Quantum devices are extremely noisy due to its inherent architecture. This can introduce errors or completely erase the information stored in qubits. High noise levels in a quantum device can lead to errors even when the quantum circuit is not buggy. Therefore, it is essential to verify that the noise level of the device is tolerable while running the quantum circuit. In this paper, we propose a quantum device testing framework using concurrent assertions. Specifically, we introduce a new type of assertion “QcAssert”, which has the ability to run concurrently with the quantum circuit to ensure that the quantum device is working as expected. We demonstrate the effectiveness of the QcAssert in dynamic device testing using a suite of popular quantum benchmarks, including Shor’s factoring algorithm and Grover’s search algorithm.

Interpreting the results of a quantum computer can pose a significant challenge due to inherent noise in these mesoscopic quantum systems. Quantum measurement, a critical component of quantum computing, involves determining the probabilities linked with quantum states post-multiple circuit computations based on quantum readout values provided by hardware. While there are promising classification-based solutions, they can either misclassify or necessitate excessive measurements, thereby proving to be costly. This article puts forth an efficient method to discern the quantum state by analyzing the probability distributions of data post-measurement. Specifically, we employ cumulative distribution functions to juxtapose the measured distribution of a sample against the distributions of basis states. The efficacy of our approach is demonstrated through experimental results on a superconducting transmon qubit architecture, which shows a substantial decrease (88%) in single qubit readout error compared to state-of-the-art measurement techniques. Moreover, we report additional error reduction (12%) compared to state-of-the-art measurement techniques when our technique is applied to enhance existing multi-qubit classification techniques. We also demonstrate the applicability of our proposed method for higher dimensional quantum systems, including classification of single qutrits as well as multiple qutrits.

In order to physically realize a robust quantum gate, a specifically tailored laser pulse needs to be derived via strategies such as quantum optimal control. Unfortunately, such strategies face exponential complexity with quantum system size and become infeasible even for moderate-sized quantum circuits. In this paper, we propose an automated framework for effective utilization of these quantum resources. Specifically, this paper makes three important contributions. First, we utilize an effective combination of register compression and dimensionality reduction to reduce the area of a quantum circuit. Next, due to the properties of an autoencoder, the compressed gates produced are robust even in the presence of noise. Finally, our proposed compression reduces the computation time of quantum control. Experimental evaluation using popular quantum algorithms demonstrates that our proposed approach can enable efficient generation of noise-resilient control pulses while state-of-the-art fails to handle large-scale quantum systems.

Quantum measurement is one of the critical steps in quantum computing that determines the probabilities associated with qubit states after conducting several circuit ex-ecutions and measurements. As a mesoscopic quantum system, real quantum computers are prone to noise. Therefore, a major challenge in quantum measurement is how to correctly inter-pret the noisy results of a quantum computer. While there are promising classification based solutions, they either produce incorrect results (misclassify) or require many measurements (expensive). In this paper, we present an efficient technique to estimate a qubit's state through analysis of probability distributions of post-measurement data. Specifically, we estimate the state of a qubit using cumulative distribution functions to compare the measured distribution of a sample with the distributions of basis states$\vert 0\rangle$and$\vert 1\rangle$. Our experimental results demonstrate a drastic reduction (78%) in single qubit readout error. It also provides significant reduction (12%) when used to boost existing multi-qubit discriminator models.

A major challenge in exploiting the principles of quantum information is the influence of noise which tends to work against the quantum features of a system. The traditional quantum gate error model provides a general framework to study noisy quantum circuits, but may fail to capture intricate microscopic interactions between qubits in addition to the environment. This can significantly overestimate the feasibility of implementing useful quantum algorithms on physical quantum hardware. In this paper, we study a noise model driven by random matrix theory that captures an effective microscopic interaction, and assess the suitability of the quantum gate error model. Our approach assumes noise arises due to interactions with many physical sources, and modeled using random matrix theory and applied to the quantum circuit model.

Spectral graph partitioning is a well known technique to estimate clusters in undirected graphs. Recent approaches explored efficient spectral algorithms for directed and mixed graphs utilizing various matrix representations. Despite its success in clustering tasks, classical spectral algorithms suffer from a cubic growth in runtime. In this paper, we propose a quantum spectral clustering algorithm for discovering clusters and properties of mixed graphs. Our experimental results based on numerical simulations demonstrate that our quantum spectral clustering outperforms classical spectral clustering techniques. Specifically, our approach leads to a linear growth in complexity, while state-of-the-art classical counterpart leads to cubic growth. In a case study, we apply our proposed algorithm to preform unsupervised machine learning using both real and simulated quantum computers. This work opens an avenue for efficient implementation of machine learning algorithms on directed as well as mixed graphs by making use of the inherent potential quantum speedup.

A major challenge in realizing efficient and powerful quantum algorithms is quantum noise. Quantum noise itself is a sophisticated topic that is not seen in the classical domain. In this paper, we explore the impact of noise on quantum algorithms in Noisy Intermediate-Scale Quantum (NISQ) systems. This paper first introduces the origins of quantum noise. Next, it proposes a common treatment to simplify the view of quantum noise. Finally, it presents a case study to investigate the impact of noise on quantum Fourier transform algorithm.