This perspective article analyzes the potential and critical challenges of employing quantum optimization algorithms to investigate phase transitions in quantum many-body systems during the Noisy Intermediate-Scale Quantum era. The simulation of strongly correlated systems is frequently intractable on classical computers due to the exponential growth of the Hilbert space and the fermionic sign problem. In this context, we review and compare the performance of traditional Variational Quantum Algorithms, such as the Variational Quantum Eigensolver and the Quantum Approximate Optimization Algorithm, against emerging heuristic approaches, specifically Feedback-based Quantum Algorithms, such as FALQON. We explore the applicability of these methods in the study of open phenomena in condensed matter physics, including Deconfined Quantum Criticality, strange metals, Many-Body Localization, topological phase transitions, and quantum spin liquids. We discuss how fundamental operational bottlenecks, notably expressibility- and noise-induced barren plateaus, severely compromise gradient-based optimization. We conclude that deterministic feedback-guided methods provide geometrically more robust trajectories for navigating the energy landscape of these systems, arguing that further advancement in the field will rely on deep hybridization and physics-informed circuit co-design towards fault tolerance.
This perspective article analyzes the potential and critical challenges of employing quantum optimization algorithms to investigate phase transitions in quantum many-body systems during the Noisy Intermediate-Scale Quantum era. The simulation of strongly correlated systems is frequently intractable on classical computers due to the exponential growth of the Hilbert space and the fermionic sign problem. In this context, we review and compare the performance of traditional Variational Quantum Algorithms, such as the Variational Quantum Eigensolver and the Quantum Approximate Optimization Algorithm, against emerging heuristic approaches, specifically Feedback-based Quantum Algorithms, such as FALQON. We explore the applicability of these methods in the study of open phenomena in condensed matter physics, including Deconfined Quantum Criticality, strange metals, Many-Body Localization, topological phase transitions, and quantum spin liquids. We discuss how fundamental operational bottlenecks, notably expressibility- and noise-induced barren plateaus, severely compromise gradient-based optimization. We conclude that deterministic feedback-guided methods provide geometrically more robust trajectories for navigating the energy landscape of these systems, arguing that further advancement in the field will rely on deep hybridization and physics-informed circuit co-design towards fault tolerance.
Variational quantum algorithms (VQAs) have established themselves as a central computational paradigm in the Noisy Intermediate-Scale Quantum (NISQ) era. By coupling parameterized quantum circuits (PQCs) with classical optimization, they operate effectively under strict hardware limitations. However, as quantum architectures transition toward early fault-tolerant (EFT) and ultimate fault-tolerant (FT) regimes, the foundational principles and long-term viability of VQAs require systematic reassessment. This review offers an insightful analysis of VQAs and their progression toward the fault-tolerant regime. We deconstruct the core algorithmic framework by examining ansatz design and classical optimization strategies, including cost function formulation, gradient computation, and optimizer selection. Concurrently, we evaluate critical training bottlenecks, notably barren plateaus (BPs), alongside established mitigation strategies. The discussion then explores the EFT phase, detailing how the integration of quantum error mitigation and partial error correction can sustain algorithmic performance. Addressing the FT phase, we analyze the inherent challenges confronting current hybrid VQA models. Furthermore, we synthesize recent VQA applications across diverse domains, including many-body physics, quantum chemistry, machine learning, and mathematical optimization. Ultimately, this review outlines a theoretical roadmap for adapting quantum algorithms to future hardware generations, elucidating how variational principles can be systematically refined to maintain their relevance and efficiency within an error-corrected computational environment.
Variational Quantum Algorithms are a major method of harnessing Noisy Intermediate-Scale Quantum devices to solve classically intractable problems in physics, chemistry, optimization, and machine learning. The current review offers a critical overview of VQAs, with special emphasis on their theoretical backgrounds, algorithmic structures, and application performance within realistic models of noise. Several key variants, such as the Variational Quantum Eigensolver, Quantum Approximate Optimization Algorithm, and Quantum Neural Networks, are compared in terms of noise robustness, ansatz archi- tecture, and hybrid quantum-classical methods of optimization. Benchmarks in fields such as quantum chemistry, finance modeling, and particles and nuclei physics reveal the poten- tial of VQAs in achieving practical quantum advantage. Fundamentally limiting factors 1such as barren plateaus, optimization overhead, and hardware constraints are considered in addition to mitigation strategies, including adaptive construction of ansatz, informed training in the presence of noise, and quantum error correction using QVECTOR. The paper is concluded with an overview of the scalability and future of VQAs in the direction of transitioning toward fault-tolerant quantum computing.
Variational quantum algorithms (VQAs) have established themselves as a central computational paradigm in the Noisy Intermediate-Scale Quantum (NISQ) era. By coupling parameterized quantum circuits with classical optimization, they operate effectively under strict hardware limitations. However, as quantum architectures transition toward early fault-tolerant (EFT) and ultimate fault-tolerant (FT) regimes, the foundational principles and long-term viability of VQAs require systematic reassessment. This review offers a comprehensive analysis of VQAs and their progression toward the FT regime. We deconstruct the core algorithmic framework by examining ansatz design and classical optimization strategies, including cost function formulation, gradient computation, and optimizer selection. Concurrently, we evaluate critical training bottlenecks, notably barren plateaus (BPs), alongside established mitigation strategies. The discussion then explores the EFT phase, detailing how the integration of quantum error mitigation (QEM) and partial error correction can sustain algorithmic performance. Addressing the FT phase, we analyze the inherent challenges confronting current hybrid VQA models. Furthermore, we synthesize recent VQA applications across diverse domains, including many-body physics, quantum chemistry, machine learning, and mathematical optimization. Ultimately, this review outlines a theoretical roadmap for adapting quantum algorithms to future hardware generations, elucidating how variational principles can be systematically refined to maintain their relevance and efficiency within an error-corrected computational environment.
In this thesis, I explore the domain of hybrid quantum-classical computation, the foremost approach for utilizing Noisy Intermediate-Scale Quantum (NISQ) devices. The opening chapter presents an overview of Variational Quantum Algorithms (VQAs), highlighting the primary algorithmic challenges. It offers an in-depth review of the Quantum Approximate Optimization Algorithm (QAOA), discussing its variants for ground state preparation. In the second chapter, we apply QAOA for the supervised learning of a simple Binary Neural Network. This model represents an idealized yet prototypical example of classical combinatorial optimization problems involving multi-spin interactions. In the following chapters, the discussion shifts toward quantum many-body ground state preparation, focusing on the one-dimensional Heisenberg XYZ model and the longitudinal-transverse-field Ising model (LTFIM). We have developed a novel technique that, at any point in the phase diagram, leverages the transferability of a specific class of optimal schedules from systems with small to those with larger numbers of qubits. This approach mitigates trainability issues, specifically vanishing gradients (Barren Plateaus). Next, we tailor a QAOA scheme to characterize a topological quantum phase transition within a lattice gauge theory model. This investigation is particularly significant due to its implications for high-energy physics and relevance to quantum error correction and surface codes. In the concluding chapter, I propose new stimulating research directions and help to identify core challenges and unresolved questions in variational quantum computing that transcend any particular application domain.
Quantum Variational Circuits (QVCs) are often claimed as one of the most potent uses of both near term and long term quantum hardware. The standard approaches to optimizing these circuits rely on a classical system to compute the new parameters at every optimization step. However, this process can be extremely challenging, due to the nature of navigating the exponentially scaling complex Hilbert space, barren plateaus, and the noise present in all foreseeable quantum hardware. Although a variety of optimization algorithms are employed in practice, there is often a lack of theoretical or empirical motivations for this choice. To this end we empirically evaluate the potential of many common gradient and gradient free optimizers on a variety of optimization tasks. These tasks include both classical and quantum data based optimization routines. Our evaluations were conducted in both noise free and noisy simulations. The large number of problems and optimizers evaluated yields strong empirical guidance for choosing optimizers for QVCs that is currently lacking.
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.