8\%$. Additionally, we conducted a comparative analysis between QAOA, integer linear programming, and deep reinforcement learning methods to evaluate the performance of the quantum-based approach relative to classical techniques. This work lays the foundation for future exploration of quantum computing in solving large-scale RSA problems in EONs, with the prospect of achieving quantum advantage as quantum technology continues to advance. Full article

Merging machine learning with the power of quantum computing holds great potential for data-driven decision making and the development of powerful models for complex datasets. This area offers the potential for improving the accuracy of the real-time prediction of renewable energy production, such as solar irradiance forecasting. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to develop and evaluate a quantum neural network model for solar irradiance prediction up to 3 h in advance. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. The proposed framework could provide competitive results compared to its competitors, considering forecasting intervals of 5 to 120 min ahead, where it was the fourth best-performing paradigm. For 3 h ahead predictions, the proposed model achieved the second-best results compared with the other approaches, reaching a root mean squared error of 77.55 W/m² and coefficient of determination of 80.92% for global horizontal irradiance forecasting. The results for longer forecasting horizons suggest that the quantum model may process spatiotemporal information from the input dataset in a manner not attainable by the current classical approaches, thus improving forecasting capacity in longer predictive windows. Full article

This research aims to establish a secure system for key exchange by using post-quantum cryptography (PQC) schemes in the classic channel of quantum key distribution (QKD). Modern cryptography faces significant threats from quantum computers, which can solve classical problems rapidly. PQC schemes address critical security challenges in QKD, particularly in authentication and encryption, to ensure the reliable communication across quantum and classical channels. The other objective of this study is to balance security and communication speed among various PQC algorithms in different security levels, specifically CRYSTALS-Kyber, CRYSTALS-Dilithium, and Falcon, which are finalists in the National Institute of Standards and Technology (NIST) Post-Quantum Cryptography Standardization project. The quantum channel of QKD is simulated with Qiskit, which is a comprehensive and well-supported tool in the field of quantum computing. By providing a detailed analysis of the performance of these three algorithms with Rivest–Shamir–Adleman (RSA), the results will guide companies and organizations in selecting an optimal combination for their QKD systems to achieve a reliable balance between efficiency and security. Our findings demonstrate that the implemented PQC schemes effectively address security challenges posed by quantum computers, while keeping the the performance similar to RSA. 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