Loading Financial Data at a New Scale
 

Enabling Realistic Quantum Monte Carlo for Finance

Quantitative finance is fundamentally about allocating capital and redistributing risk in ways that support long-term economic health. By making uncertainty explicit and actionable, quantitative methods help firms allocate resources with confidence and contribute to more transparent, reliable markets.

A canonical example is the Black–Scholes equation, which provides a principled framework for pricing derivatives. By making the cost of risk explicit, derivative pricing improves market efficiency and enables firms to hedge uncertainty and invest over longer horizons. In practice, however, many of today’s most important financial problems extend beyond closed-form solutions and rely on computationally intensive techniques such as Monte Carlo simulation. As models become more realistic, higher-dimensional, and sensitive to tail risk, these methods become increasingly slow and expensive to simulate on classical hardware.

Quantum computing has emerged as a promising way to accelerate financial workloads by offering fundamentally different scaling behavior than classical approaches. Beyond derivative pricing, applications such as portfolio optimization, fraud detection, and machine learning are all to benefit from quantum computation. These applications share a common and often overlooked requirement: realistic financial distributions must first be loaded into a quantum computer to govern the modelled instrument.

Distribution loading is extremely challenging. The number of required quantum operations in conventional algorithms can scale exponentially with the number of qubits, making it a significant bottleneck on today’s noisy, depth-limited hardware.

Haiqu addresses this challenge by exploiting structure and smoothness in distributions to factor the loading process into compact quantum circuits with linear (rather than exponential) scaling. Using this approach, Haiqu demonstrated the largest-scale loading of realistic financial distributions on quantum hardware by successfully encoding heavy-tailed distributions on up to 64 qubits on IBM’s Torino processor and validating the results with standard statistical tests on up to 25 qubits. Following the initial project, Haiqu demonstrated the applicability of this method at a scale of up to 156 qubits. 

Combined with Haiqu’s optimized execution tools, this capability enables, for the first time, the execution of Quantum Monte Carlo routines on realistic fat-tailed financial distributions directly on quantum hardware. It also unlocks practical exploration of quantum machine learning applications, such as fraud detection, by enabling high-dimensional feature encoding with only a few dozen qubits.

For business decision makers, the implications are immediate. Haiqu lowers the cost and increases the performance of financial quantum workloads, transforming quantum computing from a long-term research bet into a near-term commercial piloting opportunity. By enabling realistic distribution loading on today’s devices, Haiqu allows financial teams to test larger models, integrate quantum methods into existing workflows, and build expertise ahead of broader hardware advances.

Ultimately, this progress reinforces the core promise of quantitative finance: using better models and better computation to manage risk more effectively, allocate capital more wisely, and support a more stable and transparent financial system.

Quantum for business. Run more with Haiqu.

Anomaly Detection with High-Dimensional Quantum Embeddings

Financial institutions are increasingly exploring quantum computing as a lever to improve risk modelling, simulation, and data-driven decision-making. Yet a fundamental obstacle remains largely underappreciated: before any quantum algorithm can deliver value, classical financial data must be transformed into quantum states, a process that is both resource-intensive and highly sensitive to hardware noise on today’s devices. Overcoming this data-loading and processing bottleneck is essential to move quantum finance from theoretical promise to executable pilots on real hardware.

Haiqu is partnering with Oxford Ionics, an IonQ company, and HSBC to explore how quantum computing can address real-world data-intensive challenges in financial services. The focus will be on detecting outliers and acceleration patterns in complex, high-volume trade and payment data. The collaboration brings together Haiqu’s quantum software expertise, Oxford Ionics’ advanced quantum hardware capabilities, and HSBC’s deep industry knowledge.

Additional details on the scope and outcomes of this collaboration will be shared soon. In the meantime, learn more about quantum machine learning for anomaly detection in our blog, or get in touch to discuss how these approaches can be applied in practice.

Quantum for business. Run more with Haiqu.
 

Quantum software startup Haiqu announced results from a trial this week demonstrating that current quantum computers could detect subtle financial anomalies that could indicate fraud more efficiently than purely classical systems.

The research, which used a hybrid computing approach pairing quantum processing power with traditional machine learning models, revealed performance gains that suggest a near-term path toward achieving "quantum advantage" for large-scale, real-world problems.

Read full article here.

A team of Haiqu-led researchers have developed a new approach to encoding complex financial data into quantum circuits, pushing the limits of IBM’s quantum processors, according to a paper published on the pre-print server arXiv. The team reports that the method, which focuses on shallow and efficient circuit designs, could advance the use of quantum computing in finance and other industries.

In a recent LinkedIn post on the paper, the team writes: “Quantum computing cannot achieve wide utility in the near term until we can efficiently load classical data onto quantum hardware. Now, we can.”

Read full article here.

Sifted asked investors in the field who they think is the next big thing in quantum computing.

European quantum startups are on the rise. Last year, while many VC-backed companies in the region were struggling to raise funds, Europe’s quantum startups actually saw investments grow by 3% to reach $781m — more than three times the amount raised in the sector in North America ($240m).

It also made Europe the only region to see funding for quantum startups increase, while investments in North America dropped by 80%, and by 17% in Asia-Pacific.

With strong support from governments — the UK has committed $4.3bn to quantum technologies, while Germany has pledged over $3.7bn — and burgeoning interest from VCs, Europe’s quantum scene is growing steadily. 

Read full article here.