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Pricing Lookback Options on a Quantum Computer

Florence Paquette, Tania Belabbas, Emmanuel Hamel, Anne MacKay

Abstract

We develop a quantum algorithm to price discretely monitored lookback options in the Black-Scholes framework using imaginary time evolution. By rewriting the pricing PDE as a Schrodinger-type equation, the problem becomes the imaginary time evolution of a quantum state under a non-Hermitian Hamiltonian. This evolution is approximated with the Variational Quantum imaginary time evolution (VarQITE) method, which replaces the exact non-unitary dynamics with a parameterized, hardware-efficient quantum circuit. A central challenge arises from jump conditions caused by the discrete updating of the running maximum. This feature is not present in standard quantum treatments of European or Asian options. To address this, we propose two quantum-compatible formulations: (i) a sequential approach that models jumps via dedicated jump Hamiltonians applied at monitoring dates, and (ii) a simultaneous multi-function evolution that removes explicit jumps at the expense of an increased number of dimensions. We compare both approaches in terms of qubit resources, circuit complexity and numerical accuracy, and benchmark them against Monte Carlo simulations. Our results show that discretely monitored, path-dependent options with jump conditions can be handled within a variational quantum framework, paving the way toward the quantum pricing of more complex derivatives with non-smooth dynamics.

Pricing Lookback Options on a Quantum Computer

Abstract

We develop a quantum algorithm to price discretely monitored lookback options in the Black-Scholes framework using imaginary time evolution. By rewriting the pricing PDE as a Schrodinger-type equation, the problem becomes the imaginary time evolution of a quantum state under a non-Hermitian Hamiltonian. This evolution is approximated with the Variational Quantum imaginary time evolution (VarQITE) method, which replaces the exact non-unitary dynamics with a parameterized, hardware-efficient quantum circuit. A central challenge arises from jump conditions caused by the discrete updating of the running maximum. This feature is not present in standard quantum treatments of European or Asian options. To address this, we propose two quantum-compatible formulations: (i) a sequential approach that models jumps via dedicated jump Hamiltonians applied at monitoring dates, and (ii) a simultaneous multi-function evolution that removes explicit jumps at the expense of an increased number of dimensions. We compare both approaches in terms of qubit resources, circuit complexity and numerical accuracy, and benchmark them against Monte Carlo simulations. Our results show that discretely monitored, path-dependent options with jump conditions can be handled within a variational quantum framework, paving the way toward the quantum pricing of more complex derivatives with non-smooth dynamics.

Paper Structure

This paper contains 25 sections, 40 equations, 5 figures, 1 table, 4 algorithms.

Figures (5)

  • Figure 1: Algorithm \ref{['alg:sequential_evolution']} compared to MC for $T=2$ years using 4 qubits (left) and 8 qubits (right). A clear difference in the first plot indicates discretization error; finer discretization improves agreement with the benchmark.
  • Figure 2: Algorithm \ref{['alg:sequential_evolution']} compared to MC for $T=4$ years using 4 qubits (left) and 8 qubits (right). Error seems to accumulate through the years.
  • Figure 3: Algorithm \ref{['alg:effective_hamiltonians']} compared to MC for $T=2$ years using 4 qubits (left) and 8 qubits (right). A clear difference in the first plot indicates discretization error; finer discretization improves agreement with the benchmark.
  • Figure 4: Algorithm \ref{['alg:effective_hamiltonians']} compared to MC for $T=4$ years using 4 qubits (left) and 8 qubits (right).
  • Figure 5: Algorithm \ref{['alg:effective_hamiltonians']} compared to MC for $T=4$ years using VarQITE to implement Variational imaginary time evolution.

Theorems & Definitions (1)

  • Remark 2.1