Quantum computing for finance

Abstract

Quantum computers are expected to surpass the computational capabilities of classical computers and have a transformative impact on numerous industry sectors. We present a comprehensive summary of the state of the art of quantum computing for financial applications, with particular emphasis on stochastic modeling, optimization, and machine learning. This Review is aimed at physicists, so it outlines the classical techniques used by the financial industry and discusses the potential advantages and limitations of quantum techniques. Finally, we look at the challenges that physicists could help tackle.

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Cited by in corpus (45)

• Alignment between Initial State and Mixer Improves QAOA Performance for Constrained Optimization
• Constrained Optimization via Quantum Zeno Dynamics
• Efficient quantum amplitude encoding of polynomial functions
• Certified randomness using a trapped-ion quantum processor
• Demonstration of low-overhead quantum error correction codes
• Polylogarithmic-depth controlled-NOT gates without ancilla qubits
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• Hybrid Quantum-Classical Heuristic for the Bin Packing Problem
• What can we learn from quantum convolutional neural networks?
• Blind quantum machine learning with quantum bipartite correlator
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• Quantum state preparation for multivariate functions
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