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A Modular Engine for Quantum Monte Carlo Integration
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We present the Quantum Monte Carlo Integration (QMCI) engine developed by Quantinuum. It is a quantum computational tool for evaluating multi-dimensional integrals that arise in various fields of science and engineering such as finance. This white paper presents a detailed description of the architecture of the QMCI engine, including a variety of distribution-loading methods, a novel quantum amplitude estimation method that improves the statistical robustness of QMCI calculations, and a library of statistical quantities that can be estimated. The QMCI engine is designed with modularity in mind, allowing for the continuous development of new quantum algorithms tailored in particular to financial applications. Additionally, the engine features a resource mode, which provides a precise resource quantification for the quantum circuits generated. The paper also includes extensive benchmarks that showcase the engine's performance, with a focus on the evaluation of various financial instruments.
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Cited by 3 Pith papers
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Unlocking Multidimensional Integration with Quantum Adaptive Importance Sampling
QAIS uses a parameterized quantum circuit to allocate Monte Carlo samples along a learned non-separable density and achieves VEGAS-competitive or better accuracy on correlated integrands in simulation.
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A simple algorithm to reflect through eigenspaces of unitaries
A quantum reflection through an eigenspace of a unitary can be implemented with one ancilla qubit and O(1/delta * log(1/epsilon)) controlled gates.
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Application of an upsampling algorithm to quantum state preparation of continuous and discrete probability distributions
An upsampling-based divide-and-conquer circuit for quantum state preparation of probability distributions is derived, but the discrete-distribution extension is not correctly normalized and the method is not novel.
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