Variational quantum optimization algorithms, such as the variational quantum eigensolver (VQE) or the quantum approximate optimization algorithm (QAOA), are among the most studied quantum algorithms. In our work, we evaluate and improve an algorithm based on the VQE, which uses exponentially fewer qubits compared to the QAOA. We highlight the numerical instabilities generated by encoding […]
Quantum information cannot be perfectly cloned, but approximate copies of quantum information can be generated. Quantum telecloning combines approximate quantum cloning, more typically referred to as quantum cloning, and quantum teleportation. Quantum telecloning allows approximate copies of quantum information to be constructed by separate parties, using the classical results of a Bell measurement made on […]
We implement a simulation of a quantum field theory in 1+1 space–time dimensions on a gate-based quantum computer using the light-front formulation of the theory. The nonperturbative simulation of the Yukawa model field theory is verified on IBM’s simulator and is also demonstrated on a small-scale IBM circuit-based quantum processor, on the cloud, using IBM […]
Text sentiment analysis is an important task in natural language processing and has always been a hot research topic. However, in low-resource regions such as South Asia, where languages like Bengali are widely used, the research interest is relatively low compared to high-resource regions due to limited computational resources, flexible word order, and high inflectional […]
Recently, quantum computing has been proven as an ideal theory for the design of fuzzy inference engines, thanks to its capability to efficiently solve the rule explosion problem. In this scenario, a quantum fuzzy inference engine (QFIE) was proposed as a quantum algorithm able to generate an exponential computational advantage over conventional fuzzy inference engines. […]
Quanvolutional neural networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of traditional convolution. Unfortunately, the qubit’s reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this article, we aim […]
Quanvolutional Neural Networks (QNNs) have been successful in image classification, exploiting inherent quantum capabilities to improve performance of the traditional convolution. Unfortunately, the qubit’s reliability can be a significant issue for QNNs inference, since its logical state can be altered by both intrinsic noise and by the interaction with natural radiation. In this paper we […]
In this article, we develop a generic controlled alternate quantum walk model by combining parity-dependent quantum walks with distinct arbitrary memory lengths and propose a hash function (called QHFM-P) based on this model. The statistical properties of the proposed scheme are stable with respect to the coin parameters of the underlying controlled quantum walks, and […]
Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to […]
Recent advancements have highlighted the limitations of current quantum systems, particularly the restricted number of qubits available on near-term quantum devices. This constraint greatly inhibits the range of applications that can leverage quantum computers. Moreover, as the available qubits increase, the computational complexity grows exponentially, posing additional challenges. Consequently, there is an urgent need to […]