We present a quantum algorithm to solve systems of linear equations of the form $A\mathbf{x}=\mathbf{b}$, where $A$ is a tridiagonal Toeplitz matrix and $\mathbf{b}$ results from discretizing an analytic function, with a circuit complexity of $poly(\log(κ), 1/\sqrtε, \log(N))$, where $N$ denotes the number of equations, $ε$ is the accuracy, and $κ$ the condition number. The \emph{repeat-until-success} algorithm has to be run $\mathcal{O}\left(κ/(1-ε)\right)$ times to succeed, leveraging amplitude amplification, and sampled $\mathcal{O}(1/ε^2)$ times. Thus, the algorithm achieves an exponential improvement with respect to $N$ over classical methods. In particular, we present efficient oracles for state preparation, Hamiltonian simulation and a set of observables together with the corresponding error and complexity analyses. As the main result of this work, we show how to use Richardson extrapolation to enhance Hamiltonian simulation, resulting in an implementation of Quantum Phase Estimation (QPE) within the algorithm with $1/\sqrtε$ circuit complexity instead of $1/ε$ and which can be parallelized. Furthermore, we analyze necessary conditions for the overall algorithm to achieve an exponential speedup compared to classical methods. Our approach is not limited to the considered setting and can be applied to more general problems where Hamiltonian simulation is approximated via product formulae, although, our theoretical results would need to be extended accordingly. All the procedures presented are implemented with Qiskit and tested for small systems using classical simulation as well as using real quantum devices available through the IBM Quantum Experience.
Quantum error mitigation is a key concept for the development of practical applications based on current noisy intermediate scale quantum (NISQ) devices. One of the most promising methods is Richardson extrapolation to the zero noise limit. While its main idea is rather simple, the full potential of Richardson extrapolation has not been completely uncovered yet. We give an in-depth analysis of the relevant parameters of Richardson extrapolation and propose an optimized protocol for its implementation. This protocol allows for a precise control of the increase in statistical uncertainty and lay…
Distilling precise estimates from noisy intermediate scale quantum (NISQ) data has recently attracted considerable attention. In order to augment digital qubit metrics, such as gate fidelity, we discuss analog error mitigability, i.e. the ability to accurately distill precise observable estimates, as a hybrid quantum-classical computing benchmarking task. Specifically, we characterize single qubit error rates on IBM's Poughkeepsie superconducting quantum hardware, incorporate control-mediated noise dependence into a generalized rescaling protocol, and analyze how noise characteristics influenc…
Joint Mitigation of Algorithmic and Physical Errors in Noisy Hamiltonian Simulation
Center on Frontiers of Computing Studies, Peking University（北京大学前沿计算研究中心）；School of Computer Science, Peking University（北京大学计算机学院）；Tencent Quantum Laboratory, Tencent（腾讯量子实验室）；Department of Mathematical Sciences, Tsinghua University（清华大学数学科学系）；Yau Mathematical Sciences Center, Tsinghua University（清华大学丘成桐数学科学中心）；RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS)（理化学研究所跨学科理论数学科学中心）；RIKEN Center for Computational Science (R-CCS)（理化学研究所计算科学中心）
Center on Frontiers of Computing Studies, Peking University（前沿计算研究中心，北京大学）；School of Computer Science, Peking University（计算机学院，北京大学）；RIKEN Center for Interdisciplinary Theoretical and Mathematical Sciences (iTHEMS)（理化学研究所跨学科理论与数学科学中心（iTHEMS））；RIKEN Center for Computational Science (R-CCS)（理化学研究所计算科学中心（R-CCS））；Tencent Quantum Laboratory, Tencent（腾讯量子实验室，腾讯）
Various methods have been developed for the quantum computation of the ground and excited states of physical and chemical systems, but many of them require either large numbers of ancilla qubits or high-dimensional optimization. The quantum imaginary-time evolution (QITE) and quantum Lanczos (QLanczos) methods proposed in [1] eschew the aforementioned issues. In this study, we demonstrate the practical application of these algorithms to nontrivial quantum computation, using the deuteron binding energy and molecular Hydrogen binding and excited state energies as examples. With the correct choic…
Hybrid quantum-classical algorithms have been proposed to circumvent noise limitations in quantum computers. Such algorithms delegate only a calculation of the expectation value to the quantum computer. Among them, the Variational Quantum Eigensolver (VQE) has been implemented to study molecules and condensed matter systems on small size quantum computers. Condensed matter systems described by the Hubbard model exhibit a rich phase diagram alongside exotic states of matter. In this manuscript, we try to answer the question: how much of the underlying physics of a 1D Hubbard chain is described …
We report results for simulating an effective field theory to compute the binding energy of the deuteron nucleus using a hybrid algorithm on a trapped-ion quantum computer. Two increasingly complex unitary coupled-cluster ansaetze have been used to compute the binding energy to within a few percent for successively more complex Hamiltonians. By increasing the complexity of the Hamiltonian, allowing more terms in the effective field theory expansion and calculating their expectation values, we present a benchmark for quantum computers based on their ability to scalably calculate the effective f…
Improved resource-tunable near-term quantum algorithms for transition probabilities, with applications in physics and variational quantum linear algebra
