Emulation of Density Matrix Dynamics With Classical Analog Circuits

Analog circuits have emerged as a valuable quantum emulation and simulation platform. Specifically, they have been experimentally shown to excel in emulating coherent state vector dynamics and motifs of quantum circuits, such as the quantum Fourier transform, tensor product superpositions, two-level systems such as Josephson junctions, and nuclear magnetic resonance state dynamics, all on a […]

Optimized Distribution of Entanglement Graph States in Quantum Networks

Building large-scale quantum computers, essential to demonstrating quantum advantage, is a key challenge. Quantum networks can help address this challenge by enabling the construction of large, robust, and more capable quantum computing platforms by connecting smaller quantum computers. Moreover, unlike classical systems, quantum networks can enable fully secured long-distance communication. Thus, quantum networks lie at […]

Utilizing Quantum Annealing in Computed Tomography Image Reconstruction

One of the primary difficulties in computed tomography (CT) is reconstructing cross-sectional images from measured projections of a physical object. There exist several classical methods for this task of generating a digital representation of the object, including filtered backprojection or simultaneous algebraic reconstruction technique. Our research aims to explore the potential of quantum computing in […]

Benchmarking Quantum Machine Learning Kernel Training for Classification Tasks

Quantum-enhanced machine learning is a rapidly evolving field that aims to leverage the unique properties of quantum mechanics to enhance classical machine learning. However, the practical applicability of these methods remains an open question, particularly beyond the context of specifically crafted toy problems, and given the current limitations of quantum hardware. For more about this […]

Dissipative Variational Quantum Algorithms for Gibbs State Preparation

In recent years, variational quantum algorithms have gained significant attention due to their adaptability and efficiency on near-term quantum hardware. They have shown potential in a variety of tasks, including linear algebra, search problems, Gibbs, and ground state preparation. Nevertheless, the presence of noise in current day quantum hardware severely limits their performance. In this […]

Improving Probabilistic Error Cancellation in the Presence of Nonstationary Noise

In this article, we investigate the stability of probabilistic error cancellation (PEC) outcomes in the presence of nonstationary noise, which is an obstacle to achieving accurate observable estimates. Leveraging Bayesian methods, we design a strategy to enhance PEC stability and accuracy. Our experiments using a five-qubit implementation of the Bernstein–Vazirani algorithm and conducted on the […]

Simulation of Charge Stability Diagrams for Automated Tuning Solutions (SimCATS)

Quantum dots (QDs) must be tuned precisely to provide a suitable basis for quantum computation. A scalable platform for quantum computing can only be achieved by fully automating the tuning process. One crucial step is to trap the appropriate number of electrons in the QDs, typically accomplished by analyzing charge stability diagrams (CSDs). Training and […]

Learning a Quantum Computer’s Capability

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 […]

Approximate Solutions of Combinatorial Problems via Quantum Relaxations

This article delves into the role of the quantum Fisher information matrix (FIM) in enhancing the performance of parameterized quantum circuit (PQC)-based reinforcement learning agents. While previous studies have highlighted the effectiveness of PQC-based policies preconditioned with the quantum FIM in contextual bandits, its impact in broader reinforcement learning contexts, such as Markov decision processes, […]

Optimizing the Electrical Interface for Large-Scale Color-Center Quantum Processors

Quantum processors based on color centers in diamond are promising candidates for future large-scale quantum computers thanks to their flexible optical interface, (relatively) high operating temperature, and high-fidelity operation. Similar to other quantum computing platforms, the electrical interface required to control and read out such qubits may limit both the performance of the whole system […]