This survey presents a system-level architectural analysis of quantum computing for portfolio optimization, an NP-hard problem in FinTech facing scalability challenges in classical cloud infrastructures. Based on a systematic review of 30 representative papers (2020-2025), three dominant paradigms—Quantum Annealing (QA), Variational Quantum Algorithms (VQAs), and Quantum Machine Learning (QML)—are evaluated specifically by focusing on their constraint enforcement strategies and latency profiles . The analysis suggests that QA currently demonstrates significant near-term operational readiness for constraint-heavy portfolios due to its native solvers and lower communication overhead compared to iterative gate-based methods. Conversely, while VQAs offer high expressivity via mixers, they are operationally limited by the communication complexity of hybrid loops over public networks. Furthermore, QML approaches are shown to shift the bottleneck from optimization to state preparation, where data encoding latency often negates inference advantages. These engineering insights are synthesized into a practical implementation roadmap, advocating for distributed hybrid architectures to bridge the gap between theoretical quantum advantage and low-latency financial deployment.
This survey presents a system-level architectural analysis of quantum computing for portfolio optimization, an NP-hard problem in FinTech facing scalability challenges in classical cloud infrastructures. Based on a systematic review of 30 representative papers (2020-2025), three dominant paradigms—Quantum Annealing (QA), Variational Quantum Algorithms (VQAs), and Quantum Machine Learning (QML)—are evaluated specifically by focusing on their constraint enforcement strategies and latency profiles . The analysis suggests that QA currently demonstrates significant near-term operational readiness for constraint-heavy portfolios due to its native solvers and lower communication overhead compared to iterative gate-based methods. Conversely, while VQAs offer high expressivity via mixers, they are operationally limited by the communication complexity of hybrid loops over public networks. Furthermore, QML approaches are shown to shift the bottleneck from optimization to state preparation, where data encoding latency often negates inference advantages. These engineering insights are synthesized into a practical implementation roadmap, advocating for distributed hybrid architectures to bridge the gap between theoretical quantum advantage and low-latency financial deployment.
Optimization problems across domains such as logistics, finance, and artificial intelligence often involve complex and NP-hard formulations that are computationally challenging for classical algorithms due to scalability and efficiency limitations. The study aims to systematically investigate the role of Quantum Machine Learning (QML) in addressing complex optimization problems and to analyze its advantages over traditional optimization techniques. A comprehensive survey and comparative analysis of key QML algorithms, including Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Quantum Neural Networks (QNNs), and Quantum Support Vector Machines (QSVMs), is conducted by examining their working principles, optimization capabilities, and real-world applications. The findings indicate that QML algorithms demonstrate significant potential in exploring large solutions spaces efficiently, achieving faster convergence, and providing improved optimization performance compared to classical approaches, although challenges such as quantum noise, scalability, and hardware limitations remain. The novelty of this study lies in providing a unified and critical comparative framework that integrates multiple QML optimization algorithms, highlights their practical feasibility, and identifies key research gaps hindering their real-world deployment. The implications of this research provide valuable insights for researchers and practitioners in selecting appropriate QML techniques and emphasize the need for advancements in hybrid quantum -classical systems, algorithms design, and quantum hardware to enable practical large-scale optimization.
Quantum Computing is a dynamic field which has evolved since its inception around 3 decades back, to the development of various powerful algorithms including Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) to name a few. Today's quantum computers are not as powerful as the current breed of supercomputers. However, the existing breed of NISQ (Noisy Intermediate Scale Quantum) quantum computers have a significant potential to provide faster solutions to problems in various domains which are not just relevant for the present but also for the future such as Quantum Machine Learning in the Fintech domain. Portfolio Optimization (PO) is one such problem in research and the financial services industry at large. In principle, Quantum Computing, makes it possible to arrive at an optimal portfolio composition for a high return and low risk investment much faster than any existing supercomputer today. This review paper examines literature on classical and quantum machine learning approaches for Mutual Fund PO, analyzing 44 papers from 2003 to 2023. PO being a vast topic, we limit our scope to Mutual Funds and its driver that is the equity markets. We provide an overview to the types of problems, preferred approaches, their benchmarks, deduced conclusions, and research gaps as a comprehensive survey for diverse readers.
The increasing complexity of financial markets has heightened the demand for advanced computational methods capable of optimizing portfolios under multi-dimensional constraints involving risk, return, diversification, and liquidity. Traditional optimization frameworks, such as mean-variance analysis and heuristic meta-optimization, are limited by their scalability and sensitivity to non-convex, high-dimensional data structures. This study explores the application of quantum algorithms to portfolio optimization, demonstrating how Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing, and Variational Quantum Eigensolver (VQE) can address computational bottlenecks inherent in classical approaches. Quantum computation offers the potential to encode complex financial systems as Hamiltonian functions, allowing parallel exploration of vast solution spaces through superposition and entanglement. This enables faster convergence toward optimal portfolio allocations while simultaneously accounting for multi-factor correlations and dynamic market conditions. The research further investigates the role of hybrid quantum–classical optimization pipelines, in which quantum solvers perform core optimization tasks while classical systems manage data preprocessing and constraint validation. Empirical modeling and simulation indicate that quantum-based approaches can outperform conventional methods in risk-adjusted return optimization, efficient frontier computation, and real-time portfolio rebalancing. By bridging the gap between quantum computing and financial engineering, this study contributes to the development of next-generation financial analytics systems, aligning with the broader goal of achieving more adaptive, transparent, and data-resilient financial decision-making.
Quantum computing offers a fundamentally new paradigm for solving complex optimisation and learning tasks by exploiting superposition, entanglement, and other quantum phenomena. Over the past decade, research has increasingly focused on quantum-enhanced approaches to optimisation and machine learning (ML), showing the potential to outperform classical methods in specific settings (Zaman, 2023; Peral-García, 2024). This paper presents a comprehensive review of quantum algorithms for optimisation (e.g., the Quantum Approximate Optimization Algorithm and quantum annealing) and quantum machine learning frameworks (such as quantum support vector machines and quantum neural networks), analysing their theoretical underpinnings, implementation status, and applicability to real-world tasks. Key challenges including hardware noise, limited qubit counts, and algorithmic scalability are examined (Chen, 2024). We also explore hybrid quantum-classical architectures as a near-term route to quantum advantage and propose future research directions aimed at bridging the current gap between quantum algorithm theory and large-scale deployment. Through this synthesis, we aim to provide both academic and practitioner audiences with a clear roadmap for leveraging quantum algorithms in optimisation and ML workflows
Quantum Machine Learning (QML) has become an important research area in recent years due to its potential to overcome the computational bottlenecks faced by classical machine learning methods. In this study, only SCI/SCI-Expanded journal articles published between 2023 and 2025 and accepted as early access for 2026 are considered, and 30 such articles are systematically examined to provide a comprehensive assessment of the current state of QML methods. The reviewed literature covers variational quantum circuits, quantum kernel methods, hybrid quantum–classical models, quantum annealing approaches, federated QML architectures, data encoding strategies, and adversarial robustness analyses. The findings indicate that QML models can exhibit stronger representational capacity and higher generalization performance compared to classical methods, particularly on high-dimensional, noisy, or small sample datasets. Reported results in domains such as healthcare, chemistry, energy systems, cybersecurity, blockchain, and finance reveal the diversity of QML applications and its interdisciplinary potential. Nevertheless, the limited qubit capacity of current NISQ hardware, constraints on circuit depth, and noise-related issues remain fundamental factors that restrict the scalability of QML models. This study synthesizes methodological trends, identifies open research problems, and proposes a directional roadmap for future QML research focused on hardware-aware circuit design, efficient data encoding schemes, and robust learning frameworks.
Portfolio optimization under realistic constraints represents a computationally intensive problem for classical financial models, particularly in volatile and high-dimensional markets such as India.Conventional mean-variance approaches become NP-hard when integer, cardinality, and regulatory constraints are incorporated.This paper investigates the applicability of quantum computing techniques for constrained portfolio optimization in the Indian financial context.The study analyzes quantum annealing and gate-based variational algorithms, including the Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE), with emphasis on Quadratic Unconstrained Binary Optimization (QUBO) formulations.In addition, quantum amplitude estimation methods are examined for accelerating financial risk metrics such as Value-at-Risk and Conditional Value-at-Risk.Empirical results derived from thematic and regression analyses indicate that perceived computational advantage and organizational readiness significantly influence adoption intent, while quantum-related security risks act as a moderating factor.The findings suggest that hybrid classical-quantum architectures provide a practical pathway during the Noisy Intermediate-Scale Quantum era.The paper concludes that early-stage quantum adoption, combined with post-quantum cryptographic preparedness, can enhance portfolio efficiency and risk governance for Indian institutional investors