This paper investigates a quantum extension of classical Multiple-Input Multiple-Output (MIMO) communication in which the conventional linear channel model is replaced by a parameterized multi-qubit unitary transformation. Within this framework, interference is represented through coherent quantum interactions rather than additive signal coupling. To recover transmitted information, a Variational Quantum Circuit (VQC) receiver is introduced that learns an approximate inverse channel transformation through supervised variational optimization. The proposed system is evaluated under realistic noisy intermediate-scale quantum (NISQ) conditions incorporating depolarizing noise, thermal relaxation, and measurement imperfections, and its performance is compared with that of standard classical detection methods. The results reveal a trade-off between the two approaches: classical detectors achieve substantially lower bit-error rates across much of the investigated parameter range but exhibit pronounced performance degradation for specific channel configurations, whereas the VQC receiver maintains a more uniform error profile as channel complexity increases, albeit at a higher average BER. These findings suggest that variational quantum receivers are not a direct replacement for classical detection methods, but rather a complementary approach that may offer increased performance stability in communication scenarios characterized by strong coupling and complex interference patterns.
This paper investigates a quantum extension of classical Multiple-Input Multiple-Output (MIMO) communication in which the conventional linear channel model is replaced by a parameterized multi-qubit unitary transformation. Within this framework, interference is represented through coherent quantum interactions rather than additive signal coupling. To recover transmitted information, a Variational Quantum Circuit (VQC) receiver is introduced that learns an approximate inverse channel transformation through supervised variational optimization. The proposed system is evaluated under realistic noisy intermediate-scale quantum (NISQ) conditions incorporating depolarizing noise, thermal relaxation, and measurement imperfections, and its performance is compared with that of standard classical detection methods. The results reveal a trade-off between the two approaches: classical detectors achieve substantially lower bit-error rates across much of the investigated parameter range but exhibit pronounced performance degradation for specific channel configurations, whereas the VQC receiver maintains a more uniform error profile as channel complexity increases, albeit at a higher average BER. These findings suggest that variational quantum receivers are not a direct replacement for classical detection methods, but rather a complementary approach that may offer increased performance stability in communication scenarios characterized by strong coupling and complex interference patterns.
Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices, where the quantum approximation optimization algorithm (QAOA) constitutes a promising candidate for demonstrating tangible quantum advantages based on NISQ devices. In this paper, we consider the maximum likelihood (ML) detection problem of binary symbols transmitted over a multiple-input and multiple-output (MIMO) channel, where finding the optimal solution is exponentially hard using classical computers. Here, we apply the QAOA for the ML detection by encoding the problem of interest into a level-pQAOA circuit having2pvariational parameters, which can be optimized by classical optimizers. This level-pQAOA circuit is constructed by applying the prepared Hamiltonian to our problem and the initial Hamiltonian alternately inpconsecutive rounds. More explicitly, we first encode the optimal solution of the ML detection problem into the ground state of a problem Hamiltonian. Using the quantum adiabatic evolution technique, we provide both analytical and numerical results for characterizing the evolution of the eigenvalues of the quantum system used for ML detection. Then, for level-1 QAOA circuits, we derive the analytical expressions of the expectation values of the QAOA and discuss the complexity of the QAOA based ML detector. Explicitly, we evaluate the computational complexity of the classical optimizer used and the storage requirement of simulating the QAOA. Finally, we evaluate the bit error rate (BER) of the QAOA based ML detector and compare it both to the classical ML detector and to the classical minimum mean squared error (MMSE) detector, demonstrating that the QAOA based ML detector is capable of approaching the performance of the classical ML detector.
Hybrid quantum-classical computing offers a promising pathway to harness quantum advantages while mitigating the hardware limitations of noisy intermediate-scale quantum (NISQ) devices. This work proposes a modular hybrid architecture that combines classical preprocessing, quantum-enhanced feature mapping through parameterized quantum circuits (PQCs), and adaptive classical optimization. The framework is designed to improve robustness and generalization of machine learning models in noisy environments, addressing critical issues such as overfitting, noise sensitivity, and hardware-induced errors. Experimental evaluations were conducted on benchmark datasets, including Iris, reduced-dimension MNIST, synthetic noisy XOR, Wine Quality, and Breast Cancer Wisconsin. The proposed model was tested under multiple noise models, such as Gaussian, salt-and-pepper, Poisson, and quantum-specific errors (bit-flip, phase-flip, and depolarizing), using 5-fold cross-validation and repeated trials for statistical rigor. Key performance metrics included Classification Accuracy (CA), Robustness Index (RI), and Training Stability (TS), with results reported as mean ± standard deviation and supported by paired t-tests (p < 0.05). Results demonstrate that the hybrid model consistently outperforms classical support vector machines (SVM) and standalone variational quantum classifiers (VQC) in noisy conditions, achieving smaller accuracy degradation and higher stability. The combination of quantum- enhanced feature mapping, optimizer synergy, and modular design is shown to be central to its resilience.
Characterization and exploitation of multiple channels between the transmitter and the receiver in multiple-input multiple-output (MIMO) communications brought a paradigm shift in classical communication systems. The techniques developed around MIMO communication systems not only brought unprecedented advancements in communication rates but also substantially improved the reliability of communication, measured by low error rates. We develop a framework for MIMO quantum communications with discrete-variable quantum systems. We propose a general model of MIMO quantum channels incorporating noise, losses, and crosstalk among multiple channels. We leverage the approximate quantum cloning to transmit imperfect clones of the input state over this channel setup. We demonstrate that transmitting multiple imperfect clones achieves better communication fidelity as compared to transmitting a single perfect copy of the state due to the diversity of the MIMO setup. We also demonstrate a practical tradeoff between fidelity and rate of communication and call it quantum diversity multiplexing tradeoff (DMT) due to its similarity with the well-known DMT in classical MIMO setups.
With the advancement of quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era, hybrid quantum-classical algorithms have emerged as promising approaches for addressing certain machine learning tasks. This study investigates the Variational Quantum Classifier (VQC), a hybrid model that integrates parameterized quantum circuits with classical optimization routines, implemented via IBM’s Qiskit framework. We provide a detailed account of the VQC architecture, including feature encoding, ansatz construction, and optimization methodology. To assess its performance, we conduct a comparative analysis between VQC and classical Support Vector Classifiers (SVC) across synthetic and real-world datasets, including Iris and a binary-filtered version of MNIST. Experimental results reveal that classical models consistently achieve higher accuracy and faster training times. However, the VQC demonstrates functional viability, particularly under noise-free conditions and reduced-dimensionality settings. These findings underscore the current limitations of quantum models while highlighting the potential of VQC as a proof-of-concept for quantum-enhanced learning as quantum hardware matures.
Massive multiple-input multiple-output (MIMO) systems are a key enabling technology for 5G-Advanced and emerging 6G networks, yet their performance is fundamentally constrained by high-dimensional, non-convex optimization problems arising from beamforming, power allocation, and user scheduling under imperfect channel state information (CSI).This paper proposes a hybrid quantum-classical optimization framework that formulates discrete and continuous massive MIMO resource allocation problems using Quadratic Unconstrained Binary Optimization (QUBO) and variational quantum algorithms.Discrete beam selection is addressed using quantum annealing, while continuous power control and hybrid beamforming are refined using variational quantum circuits within a classical feedback loop.Simulation results demonstrate improvements in aggregate spectral efficiency, runtime scalability, and robustness under imperfect CSI compared to conventional ZF and WMMSE baselines.The benefits of using the proposed solution include scalable runtime performance with respect to larger numbers of antennas; thus, it highlights the potential of optimizing quantum-assisted technologies for the future of wireless networks based on realistic assumptions regarding near-term NISQ-era performance.
The limited supply of qubits and significant quantum noise impose limitations on the capability of quantum algorithms in the Noisy Intermediate-Scale Quantum (NISQ) era. NISQ devices have a variety of applications, such as Variational Quantum Circuit (VQC), which provides answers to difficult optimization and machine learning problems. This paper presents a thorough investigation of quantum variational classification in the NISQ context, with a focus on comprehending noise's impact on various feature maps and VQCs. We evaluate the effectiveness of quantum classifiers using a variety of datasets, ranging from straightforward binary classification problems to more complex tasks. Our results reveal the critical role that feature maps and variational circuit selection play in mitigating the effect of noise, identifying specific quantum circuit designs that exhibit robustness even in noisy situations. In order to highlight the potential of quantum machine learning in solving complex problems within in the NISQ setting, this study emphasizes the delicate interaction between feature map selection, variational circuit design, dataset complexity, and quantum noise.