Error Resilient Quantum Amplitude Estimation from Parallel Quantum Phase Estimation
@inproceedings{Braun2022ErrorRQ, title={Error Resilient Quantum Amplitude Estimation from Parallel Quantum Phase Estimation}, author={Michael C. Braun and Thomas Decker and Niklas Hegemann and Sven Kerstan}, year={2022} }
We show how phase and amplitude estimation algorithms can be parallelized. This can reduce the gate depth of the quantum circuits to that of a single Grover operator with a small overhead. Further, we show that for quantum amplitude estimation, the parallelization can lead to vast improvements in resilience against quantum errors. The resilience is not caused by the lower gate depth, but by the structure of the algorithm. Even in cases with errors that make it impossible to read out the exact…
Figures from this paper
References
SHOWING 1-10 OF 26 REFERENCES
Iterative quantum amplitude estimation
- Computer Science
- 2019
We introduce a variant of Quantum Amplitude Estimation (QAE) , called Iterative QAE (IQAE), which does not rely on Quantum Phase Estimation (QPE) but is only based on Grover’s Algorithm , which…
Amplitude estimation without phase estimation
- Computer ScienceQuantum Inf. Process.
- 2020
This paper proposes a quantum amplitude estimation algorithm without the use of expensive controlled operations to utilize the maximum likelihood estimation based on the combined measurement data produced from quantum circuits with different numbers of amplitude amplification operations.
Amplitude estimation via maximum likelihood on noisy quantum computer
- Computer Science, PhysicsQuantum Inf. Process.
- 2021
This paper extends the maximum likelihood estimate with parallelized quantum circuits so that it incorporates the realistic noise effect, and shows that the proposed maximum likelihood estimator achieves quantum speedup in the number of queries, though the estimation error saturates due to the noise.
A Quantum Algorithm for the Sensitivity Analysis of Business Risks
- Computer Science
- 2021
This work presents a novel use case for quantum computation: the sensitivity analysis for a risk model used at Deutsche Börse Group and shows in detail how the risk model and its analysis can be implemented as a quantum circuit.
Quantum Monte-Carlo Integration: The Full Advantage in Minimal Circuit Depth
- Physics
- 2021
This paper proposes a method of quantum Monte-Carlo integration that retains the full quadratic quantum advantage, without requiring any arithmetic or the quantum Fourier transform to be performed on…
Quantum risk analysis
- Computer Sciencenpj Quantum Information
- 2019
A quantum algorithm that analyzes risk more efficiently than Monte Carlo simulations traditionally used on classical computers is presented and a near quadratic speed-up compared to Monte Carlo methods is provided.
A Threshold for Quantum Advantage in Derivative Pricing
- Computer ScienceQuantum
- 2021
An upper bound on the resources required for valuable quantum advantage in pricing derivatives is given, using autocallable and Target Accrual Redemption Forward (TARF) derivatives as benchmark use cases, and a new method for quantum derivative pricing – the re-parameterization method – is introduced.
Low depth algorithms for quantum amplitude estimation
- Computer ScienceArXiv
- 2020
Two new low depth algorithms for amplitude estimation (AE) achieving an optimal tradeoff between the quantum speedup and circuit depth are designed and analyzed and have provable correctness guarantees.
Option Pricing using Quantum Computers
- Physics, Computer ScienceQuantum
- 2020
We present a methodology to price options and portfolios of options on a gate-based quantum computer using amplitude estimation, an algorithm which provides a quadratic speedup compared to classical…
Quantum-enhanced analysis of discrete stochastic processes
- Computer Sciencenpj Quantum Information
- 2021
A quantum algorithm is proposed for calculating the characteristic function of a DSP, which completely defines its probability distribution, using the number of quantum circuit elements that grows only linearly with the number-steps, which guarantees the optimal variance without the need for importance sampling.