arXiv subjects

Jingbo B Wang

Publications and source records attributed to Jingbo B Wang.

3 recordsLinked to original sources

arXiv2026

Quantum State Preparation via Schmidt Spectrum Optimisation

Quantum state preparation represents a critical bottleneck for a broad class of quantum algorithms. In this work, we introduce the Schmidt Spectrum Optimisation (SSO) algorithm as an efficient and scalable approach for preparing quantum states described by Matrix Product States (MPS). The SSO algorithm employs a preparation-by-disentangling strategy by optimising circuit layers of two-qubit gates to progressively remove entanglement from a target state. Each circuit layer is computed sequentially and efficiently on a classical computer using tensor network optimisation techniques. Once the target state has been successfully disentangled, a quantum state-preparation circuit is formed by reversing the sequence of optimised disentangling layers. Across benchmarks including random MPS and MPS approximations to the ground-states of local Hamiltonians, we find that the SSO algorithm significantly improves upon prior variational and disentangling-based approaches, highlighting its potential as a scalable framework for quantum state preparation.

quant-ph↗

arXiv2025

Quantum Encoding of Structured Data with Matrix Product States

The amplitude encoding of an arbitrary $n$-qubit state vector requires $Ω(2^n)$ gate operations, owing to the exponential dimension of the Hilbert space. We can, however, form dimensionality-reduced representations of quantum states using matrix product states (MPS). In this article, we illustrate that MPS techniques enable the preparation of quantum states representative of functions with complexity up to low-degree piecewise polynomials via shallow-depth quantum circuits with accuracy exceeding 99.99\%. We extend these results to the approximate amplitude encoding of pixel values. We showcase this approach by efficiently preparing a $128\times 128$ ChestMNIST medical image (https://medmnist.com/) on 14 qubits with fidelity exceeding 99.2\% on a circuit with a total depth of just 425 single-qubit rotation and CNOT gates.

quant-ph↗

arXiv2025

Monte-Carlo Option Pricing in Quantum Parallel

Financial derivative pricing is a significant challenge in finance, involving the valuation of instruments like options based on underlying assets. While some cases have simple solutions, many require complex classical computational methods like Monte Carlo simulations and numerical techniques. However, as derivative complexities increase, these methods face limitations in computational power. Cases involving Non-Vanilla Basket pricing, American Options, and derivative portfolio risk analysis need extensive computations in higher-dimensional spaces, posing challenges for classical computers. Quantum computing presents a promising avenue by harnessing quantum superposition and entanglement, allowing the handling of high-dimensional spaces effectively. In this paper, we introduce a self-contained and all-encompassing quantum algorithm that operates without reliance on oracles or presumptions. More specifically, we develop an effective stochastic method for simulating exponentially many potential asset paths in quantum parallel, leading to a highly accurate final distribution of stock prices. Furthermore, we demonstrate how this algorithm can be extended to price more complex options and analyze risk within derivative portfolios.

q-fin.CP↗