Abstract: To address the challenge posed by noise in real quantum devices, quantum error mitigation techniques play a crucial role. These techniques are resource-efficient, making them suitable for implementation in noisy intermediate-scale quantum devices, unlike the more resource-intensive quantum error correction codes. A notable example of such a technique is Clifford Data Regression, which employs […]
Abstract: We present an information-theoretic approach to quantum state classification based on sequential Bayesian inference. In each measurement step, the algorithm updates a probability distribution over candidate states by applying Bayes’ rule to the observed outcome. For each measurement shot on an unknown quantum state, the algorithm selects the observable with the highest expected information […]
Abstract: Image processing is one of the most promising applications for quantum machine learning. Quanvolutional neural networks with nontrainable parameters are the preferred solution to run on current and near future quantum devices. The typical input preprocessing pipeline for quanvolutional layers comprises of four steps: optional input binary quantization, encoding classical data into quantum states, […]
Abstract: This study presents an investigation of the dual-discriminator hybrid quantum generative adversarial network (DDHQ-GAN), a framework designed to enhance the performance of conventional generative adversarial networks (GANs) through the incorporation of a hybrid quantum discriminator. The proposed DDHQ-GAN architecture comprises three primary components: a generator and two discriminators. The research evaluates the efficacy of […]
Abstract: We consider exact quantum circuit synthesis, quantum gate efficiency, and the spectral gap conjecture from the perspective of computable analysis. Circuit synthesis, in both its exact and its approximate variant, is fundamental to the circuit model of quantum computing. As an engineering problem, however, the practical and theoretical aspects of quantum circuit synthesis are […]
Abstract: Quantum computation requires high-fidelity qubit readout, preserving the quantum state. In the case of superconductings qubits, readout is typically performed using a complex analog experimental setup operating at room temperature, which poses significant technological and economic barriers to large system scalability. An alternative approach is to perform a cryogenic on-chip qubit readout based on […]
Abstract: Unlike most classical algorithms that take an input and give the solution directly as an output, quantum algorithms produce a quantum circuit that works as an indirect solution to computationally hard problems. In the full quantum computing workflow, most data processing remains in the classical domain except for running the quantum circuit in the […]
Abstract: Expressibility is a crucial factor of a parameterized quantum circuit (PQC). In the context of variational-quantum-algorithm-based quantum machine learning (QML), a QML model composed of a highly expressible PQC and a sufficient number of qubits is theoretically capable of approximating any arbitrary continuous function. While much research has explored the relationship between expressibility and […]
Abstract: Quantum Hamiltonian simulation is one of the most promising applications of quantum computing and forms the basis for many quantum algorithms. Benchmarking them is an important gauge of progress in quantum computing technology. We present a methodology and software framework to evaluate various facets of the performance of gate-based quantum computers on Trotterized quantum […]
Accurately predicting a quantum computer’s capability—which circuits it can run and how well it can run them—is a foundational goal of quantum characterization and benchmarking. As modern quantum computers become increasingly hard to simulate, we must develop accurate and scalable predictive capability models to help researchers and stakeholders decide which quantum computers to build and […]