Quantum computers are able to efficiently solve certain problems that are very computationally demanding or even intractable on classical computers. With recent advances in quantum computing hardware, we are entering an exciting era where it is becoming possible to execute ‘useful’ quantum algorithms. However, quantum computers will continue to be noisy for the foreseeable future. In this paper, quantum adder circuits are used to evaluate three simple error mitigation methods: (1) error correction through the use of replicated circuits and a voter circuit, (2) error detection by encoding, which involves expanding the qubit space and partitioning it into orthogonal subspaces consisting of correct and incorrect code words, and (3) error mitigation by reducing circuit depth at the expense of increasing the number of qubits. These three mitigation methods are what we dub replication, encoding, and simplification, respectively. We find simplification to be most effective for implementing the simple quantum adders on the IBM Quantum Experience, while replication increases the gate count and the error count on current quantum devices. The encoding methods used in this study proved to be not effective for even the simple adder circuits that were implemented, suggesting the need to study and evaluate more sophisticated error correcting schemes.
Quantum computers are able to efficiently solve certain problems that are very computationally demanding or even intractable on classical computers. With recent advances in quantum computing hardware, we are entering an exciting era where it is becoming possible to execute ‘useful’ quantum algorithms. However, quantum computers will continue to be noisy for the foreseeable future. In this paper, quantum adder circuits are used to evaluate three simple error mitigation methods: (1) error correction through the use of replicated circuits and a voter circuit, (2) error detection by encoding, which involves expanding the qubit space and partitioning it into orthogonal subspaces consisting of correct and incorrect code words, and (3) error mitigation by reducing circuit depth at the expense of increasing the number of qubits. These three mitigation methods are what we dub replication, encoding, and simplification, respectively. We find simplification to be most effective for implementing the simple quantum adders on the IBM Quantum Experience, while replication increases the gate count and the error count on current quantum devices. The encoding methods used in this study proved to be not effective for even the simple adder circuits that were implemented, suggesting the need to study and evaluate more sophisticated error correcting schemes.
Near-term quantum computers have been built as intermediate-scale quantum devices and are fragile against quantum noise effects, namely, NISQ devices. Traditional quantum-error-correcting codes are not implemented on such devices and to perform quantum computation in good accuracy with these machines we need to develop alternative approaches for mitigating quantum computational errors. In this work, we propose quantum error mitigation (QEM) scheme for quantum computational errors which occur due to couplings with environments during gate operations, i.e., decoherence. To establish our QEM scheme, first we estimate the quantum noise effects on single-qubit states and represent them as groups of quantum circuits, namely, quantum-noise-effect circuit groups. Then our QEM scheme is conducted by subtracting expectation values generated by the quantum-noise-effect circuit groups from those obtained by the quantum circuits for the quantum algorithms under consideration. As a result, the quantum noise effects are reduced, and we obtain approximately the ideal expectation values via the quantum-noise-effect circuit groups and the numbers of elementary quantum circuits composing them scale polynomial with respect to the products of the depths of quantum algorithms and the numbers of register bits. To numerically demonstrate the validity of our QEM scheme, we run noisy quantum simulations of qubits under amplitude damping effects for four types of quantum algorithms. Furthermore, we implement our QEM scheme on IBM Q Experience processors and examine its efficacy. Consequently, the validity of our scheme is verified via both the quantum simulations and the quantum computations on the real quantum devices. Our QEM scheme is solely composed of quantum-computational operations (quantum gates and measurements), and thus, it can be conducted by any type of quantum device. In addition, it can be applied to error mitigation for many other types of quantum noise effects as well as noisy quantum computing of long-depth quantum algorithms.
We explore the field of quantum computing and specifically address the crucial problem of error reduction in the context of IBM simulators. Although quantum computing is a rapidly developing technology with great potential, its practical applications are severely hindered by its inherent error-proneness. We tackle this problem by introducing new developments in quantum error correction techniques designed for IBM simulators. In contrast to conventional methods that frequently depend on large matrices or pre-established error models; our technique presents a flexible and dynamic approach. By utilizing several variations of a main circuit, in which classically efficient gates are used in place of non-Clifford gates, we are able to discover the best compensation scheme from scratch for actual quantum systems. The suggested method exhibits great flexibility in handling different levels of noise and different kinds of noise, such as spatially and temporally correlated variations. It also effectively reduces errors on IBM quantum hardware as well as precisely emulated imperfect quantum computers. By offering a thorough investigation of sophisticated error mitigation techniques, this research advances the continuing quest for dependable quantum computations and opens up interesting new directions for improving both the accuracy and performance of quantum computing operations carried out on IBM simulators.
Current era quantum computers continue to grow in both capability and capacity. Despite these advancements, errors induced by environmental noise severely limit practical applicability. Current research into error mitigation and correction to bridge the gap between current-era quantum computers and the execution of noise-sensitive workloads. These methods have significant performance and resource overheads, thereby greatly limiting the real-world benefits of their use. Circuit replication, as a naive form of error mitigation, is not new and has largely been ignored given the resource constraints of current quantum hardware. However, its simplicity is attractive as a means to supplement modern methods, reducing the overall performance overhead while still preserving error-mitigation capabilities. In this paper, we profile the effects of simple circuit replication under real-world noise profiles to better establish replication's limits as a supplemental mitigation strategy.
Quantum Approximate Optimization Algorithm (QAOA) for the Maxcut problem is explored for the analysis. For small graphs, we found that the average inference strength decreases by approximately 21.8% while the average standard deviation decreases by 108.8% compared to 6 replicates. For larger graphs, inference strength decreases by 35.4% while the average standard deviation decreased only 20.5%. Fewer replications did not affect smaller graphs, but degraded inference strength, with comparable benefits to standard deviation in larger graphs. These results show that replication has potential uses as a supplemental mitigation strategy for large-depth, highly variable workloads.
The "Noisy intermediate-scale quantum" NISQ machine era primarily focuses on mitigating noise, controlling errors, and executing high-fidelity operations, hence requiring shallow circuit depth and noise robustness. Approximate computing is a novel computing paradigm that produces imprecise results by relaxing the need for fully precise output for error-tolerant applications including multimedia, data mining, and image processing. We investigate how approximate computing can improve the noise resilience of quantum adder circuits in NISQ quantum computing. We propose five designs of approximate quantum adders to reduce depth while making them noise-resilient, in which three designs are with carryout, while two are without carryout. We have used novel design approaches that include approximating the Sum only from the inputs (pass-through designs) and having zero depth, as they need no quantum gates. The second design style uses a single CNOT gate to approximate the SUM with a constant depth of O(1). We performed our experimentation on IBM Qiskit on noise models including thermal, depolarizing, amplitude damping, phase damping, and bitflip: (i) Compared to exact quantum ripple carry adder without carryout the proposed approximate adders without carryout have improved fidelity ranging from 8.34% to 219.22%, and (ii) Compared to exact quantum ripple carry adder with carryout the proposed approximate adders with carryout have improved fidelity ranging from 8.23% to 371%. Further, the proposed approximate quantum adders are evaluated in terms of various error metrics.
Abstract Quantum computing has recently emerged as a novel paradigm for solving problems otherwise intractable on classical computers, yet the practical progress of quantum computers is severely impacted by noise, decoherence, and imperfect gate operations in Noisy Intermediate-Scale Quantum (NISQ) devices. Specific to IBM Quantum hardware, imperfections due to readout error, gate errors, and device drift, which depends on time, resolves limits for the accuracy of outputs from our algorithms. The mitigation strategies currently used, for example, Measurement Error Mitigation (MEM), Zero Noise Extrapolation (ZNE), and Clifford Data Regression (CDR), offer improvement over raw, unmitigated outputs, but they are static and not dynamic; the reduction of noise in within an NISQ device is temporal and can vary in patterns that are non-zero. In addition to this, using the conventional methods provides inconsistent and suboptimal results.This study will provide enhanced reliability with respect to performing quantum computation on IBM Quantum hardware with a unique mitigation framework that is adaptive and computationally efficient for a high-fidelity output. The problem taken on here is the failure of existing error mitigation strategies to provide adaptive noise compensation, as they assume a statically-distributed error, despite the performance-related characteristics already being documented in regards to calibration drift and variation over time.To achieve this, we propose a Hybrid Adaptive Error Mitigation (HAEM) approach that includes baseline measurement error mitigation and an additional correction layer that utilizes realtime calibration information, along with lightweight machine learning models. The approach can be broken down into three steps: (i) perform standard measurement error mitigation to reduce classical readout noise, (ii) execute compact calibration circuits, such as Bell states, GHZ states and Clifford benchmarks to capture then current error profile of the device, and (iii) use a lightweight machine learning model (having been trained on historical and newest calibration data) to dynamically change the mitigation weights applied for the target quantum algorithm. All implementation is done using Qiskit Runtime to ensure low-latency execution, and we completed experiments on both Qiskit Aer noisy simulators and IBM Quantum devices.Initial results suggest HAEM improved the outputs of quantum algorithms' fidelity compared to the unmitigated runs and the baseline measurement error mitigation (MEM) runs. On the noisy simulators, HAEM increased fidelity from a raw performance of 0.65 to 0.87, nearly a 34% improvement. In fairer hardware-like scenarios, fidelity-on-average was persistently 12% better than MEM, with nearly equal time duration for the additional adaptive measurements. The adaptive aspect of HAEM consistently supported subsequent calibration cycles of the device and was effective to continue providing fidelity improvements despite the static mitigation performance degrading.To sum up, the proposed HAEM framework is a modern and viable alternative for error mitigation which brings together the benefits of baseline approaches with corrections that are driven by fixed-function error-corrective, adaptive learning. As both learning-based and standard baseline approaches can only achieve fixed levels of performance, HAEM, through its use of noise mitigation both at execution time and through correlating it with standard data, provides a route towards reliable quantum computations on current IBM hardware. HAEM also presents a contribution to the NISQ error-resilient landscape, and however it is intended that it will continue to improve future applications of hybrid workflows of quantum and classical computation where error mitigation and resilience is important.
Quantum computing holds the potential to solve problems that are practically unsolvable by classical computers due to its ability to significantly reduce time complexity. We aim to harness this potential to enhance ray casting, a pivotal technique in computer graphics for simplifying the rendering of 3D objects. To perform ray casting in a quantum computer, we need to encode the defining parameters of primitives into qubits. However, during the current noisy intermediate-scale quantum (NISQ) era, challenges arise from the limited number of qubits and the impact of noise when executing multiple gates. Through logic optimization, we reduced the depth of quantum circuits as well as the number of gates and qubits. As a result, the event count of correct measurements from an IBM quantum computer significantly exceeded that of incorrect measurements.