Noise tailoring for Robust Amplitude Estimation
arXiv:2208.11797 · doi:10.1088/1367-2630/acb5bc
Abstract
A universal fault-tolerant quantum computer holds the promise to speed up computational problems that are otherwise intractable on classical computers; however, for the next decade or so, our access is restricted to noisy intermediate-scale quantum (NISQ) computers and, perhaps, early fault tolerant (EFT) quantum computers. This motivates the development of many near-term quantum algorithms including robust amplitude estimation (RAE), which is a quantum-enhanced algorithm for estimating expectation values. One obstacle to using RAE has been a paucity of ways of getting realistic error models incorporated into this algorithm. So far the impact of device noise on RAE is incorporated into one of its subroutines as an exponential decay model, which is unrealistic for NISQ devices and, maybe, for EFT devices; this hinders the performance of RAE. Rather than trying to explicitly model realistic noise effects, which may be infeasible, we circumvent this obstacle by tailoring device noise to generate an effective noise model, whose impact on RAE closely resembles that of the exponential decay model. Using noisy simulations, we show that our noise-tailored RAE algorithm is able to regain improvements in both bias and precision that are expected for RAE. Additionally, on IBM's quantum computer our algorithm demonstrates advantage over the standard estimation technique in reducing bias. Thus, our work extends the feasibility of RAE on NISQ computers, consequently bringing us one step closer towards achieving quantum advantage using these devices.
arXiv comment11 pages, 11 figures
References in corpus (37)
- A variational eigenvalue solver on a quantum processor
- Variational Quantum Algorithms
- Hardware-efficient Variational Quantum Eigensolver for Small Molecules and Quantum Magnets
- Quantum Amplitude Amplification and Estimation
- The Variational Quantum Eigensolver: a review of methods and best practices
- An adaptive variational algorithm for exact molecular simulations on a quantum computer
- Circuit-centric quantum classifiers
- Scalable Quantum Simulation of Molecular Energies
- The effect of data encoding on the expressive power of variational quantum machine learning models
- Hartree-Fock on a superconducting qubit quantum computer
- Towards Practical Quantum Variational Algorithms
- Quantum advantage in learning from experiments
- Noise tailoring for scalable quantum computation via randomized compiling
- Quantum generative adversarial networks
- A simple all-microwave entangling gate for fixed-frequency superconducting qubits
- Quantum chemistry calculations on a trapped-ion quantum simulator
- A generative modeling approach for benchmarking and training shallow quantum circuits
- Differentiable Learning of Quantum Circuit Born Machine
- Warm-starting quantum optimization
- Information-theoretic bounds on quantum advantage in machine learning
- Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
- Amplitude estimation without phase estimation
- Accelerated Variational Quantum Eigensolver
- Mitigating depolarizing noise on quantum computers with noise-estimation circuits
- Reachability Deficits in Quantum Approximate Optimization
- Hybrid quantum-classical algorithms for approximate graph coloring
- Tensor-Network Simulations of the Surface Code under Realistic Noise
- Minimizing estimation runtime on noisy quantum computers
- Low depth algorithms for quantum amplitude estimation
- Analyzing the Performance of Variational Quantum Factoring on a Superconducting Quantum Processor
- Noise-Assisted Quantum Autoencoder
- Scalable Method for Eliminating Residual Interaction between Superconducting Qubits
- Variational quantum amplitude estimation
- Amplitude estimation via maximum likelihood on noisy quantum computer
- Quantum algorithm for credit valuation adjustments
- Complexity of quantum state verification in the quantum linear systems problem
- Noisy quantum amplitude estimation without noise estimation