In recent years, fintech has expanded rapidly, driven by advances in artificial intelligence, big data, and cloud computing. However, in highly complex scenarios such as asset allocation, credit risk assessment, and derivatives pricing, the computational capacity of traditional systems is becoming increasingly constrained. Quantum computing, with its high degree of parallelism and powerful simulation capabilities, has the potential to overcome these computational bottlenecks while also addressing emerging cybersecurity threats such as “harvest now, decrypt later” (HNDL) attacks. In 2024, the National Institute of Standards and Technology (NIST) released three Federal Information Processing Standards (FIPS 203, 204, and 205) for post‑quantum cryptography (PQC), accelerating global efforts to deploy quantum‑resistant cybersecurity frameworks. Major technology companies, including Google, IBM, and Amazon Web Services (AWS), are likewise actively advancing initiatives in quantum-safe security and quantum‑enabled financial solutions.
Forward-Looking Applications for Financial Innovation and
Cybersecurity Upgrading: Quantum Technologies as a Key Enabler
The rapid growth of data volumes and escalating cybersecurity risks have elevated quantum technologies, particularly their capabilities in high-speed simulation, optimization, and security enhancement, to a core driver of the next wave of financial technology. Quantum computing is especially well suited to NP‑hard combinatorial problems such as asset allocation and portfolio optimization. The Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing enable more efficient approximation of global optima within large parameter spaces, thereby enhancing the performance of fund management, algorithmic trading, and large‑scale portfolio management.
Quantum simulation also has the potential to enhance market risk assessment. By leveraging quantum random walks and advanced simulation techniques, quantum approaches can more accurately approximate non‑linear market dynamics and extreme events. This capability enables higher‑precision stress testing and supports financial institutions and regulators in identifying, monitoring, and managing key systemic financial risks.
In the domain of derivatives pricing, quantum Fourier transform (QFT) techniques have the potential to accelerate Monte Carlo simulations, particularly for instruments such as options and credit default swaps (CDS) that require extensive path sampling and multi‑factor analysis. This can improve pricing accuracy and strengthen risk-hedging capabilities.
Quantum machine learning integrates approaches such as quantum support vector machines (QSVM) and variational quantum classifiers (VQC) to enhance market forecasting, fraud detection, personalized recommendation systems, and customer behavior analysis, thereby enabling more intelligent and data‑driven financial services.
From a cybersecurity perspective, quantum computing poses long-term risks to existing cryptographic systems, making post‑quantum cryptography (PQC) a central focus of ongoing research and standardization efforts. Quantum key distribution (QKD) and high‑quality random number generation (RNG) are also expected to become foundational components of next-generation security architectures. These technologies are applicable to cross‑border payments, central bank digital currencies (CBDCs), digital asset protection, and cloud security, thereby strengthening the overall resilience of financial systems.
International Case Studies in Financial Applications of Quantum Technologies:
Goldman Sachs and HSBC
Quantum technologies have entered an empirical phase of application within the financial sector. Goldman Sachs, in collaboration with IBM, has applied quantum algorithms to enhance the pricing of interest rate derivatives. By leveraging the quadratic speedup offered by quantum Monte Carlo methods, this collaboration has significantly reduced computational time and associated costs. The proposed re‑parameterization approach, combined with pre‑training and quantum error correction techniques, provides a practical foundation for the future deployment of quantum financial algorithms.
HSBC has partnered with Quantinuum across three key domains: cybersecurity, fraud detection, and natural language processing. In cybersecurity, Quantinuum’s Quantum Origin platform generates cryptographic keys using quantum computers, enhancing the security of financial transactions and identity verification. In the area of fraud detection, the TKET platform improves quantum computational efficiency, supporting the development of more effective detection models. The collaboration also explores quantum natural language processing (QNLP), employing interpretable models for semantic analysis in highly regulated financial environments, including applications such as text analytics and question-answering services.
International Case Studies in Cybersecurity Applications of Quantum Technologies: Google, IBM, and SandboxAQ
Following its initial testing of the CECPQ1 hybrid key exchange protocol in Chrome in 2016, Google introduced post‑quantum cryptography (PQC) into its internal communications in 2022. In 2024, Google further deployed PQC signature algorithms—ML‑DSA‑65 (FIPS 204) and SLH-DSA‑ SHA2‑ 128S (FIPS 205)—within Cloud Key Management Service (Cloud KMS), while open‑sourcing cryptographic libraries such as BoringSSL, BoringCrypto, and Tink to accelerate standardization and adoption.
As a co‑developer of CRYSTALS‑Kyber and CRYSTALS‑Dilithium, IBM was among the first to deploy post‑quantum cryptography (PQC) on its z16 mainframe, enabling quantum‑safe TLS and digital signature capabilities. IBM has also introduced Cryptography Bills of Materials (Crypto‑BOMs, CBOMs) to help enterprises inventory and manage cryptographic assets, and now offers PQC migration and proof‑of‑concept services through IBM Cloud and IBM Consulting.
SandboxAQ has played a significant role in the development of post‑quantum cryptography standards led by National Institute of Standards and Technology (NIST). Its HQC (Hamming Quasi‑Cyclic) algorithm has been advanced as a candidate lightweight cryptographic solution. Through the SandboxAQ Security Suite, the company provides capabilities including automated cryptographic scanning, algorithm management, and policy recommendations. These services are delivered to financial institutions, government agencies, and defense organizations, in collaboration with partners such as Google Cloud and Deloitte.
Conclusion: Opportunities and Policy Recommendations for Taiwan’s Quantum Technology Development
Taiwan possesses strong capabilities in IC design and semiconductor manufacturing; however, quantum computing hardware development remains at an early stage. While fintech in Taiwan has primarily focused on blockchain, artificial intelligence, and big data, dedicated platforms for quantum finance research are still lacking.
Similarly, the PQC ecosystem is still nascent, and the supporting education and talent pipelines have yet to fully develop.
In the financial sector, it is recommended to promote cross‑disciplinary talent development through the establishment of quantum financial technology programmes that integrate expertise in physics, mathematics, computer science, and finance. Cloud‑based platforms such as the IBM Quantum Platform and D‑Wave Systems’s Leap quantum cloud service could be incorporated as practical training environments. In the area of cybersecurity, strengthened international collaboration is advised, including the adoption of quantum technology solutions from companies such as Multiverse Computing, Quantinuum, and Xanadu, to support local testing, pilot deployment, and application development.
Quantum technologies are poised to redefine the computational logic and competitive landscape of finance and cybersecurity. By leveraging its existing technological strengths and proactively advancing quantum finance and quantum cybersecurity, Taiwan has the opportunity to secure a strategically significant position within the global fintech and cybersecurity ecosystem.
