Variational algorithms are among the first practical applications of quantum computing, but their performance is limited by today's noisy intermediate-scale quantum (NISQ) devices. The authors propose scalable, depth-optimal solutions to overcome these limitations by integrating optimal mapping algorithms applied to small submodules of a given NISQ computer (focusing on popular linear and T- and H-shaped subtopologies). Identification of the best qubits combined with postselection keeps the error rate in check. The team reports up to 82% reduction in circuit depth and an average of 138% better success probability, thus paving the way for reliable quantum computing ecosystems of tomorrow.
Variational algorithms are among the first practical applications of quantum computing, but their performance is limited by today's noisy intermediate-scale quantum (NISQ) devices. The authors propose scalable, depth-optimal solutions to overcome these limitations by integrating optimal mapping algorithms applied to small submodules of a given NISQ computer (focusing on popular linear and T- and H-shaped subtopologies). Identification of the best qubits combined with postselection keeps the error rate in check. The team reports up to 82% reduction in circuit depth and an average of 138% better success probability, thus paving the way for reliable quantum computing ecosystems of tomorrow.
Variational quantum algorithms (VQAs) can potentially solve practical problems using contemporary Noisy Intermediate Scale Quantum (NISQ) computers. VQAs find near-optimal solutions in the presence of qubit errors by classically optimizing a loss function computed by parameterized quantum circuits. However, developing and testing VQAs is challenging due to the limited availability of quantum hardware, their high error rates, and the significant overhead of classical simulations. Furthermore, VQA researchers must pick the right initialization for circuit parameters, utilize suitable classical optimizer configurations, and deploy appropriate error mitigation methods. Unfortunately, these tasks are done in an ad-hoc manner today, as there are no software tools to configure and tune the VQA hyperparameters.
Quantum computing has the potential to solve complex problems that are outside the scope of classical systems, making it a standard shifter. Current devices, which are considered as Noisy Intermediate-Scale Quantum (NISQ) systems, are insufficient in their practical utility due to hardware limitations, error rates, and qubit counts. The dynamic growths wanted to exceed the NISQ era and grow scalable quantum algorithms and architectures that can achieve useful quantum benefit in real-world applications are reviewed in this research. This research gives a comprehensive examination of the complications in scaling quantum systems, adjacent resource efficacy, qubit connectivity, and error correction. In addition to connecting architectural innovations like modular quantum computing, error-resilient circuit design, and distributed quantum systems, the paper provides new hybrid quantum-classical algorithms planned to exploit performance in noisy environments. Results from benchmarking in arenas like machine learning, optimization, and quantum chemistry show how these methods can provide short-term quantum benefit. Lastly, this paper proposes a research roadmap for quantum computing that highlights the requirement of co-designing algorithms, software, and hardware. The goal of this effort is to straighten the progress of quantum systems that can resolve problems in the real world at scale by addressing the inadequacies of NISQ devices and emphasizing scalable solutions.
Noisy intermediate-scale quantum (NISQ) computers are coming online. The lack of error-correction in these devices prevents them from realizing the full potential of fault-tolerant quantum computation, a technology that is known to have significant practical applications, but which is years, if not decades, away. A major open question is whether NISQ devices will have practical applications. In this thesis, we explore and implement proposals for using NISQ devices to achieve practical applications. In particular, we develop and execute variational quantum algorithms for solving problems in combinatorial optimization and quantum chemistry. We also execute a prototype of a protocol for generating certified random numbers. We perform our experiments on a superconducting qubit processor developed at Google. While we do not perform any quantum computations that are beyond the capabilities of classical computers, we address many implementation challenges that must be overcome to succeed in such an endeavor, including optimization, efficient compilation, and error mitigation. In addressing these challenges, we push the limits of what can currently be done with NISQ technology, going beyond previous quantum computing demonstrations in terms of the scale of our experiments and the types of problems we tackle. While our experiments demonstrate progress in the utilization of quantum computers, the limits that we reached underscore the fundamental challenges in scaling up towards the classically intractable regime. Nevertheless, our results are a promising indication that NISQ devices may indeed deliver practical applications.
Abstract Noisy Intermediate-Scale Quantum (NISQ) devices are near-term quantum computing devices that inherently suffer from imperfect gate implementation, decoherence, and measurement noise. The main type of variational quantum algorithms(VQAs) is based on Parameterized Quantum Circuits (PQCs), which, however, face practical challenges due to optimization instability of optimization caused by noise and reduced performance. In this paper, we suggest a comprehensive noise-resilient optimization framework for PQCs, which explicitly incorporates noise-resilient circuit architecture design, incorporating noise-aware training strategies into the variational optimization loop. The framework uses circuit structures that are efficient in depth and are hardware compatible in addition to adaptive optimizer choice, stochastic optimization, and averaging as a part of repeated measurement to reduce the impacts of realistic noise. Large-scale experiments are performed with different amounts of qubits, depths of the circuit and noise strengths with realistic noise models. The findings indicate that the convergence rate is faster, the stability is increased, the variability of run-to-run variability is minimized, and other robustness is better than the noise-unaware optimization techniques. Moreover, systematic studies determine optimal operating regions that optimally trade-off expressibility and noise tolerance to offer useful design advice on trusted implementation of VQAs with near-term quantum hardware.
Optimization problems are pervasive across various domains, including logistics, finance, machine learning, and operations research. Quantum computing has emerged as a promising frontier to address these challenges, offering potential speedups for certain classes of optimization tasks. This paper explores the development and application of quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and Grover’s search algorithm, tailored for optimization. Recent advancements in hardware, hybrid quantum-classical approaches, and variational techniques have enabled practical implementations on Noisy Intermediate-Scale Quantum (NISQ) devices. Challenges such as noise, scalability, and performance limitations are also discussed. Through theoretical analysis and case studies, this work demonstrates how quantum computing can complement classical methods, paving the way for breakthroughs in solving complex optimization problems.