[논문 리뷰] Noisy intermediate-scale quantum (NISQ) algorithms

Kishor Bharti, Alba Cervera-Lierta외 12명|arXiv (Cornell University)|2021. 01. 21.

Quantum Computing Algorithms and Architecture참고 문헌 264인용 수 181

한 줄 요약

이 리뷰는 NISQ- 시대의 양자 알고리즘, 그 구성 요소들, 하드웨어 플랫폼, 도전 과제, 벤치마킹 도구, 그리고 물리학, 화학, ML, 최적화에 걸친 근시계 애플리케이션을 조사합니다.

ABSTRACT

A universal fault-tolerant quantum computer that can solve efficiently problems such as integer factorization and unstructured database search requires millions of qubits with low error rates and long coherence times. While the experimental advancement towards realizing such devices will potentially take decades of research, noisy intermediate-scale quantum (NISQ) computers already exist. These computers are composed of hundreds of noisy qubits, i.e. qubits that are not error-corrected, and therefore perform imperfect operations in a limited coherence time. In the search for quantum advantage with these devices, algorithms have been proposed for applications in various disciplines spanning physics, machine learning, quantum chemistry and combinatorial optimization. The goal of such algorithms is to leverage the limited available resources to perform classically challenging tasks. In this review, we provide a thorough summary of NISQ computational paradigms and algorithms. We discuss the key structure of these algorithms, their limitations, and advantages. We additionally provide a comprehensive overview of various benchmarking and software tools useful for programming and testing NISQ devices.

연구 동기 및 목표

• Summarize the concept of NISQ devices and their place in the quest for quantum advantage.
• Describe the architecture and components of variational quantum algorithms (VQAs) and their role in near-term computing.
• Provide an overview of alternative NISQ approaches beyond VQAs, such as quantum annealing and Gaussian boson sampling.
• Discuss benchmarking, software tooling, and near-term applications across physics, chemistry, ML, and optimization.

제안 방법

• Define objective/function as a Hamiltonian expectation value or other operatively measurable quantities (e.g., Pauli-string decompositions).
• Explain parameterized quantum circuits (PQC) and problem-inspired vs hardware-efficient ansätze.
• Detail measurement strategies, including Pauli-string measurements and fidelity-based objectives.
• Outline optimization strategies including gradient-based and gradient-free methods, plus resource-aware considerations.
• Describe non-VQA NISQ approaches (quantum annealing, Gaussian boson sampling, analog/digital-analog simulation).
• Summarize error mitigation, circuit compilation, and hardware-software co-design for NISQ devices.

실험 결과

연구 질문

• RQ1 What are the most effective building blocks and objective functions for NISQ-era quantum algorithms?
• RQ2 How do variational quantum algorithms perform on current hardware, including issues like barren plateaus and ansatz expressibility?
• RQ3 What benchmarking, software tools, and practical strategies maximize NISQ utility across applications?
• RQ4 What are the prospects and limitations of non-VQA NISQ approaches (annealing, GBS, analog simulation) for achieving quantum advantage?

주요 결과

• NISQ algorithms are mainly hybrid quantum-classical, relying on variational optimization of parameterized quantum circuits.
• Hamiltonian (or operator) expectation values decomposed into Pauli strings enable tractable measurement for VQAs.
• Barren plateaus and ansatz expressibility affect trainability and performance of VQAs on NISQ devices.
• A broad ecosystem of benchmarking metrics and software tools supports programming, testing, and comparing NISQ devices and algorithms.
• Non-VQA NISQ approaches (quantum annealing, Gaussian boson sampling, analog/digital-analog simulators) offer alternative paths to practical quantum advantage in specific tasks.