QMDUC: quantum multi-channel data uploading convolution for image classification

Abstract: Efficient quantum circuit compilation is crucial for maximizing the utility of Noisy Intermediate- Scale Quantum (NISQ) devices. This paper presents a novel adaptive quantum circuit compilation technique using dynamic programming, which significantly reduces circuit depth while maintaining high fidelity. Our approach, termed AdaQC (Adaptive Quantum Compiler), dynamically adjusts the compilation strategy based on the specific quantum hardware constraints and noise characteristics. We demonstrate up to 30% reduction in circuit depth compared to state-of-the-art compilers, with less than 1% loss in fidelity for benchmark circuits on superconducting qubit architectures. Furthermore, we provide a comprehensive analysis of AdaQC's performance across various quantum algorithms and hardware configurations, showcasing its adaptability and efficiency in real-world scenarios

Quantum circuit execution on Noisy IntermediateScale Quantum (NISQ) hardware remains fundamentally constrained by limited qubit capacity, elevated error rates, and temporal variability in hardware performance. Circuit cutting has emerged as a promising strategy to address these limitations by decomposing large quantum programs into smaller, hardwarecompatible subcircuits. However, existing approaches often lack adaptive execution capabilities and fail to account for the structural semantics of quantum circuits, resulting in suboptimal scheduling decisions and diminished fidelity. In this work, we present QUARTET-a novel partitionagnostic, resource-aware quantum scheduling framework. It combines ZX-calculus-based circuit simplification, Graph Convolution Network (GCN)-driven resource estimation, and a Deep Q-Network (DQN) agent for adaptive execution decisions. QUARTET uses ZX-graph abstractions to extract structural features from quantum circuits. These features are used by a pre-trained GCN to predict key resource metrics such as runtime, qubit count, and fidelity. Based on these predictions, the reinforcement learning agent selects the most suitable execution mode while ensuring semantic correctness through ZX-equivalence verification. Experimental evaluations over 50 different benchmark circuits demonstrate that QUARTET achieves a 50 % improvement in throughput over baseline direct execution, along with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{8 2 \%}$</tex> device utilization and a 68.6 % fidelity gain. Compared to state-of-the-art schedulers like FitCut and DRLQ, QUARTET improves throughput by up to 33 %, boosts utilization by up to 17 %, and achieves up to 21 % higher fidelity. Furthermore, incorporating verification during partitioned execution improves fidelity by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{1 4. 7 \%}$</tex> and reduces execution variance. These results establish QUARTET as a robust, structure-aware, and verification-backed middleware for orchestrating scalable quantum workloads.

Quantum Random Access Memory (QRAM) plays a pivotal role in accelerating quantum algorithms, especially in quantum machine learning. However, its implementation on noisy intermediate-scale quantum (NISQ) computers faces major challenges due to high qubit requirements and large circuit depth. These factors amplify quantum noise, limiting QRAM performance and scalability. In this work, we introduce a gate-pruning strategy that mitigates noise while preserving QRAM functionality. Our approach reduces circuit depth by eliminating small-angle rotation gates which are particularly noise-sensitive on NISQ hardware. This pruning significantly improves QRAM resilience without compromising data-loading efficiency or superposition-based querying. We further show that the pruned QRAM supports a variational quantum classifier capable of parallel training with logarithmic qubit scaling. Experiments on benchmark datasets confirm that our approach enhances noise resilience and classification accuracy, underscoring its potential for practical quantum machine learning in the NISQ era.

Advancements in noisy intermediate-scale quantum (NISQ) computing are steadily pushing these systems toward outperforming classical supercomputers on specific well-defined computational tasks. In this work we explore and control quantum chaos in NISQ systems using discrete-time quantum walks (DTQWs) on cyclic graphs. To efficiently implement quantum walks on NISQ hardware, we employ the quantum Fourier transform to diagonalize the conditional shift operator, optimizing circuit depth and fidelity. We experimentally realize the transition from quantum chaos to order via DTQW dynamics on both odd and even cyclic graphs, specifically 3- and 4-cycle graphs, using the counterintuitive Parrondo paradox strategy across three different NISQ devices. While the 4-cycle graphs exhibit high-fidelity quantum evolution, the 3-cycle implementation shows significant fidelity improvement when augmented with dynamical decoupling pulses. Our results demonstrate a practical approach to probing and harnessing controlled chaotic dynamics on real quantum hardware, laying the groundwork for future quantum algorithms and cryptographic protocols based on quantum walks.

The success of the current generation of Noisy Intermediate-Scale Quantum (NISQ) hardware shows that quantum hardware may be able to tackle complex problems even without error correction. One outstanding issue is that of coherent errors arising from the increased complexity of these devices. These errors can accumulate through a circuit, making their impact on algorithms hard to predict and mitigate. Iterative algorithms like Quantum Imaginary Time Evolution are susceptible to these errors. This article presents the combination of both noise tailoring using Randomized Compiling and error mitigation with a purification. We also show that Cycle Benchmarking gives an estimate of the reliability of the purification. We apply this method to the Quantum Imaginary Time Evolution of a Transverse Field Ising Model and report an energy estimation and a ground state infidelity both below 1\%. Our methodology is general and can be used for other algorithms and platforms. We show how combining noise tailoring and error mitigation will push forward the performance of NISQ devices.

The quantum approximate optimization algorithm (QAOA) is a method of approximately solving combinatorial optimization problems. While QAOA is developed to solve a broad class of combinatorial optimization problems, it is not clear which classes of problems are best suited for it. One factor in demonstrating quantum advantage is the relationship between a problem instance and the circuit depth required to implement the QAOA method. As errors in noisy intermediate-scale quantum (NISQ) devices increase exponentially with circuit depth, identifying lower bounds on circuit depth can provide insights into when quantum advantage could be feasible. Here, we identify how the structure of problem instances can be used to identify lower bounds for circuit depth for each iteration of QAOA and examine the relationship between problem structure and the circuit depth for a variety of combinatorial optimization problems including MaxCut and MaxIndSet. Specifically, we show how to derive a graph, G, that describes a general combinatorial optimization problem and show that the depth of circuit is at least the chromatic index of G. By looking at the scaling of circuit depth, we argue that MaxCut, MaxIndSet, and some instances of vertex covering and Boolean satisfiability problems are suitable for QAOA approaches while knapsack and traveling salesperson problems are not.

Alzheimer's disease (AD) presents significant diagnostic challenges owing to the subtle morphological similarities observed in the early stages, with traditional deep learning approaches often struggling to distinguish between the various stages of disease progression via structural Magnetic Resonance Imaging (MRI) data. Quantum computing offers unique advantages for medical image analysis, leveraging superposition and entanglement capabilities to process high-dimensional feature spaces beyond the limits of classical computation. This study introduces a hybrid quantum-classical neural network architecture (HQC-Net) for accurate four-class Alzheimer's disease classification, which uses quantum processing to detect patterns that are often invisible to classical spatial analysis methods. The proposed framework integrates classical feature extractors, including a custom CNN and modified ResNet18, with six-qubit variational quantum circuits that employ multiaxis rotation encoding (RY→RZ→RX), a quantum Fourier transform for spectral decomposition, and multihead attention for effective quantum-classical feature fusion. Comprehensive evaluations were conducted on the Kaggle dataset (5,121 samples) and the OASIS dataset (20,000 samples), incorporating realistic quantum noise modelling, including depolarising, amplitude damping, and phase damping channels. The modified QResNet18 configuration achieved a test accuracy of 99.67%, with perfect discriminative capability (AUC = 1.0000) on the OASIS dataset. Quantum processing demonstrated superior detection of very mild dementia (99.86% accuracy), which is crucial for early intervention. The proposed approach outperformed existing quantum-enhanced methods by 3.57 percentage points while effectively handling the increased diagnostic complexity associated with four-class classification. This study demonstrates a practical quantum advantage for multiclass neuroimaging classification, achieving superior diagnostic accuracy while maintaining computational efficiency and clinical deployment feasibility under current Noisy Intermediate-Scale Quantum (NISQ) hardware constraints.

In recent times, a variety of hybrid quantum-classical algorithms have been developed that aim to calculate the ground state energies of molecular systems on Noisy Intermediate-Scale Quantum (NISQ) devices. Albeit the utilization of shallow depth circuits in these algorithms, the optimization of ansatz parameters necessitates a substantial number of quantum measurements, leading to prolonged runtimes on the scantly available quantum hardware. Through our work, we lay the general foundation for an interdisciplinary approach that can be used to drastically reduce the dependency of these algorithms on quantum infrastructure. We showcase these pertinent concepts within the framework of the recently developed Projective Quantum Eigensolver (PQE) that involves iterative optimization of the nonlinearly coupled parameters through repeated residue measurements on quantum hardware. We demonstrate that one may perceive such a nonlinear optimization problem as a collective dynamic interplay of fast and slow relaxing modes. As such, the synergy among the parameters is exploited using an on-the-fly supervised machine learning protocol that dynamically casts the PQE optimization into a smaller subspace by reducing the effective degrees of freedom. We demonstrate analytically and numerically that our proposed methodology ensures a drastic reduction in the number of quantum measurements necessary for the parameter updates without compromising the characteristic accuracy. Furthermore, the machine learning model may be tuned to capture the noisy data of NISQ devices, and thus the predicted energy is shown to be resilient under a given noise model.

The variational quantum eigensolver is a promising way to solve the Schrödinger equation on a noisy intermediate-scale quantum (NISQ) computer, while its success relies on a well-designed wave function ansatz. Inspired by the quantum neural network, we propose a new hardware heuristic ansatz where its expressibility can be improved by increasing either the depth or the width of the circuit. Such a character makes this ansatz adaptable to different hardware environments. More importantly, it provides a general framework to improve the efficiency of the quantum resource utilization. For example, on a superconducting quantum computer where circuit depth is usually the bottleneck and the qubits thus cannot be fully used, circuit depth can be significantly reduced by introducing ancilla qubits. Ancilla qubits also make the circuit less sensitive to noises in practical application. These results open a new avenue to develop practical applications of quantum computation in the NISQ era.

In the era of noisy intermediate-scale quantum (NISQ) computing, the synergistic collaboration between quantum and classical computing models has emerged as a promising solution for tackling complex computational challenges. Long short-term memory (LSTM), as a popular network for modeling sequential data, has been widely acknowledged for its effectiveness. However, with the increasing demand for data and spatial feature extraction, the training cost of LSTM exhibits exponential growth. In this study, we propose the quantum convolutional long short-term memory (QConvLSTM) model. By ingeniously integrating classical convolutional LSTM (ConvLSTM) networks and quantum variational algorithms, we leverage the variational quantum properties and the accelerating characteristics of quantum states to optimize the model training process. Experimental validation demonstrates that, compared to various LSTM variants, our proposed QConvLSTM model outperforms in terms of performance. Additionally, we adopt a hierarchical tree-like circuit design philosophy to enhance the model’s parallel computing capabilities while reducing dependence on quantum bit counts and circuit depth. Moreover, the inherent noise resilience in variational quantum algorithms makes this model more suitable for spatiotemporal sequence modeling tasks on NISQ devices.

We are in the noisy intermediate-scale quantum (NISQ) devices' era, in which quantum hardware has become available for application in real-world problems. However, demonstrations of the usefulness of such NISQ devices are still rare. In this work, we consider a practical railway dispatching problem: delay and conflict management on single-track railway lines. We examine the train dispatching consequences of the arrival of an already delayed train to a given network segment. This problem is computationally hard and needs to be solved almost in real time. We introduce a quadratic unconstrained binary optimization (QUBO) model of this problem, which is compatible with the emerging quantum annealing technology. The model's instances can be executed on present-day quantum annealers. As a proof-of-concept, we solve selected real-life problems from the Polish railway network using D-Wave quantum annealers. As a reference, we also provide solutions calculated with classical methods, including the conventional solution of a linear integer version of the model as well as the solution of the QUBO model using a tensor network-based algorithm. Our preliminary results illustrate the degree of difficulty of real-life railway instances for the current quantum annealing technology. Moreover, our analysis shows that the new generation of quantum annealers (the advantage system) does not perform well on those instances, either.

The class imbalance of electrocardiogram (ECG) data is a serious impediment to the development of diagnostic systems for heart disease. To address this issue, this paper proposes HQ-DCGAN, a hybrid quantum deep convolutional generative adversarial network, specifically designed for the generation of ECGs. The proposed algorithm employs different quantum convolutional layers for the generator and discriminator as feature extractors and utilizes parameterized quantum circuits (PQCs) to enhance computational capabilities, along with the model-feature mapping process. Moreover, this algorithm preserves the nonlinearity and scalability inherent to classical convolutional neural networks (CNNs), thereby optimizing the utilization of quantum resources, and ensuring compatibility with contemporary quantum devices. In addition, this paper proposes a novel evaluation metric, 1D Fréchet Inception Distance (1DFID), to assess the quality of the generated ECG signals. Simulation experiments show that HQ-DCGAN exhibits strong performance in ECG signal generation. Furthermore, the generated signals achieve an average classification accuracy of 82.2%, outperforming the baseline algorithms. It has been experimentally proven that HQ-DCGAN is friendly to currently noisy intermediate-scale quantum (NISQ) computers, in terms of both number of qubits and circuit depths, while improving the stability.

We explore the use of symmetry-adapted perturbation theory (SAPT) as a simple and efficient means to compute interaction energies between large molecular systems with a hybrid method combining NISQ-era quantum and classical computers. From the one- and two-particle reduced density matrices of the monomer wavefunctions obtained by the variational quantum eigensolver (VQE), we compute SAPT contributions to the interaction energy [SAPT(VQE)]. At first order, this energy yields the electrostatic and exchange contributions for non-covalently bound systems. We empirically find from ideal statevector simulations that the SAPT(VQE) interaction energy components display orders of magnitude lower absolute errors than the corresponding VQE total energies. Therefore, even with coarsely optimized low-depth VQE wavefunctions, we still obtain sub kcal mol−1 accuracy in the SAPT interaction energies. In SAPT(VQE), the quantum requirements, such as qubit count and circuit depth, are lowered by performing computations on the separate molecular systems. Furthermore, active spaces allow for large systems containing thousands of orbitals to be reduced to a small enough orbital set to perform the quantum portions of the computations. We benchmark SAPT(VQE) (with the VQE component simulated by ideal statevector simulators) against a handful of small multi-reference dimer systems and the iron center containing human cancer-relevant protein lysine-specific demethylase 5 (KDM5A).

Rapid advancement in the domain of quantum technologies have opened up researchers to the real possibility of experimenting with quantum circuits, and simulating small-scale quantum programs. Nevertheless, the quality of currently available qubits and environmental noise pose a challenge in smooth execution of the quantum circuits. Therefore, efficient design automation flows for mapping a given algorithm to the Noisy Intermediate Scale Quantum (NISQ) computer becomes of utmost importance. State-of-the-art quantum design automation tools are primarily focused on reducing logical depth, gate count and qubit counts with recent emphasis on topology-aware (nearest-neighbour compliance) mapping. In this work, we extend the technology mapping flows to simultaneously consider the topology and gate fidelity constraints while keeping logical depth and gate count as optimization objectives. We provide a comprehensive problem formulation and multi-tier approach towards solving it. The proposed automation flow is compatible with commercial quantum computers, such as IBM QX and Rigetti. Our simulation results over 10 quantum circuit benchmarks, show that the fidelity of the circuit can be improved up to 3.37 × with an average improvement of 1.87 ×.

We introduce a new method to calculate phase shifts on noisy intermediate scale quantum (NISQ) hardware platforms using a wave packet edge time delay. The method uses the early and intermediate stages of the collision because the standard method based on the asymptotic out-state behavior is unreachable using today’s NISQ platforms. The calculation was implemented on a 4-site transverse Ising model in one spatial dimension with and without a potential interaction. A time evolution operator describing the progression of the system was constructed and transmission and reflection coefficients were calculated based on the identified quantum Fourier transformed momentum states. The detailed analysis of the phase shift calculations on both IBM superconducting transmon and University of Maryland ion trap quantum computers shows the platform independence of the methodology. This successful implementation of this wave packet preparation and projection on momentum eigenstates can now be performed with actual quantum computing hardware platforms. This method provides a procedure for calculating phase shifts and opens the possibility of using noisy intermediate scale quantum devices to perform real-time quantum mechanics and quantum field theory scattering calculations.