Quantum computer analyzing financial charts.

Quantum Leap for Finance: New Tech Slashes Derivative Pricing Costs

Soraya Malik in Tech & Innovation December 2025 • 4 min read.

"Researchers unveil a Quantum Signal Processing method that dramatically reduces the resources needed for pricing financial derivatives, paving the way for faster, cheaper, and more accessible quantum finance."

Financial derivatives are complex contracts whose value hinges on the future performance of underlying assets. Pricing these derivatives accurately is crucial for managing risk and making informed investment decisions. Traditionally, this has been a computationally intensive task, often relying on classical Monte Carlo methods, which can be slow and resource-heavy.

Quantum computing offers the potential to revolutionize financial modeling, providing speedups for complex calculations. However, early quantum algorithms for derivative pricing still faced significant hurdles, particularly in the quantum resources required. Quantum arithmetic, a core component of these algorithms, demanded substantial qubit counts and complex circuits, hindering their practicality for near-term quantum devices.

Now, a team of researchers from Goldman Sachs has introduced a game-changing approach that leverages Quantum Signal Processing (QSP) to dramatically reduce the quantum resources needed for derivative pricing. Their method, detailed in a recent paper, promises to accelerate the adoption of quantum computing in the financial industry, making it more accessible and cost-effective.

QSP: The Secret Weapon for Quantum Finance

Quantum computer analyzing financial charts.

The core innovation lies in using QSP to directly encode financial derivative payoffs into quantum amplitudes. This bypasses the need for costly quantum arithmetic, which has been a major bottleneck in previous quantum pricing algorithms. By cleverly manipulating quantum signals, the QSP method efficiently represents complex payoff functions, streamlining the entire pricing process.

Compared to existing state-of-the-art approaches, the QSP technique offers significant advantages across several key metrics, especially for derivative contracts of practical interest. The study highlights impressive reductions in:

Total number of T-gates: Reduced by approximately 16 times. T-gates are a measure of the complexity of quantum circuits.
Number of logical qubits: Cut down by roughly 4 times. Qubits are the fundamental units of quantum information.
Logical clock rate: Decreased by about 5 times, lowering the barrier to quantum advantage.

These improvements translate into a substantial decrease in the quantum resources required for achieving quantum advantage in derivative pricing. The researchers estimate that quantum advantage can be reached with approximately 4.7k logical qubits and quantum devices capable of executing 10^9 T-gates at a rate of 45MHz.

What Does This Mean for the Future of Finance?

This research marks a significant step forward in making quantum computing a practical tool for the financial industry. By dramatically reducing the resource requirements for derivative pricing, the QSP method brings quantum advantage closer to reality. While the current study focuses on the payoff component of derivative pricing, the researchers suggest that similar techniques could be applied to other areas, such as state preparation, further enhancing the efficiency of quantum financial algorithms. As quantum technology continues to evolve, expect to see more innovative applications of QSP and related techniques in finance and beyond.

About this Article -

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This article is based on research published under:

DOI-LINK: 10.22331/q-2024-04-30-1322,

Title: Derivative Pricing Using Quantum Signal Processing

Subject: quant-ph q-fin.cp

Authors: Nikitas Stamatopoulos, William J. Zeng

Published: 26-07-2023

Everything You Need To Know

What is Quantum Signal Processing (QSP) and how does it improve derivative pricing?

Quantum Signal Processing (QSP) is a new method that directly encodes financial derivative payoffs into quantum amplitudes. This innovative technique circumvents the need for complex quantum arithmetic, which was a significant bottleneck in earlier quantum derivative pricing algorithms. By manipulating quantum signals, QSP efficiently represents complex payoff functions, thus streamlining the pricing process and significantly reducing the quantum resources required, such as T-gates and logical qubits, leading to faster and more efficient calculations in financial modeling.

What are financial derivatives and why is accurate pricing so important?

Financial derivatives are complex financial contracts whose value is derived from the performance of underlying assets, like stocks or commodities. Accurate pricing of these derivatives is crucial for effective risk management and making informed investment decisions. Correctly valuing derivatives allows financial institutions to understand their exposure to market fluctuations, hedge against potential losses, and optimize their trading strategies. Inaccurate pricing can lead to significant financial risks.

How does the Quantum Signal Processing (QSP) method compare to older approaches in terms of resource usage?

The QSP method offers substantial improvements over existing approaches used in pricing financial derivatives. The study highlights significant reductions in several key metrics. Specifically, the total number of T-gates is reduced by approximately 16 times, the number of logical qubits is cut down by roughly 4 times, and the logical clock rate is decreased by about 5 times. These improvements collectively translate into a substantial decrease in the quantum resources needed to achieve a quantum advantage in derivative pricing, making it more practical for near-term quantum devices.

What are T-gates and logical qubits, and why are reductions in these important for quantum computing?

T-gates are a measure of the complexity of quantum circuits. A lower number of T-gates indicates a simpler and more efficient quantum circuit, making it easier to implement on a quantum computer. Logical qubits are the fundamental units of quantum information, similar to bits in classical computing. Reducing the number of logical qubits needed for a calculation makes it more feasible to run on existing quantum hardware, which typically has a limited number of qubits. Therefore, reducing the count of both T-gates and logical qubits makes quantum algorithms more practical and closer to achieving a quantum advantage.

What is the expected impact of Quantum Signal Processing (QSP) on the future of the financial industry?

The Quantum Signal Processing (QSP) method is poised to have a significant impact on the financial industry by making quantum computing a more practical tool. By dramatically reducing the resource requirements for derivative pricing, QSP accelerates the path to achieving quantum advantage. This advancement enables faster, cheaper, and more accessible quantum finance, potentially revolutionizing risk management, investment strategies, and the overall efficiency of financial modeling. Moreover, the researchers suggest that similar techniques could be extended to other areas within quantum finance, further amplifying the benefits and driving innovation in the sector.