In this paper, we first study the quantum jump formalism and its equivalence to the Lindblad master equation in describing the evolution of open quantum systems. Second, we evaluate the impact of logical Pauli and coherent Z-rotation errors on six quantum algorithms and their algorithmic resilience to these errors by performing the Monte Carlo simulations dictated by the quantum jump method. The results suggest that the resilience of the studied quantum algorithms decreases as the number of qubits and the depth of the algorithms' circuits increase for single Pauli and Z-rotation errors. Moreover, when two Pauli errors occur, the success of the algorithms becomes significantly unlikely. As per two Z-rotations errors, the success rate of the algorithms vanishes.
In this paper, we first study the quantum jump formalism and its equivalence to the Lindblad master equation in describing the evolution of open quantum systems. Second, we evaluate the impact of logical Pauli and coherent Z-rotation errors on six quantum algorithms and their algorithmic resilience to these errors by performing the Monte Carlo simulations dictated by the quantum jump method. The results suggest that the resilience of the studied quantum algorithms decreases as the number of qubits and the depth of the algorithms' circuits increase for single Pauli and Z-rotation errors. Moreover, when two Pauli errors occur, the success of the algorithms becomes significantly unlikely. As per two Z-rotations errors, the success rate of the algorithms vanishes.
Quantum error correction (QEC), the lynchpin of fault-tolerant quantum computing (FTQC), is designed and validated against well-behaved Pauli stochastic error models. But in real-world deployment, QEC protocols encounter a vast array of other errors -- coherent and non-Pauli errors -- whose impacts on quantum circuits are vastly different than those of stochastic Pauli errors. The impacts of these errors on QEC and FTQC protocols have been largely unpredictable to date due to exponential classical simulation cost. Here, we show how to accurately and efficiently model the effects of coherent and non-Pauli errors on FTQC, and we study the effects of such errors on syndrome extraction for surface and bivariate bicycle codes, and on magic state cultivation. Our analysis suggests that coherent error can shift fault-tolerance thresholds, increase the space-time cost of magic state cultivation, and can increase logical error rates by an order of magnitude compared to equivalent stochastic errors. These analyses are enabled by a new technique for mapping any Markovian circuit-level error model with sufficiently small error rates onto a detector error model (DEM) for an FTQC circuit. The resulting DEM enables Monte Carlo estimation of logical error rates and noise-adapted decoding, and its parameters can be analytically related to the underlying physical noise parameters to enable approximate strong simulation.
In this work, we explore the impact of logical stochastic Pauli and coherent Z-rotation errors on quantum algorithms. We evaluate six canonical quantum algorithms' intrinsic resilience to the logical qubit and gate errors by performing the Monte Carlo simulations guided by the quantum jump formalism. The results suggest that the resilience of the studied quantum algorithms decreases as the number of qubits and the depth of the algorithms' circuits increase for both Pauli and Z-rotation errors. Our results also suggest that the algorithms split into two different groups in terms of algorithmic resilience. The evolution of Hamiltonian, Simon and the quantum phase estimation algorithms are less resilient to logical errors than Grover's search, Deutsch-Jozsa and Bernstein-Vazirani algorithms.
Scaling up quantum algorithms to tackle high-impact problems in science and industry requires quantum error correction and fault tolerance. While progress has been made in experimentally realizing error-corrected primitives, the end-to-end execution of logical quantum algorithms using only fault-tolerant (FT) components has remained out of reach. We demonstrate the FT and error-corrected execution of two quantum algorithms, the Quantum Approximate Optimization Algorithm (QAOA) and the Harrow-Hassidim-Lloyd (HHL) algorithm applied to the Poisson equation, on Quantinuum H2 and Helios trapped-ion quantum processors using the [[7,1,3]] Steane code. For QAOA circuits on 5 and 6 logical qubits, we show performance improvements from increasing the number of QAOA layers and the number of T gates used to approximate logical rotations, despite increased physical circuit complexity. We further show that QAOA circuits with up to 8 logical qubits and 9 logical T gates perform similarly to unencoded circuits. For the largest QAOA circuits we run, with 12 logical (97 physical) qubits and 2132 physical two-qubit gates, we still observe better-than-random performance. Finally, we show that adding active QEC cycles and increasing the repeat-until-success limit of state preparation subroutines can improve the performance of a quantum algorithm, thereby demonstrating critical capabilities of scalable FT quantum computation. Our results are enabled by an FT logical T gate implementation with an infidelity of ∼2.6(4)×10−3 and dynamic circuits with measurement-dependent feedback. Our work demonstrates near-break-even performance of complex, error-corrected algorithmic quantum circuits using only FT components.
Analysis of quantum error correcting codes is typically done using a stochastic, Pauli channel error model for describing the noise on physical qubits. However, it was recently found that coherent errors (systematic rotations) on physical data qubits result in both physical and logical error rates that differ significantly from those predicted by a Pauli model. Here we examine the accuracy of the Pauli approximation for noise containing coherent errors (characterized by a rotation angle ) under the repetition code. We derive an analytic expression for the logical error channel as a function of arbitrary code distance d and concatenation level n , in the small error limit. We find that coherent physical errors result in logical errors that are partially coherent and therefore non-Pauli. However, the coherent part of the logical error is negligible at fewer than error correction cycles when the decoder is optimized for independent Pauli errors, thus providing a regime of validity for the Pauli approximation. Above this number of correction cycles, the persistent coherent logical error will cause logical failure more quickly than the Pauli model would predict, and this may need to be combated with coherent suppression methods at the physical level or larger codes.
Computing the ground-state properties of quantum many-body systems is a promising application of near-term quantum hardware with a potential impact in many fields. The conventional algorithm quantum phase estimation uses deep circuits and requires fault-tolerant technologies. Many quantum simulation algorithms developed recently work in an inexact and variational manner to exploit shallow circuits. In this work, we combine quantum Monte Carlo with quantum computing and propose an algorithm for simulating the imaginary-time evolution and solving the ground-state problem. By sampling the real-time evolution operator with a random evolution time according to a modified Cauchy-Lorentz distribution, we can compute the expected value of an observable in imaginary-time evolution. Our algorithm approaches the exact solution given a circuit depth increasing polylogarithmically with the desired accuracy. Compared with quantum phase estimation, the Trotter step number, i.e. the circuit depth, can be thousands of times smaller to achieve the same accuracy in the ground-state energy. We verify the resilience to Trotterisation errors caused by the finite circuit depth in the numerical simulation of various models. The results show that Monte Carlo quantum simulation is promising even without a fully fault-tolerant quantum computer.
One of the central challenges in quantum error correction is determining the performance of a code in the low-error regimes needed to implement utility-scale computations. While performance at these error rates is not amenable to direct Monte Carlo simulation, it can be extrapolated from simulations at higher logical error rates, assuming the logical error rate scales predictably with increasing distance or decreasing physical error rate. However, the expected scaling depends sensitively on the minimum weight of uncorrectable error patterns. In many cases, the minimum weight is unknown since it depends not only on the theoretical code distance, but also on details of the implementation. Markov chain Monte Carlo (MCMC) methods, as adapted to quantum error correction by Bravyi and Vargo, provide a way to estimate logical failure rates in these low-error regimes via simulation. While offering significant gains over Monte Carlo, the described Metropolis algorithm makes small changes to the current logical failure patterns which results in slow convergence. In this paper, we argue that typical failure patterns include a large number of easily correctable errors that coexist alongside a malignant core. This observation motivates two new approaches to better evaluate code performance. First, we describe a pruning algorithm designed to obviate these correctable errors and focus on the problematic low-weight core. Second, we develop a novel family of Metropolis-Hastings algorithms, referred to as subregion MCMC. This technique is parameterized by the fraction of the error pattern that is resampled at each step, effectively interpolating between Monte Carlo and single step MCMC. We show that a judicious choice of this parameter results in far faster convergence than prior work.