Quantum Circuit Fidelity Improvement with Long Short-Term Memory Networks

• URL: http://arxiv.org/abs/2303.17523v2
• Date: Tue, 9 May 2023 12:17:51 GMT
• Title: Quantum Circuit Fidelity Improvement with Long Short-Term Memory Networks
• Authors: Yikai Mao, Shaswot Shresthamali, Masaaki Kondo
• Abstract summary: NISQ computers show great promise in accelerating many tasks that are not practically possible using classical computation. One important reason is due to the fragile nature of quantum hardware. As the building blocks of a quantum circuit (QC), quantum gates and qubits are susceptible to external interference.
• Score: 1.2461503242570644
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although NISQ computers show great promise in accelerating many tasks that are not practically possible using classical computation, useful quantum computing is still a long way off. One important reason is due to the fragile nature of quantum hardware. As the building blocks of a quantum circuit (QC), quantum gates and qubits are susceptible to external interference, and therefore even a simple QC can produce extremely noisy output. Since it is hard to distinguish whether the output represents meaningful computation or just random noise, it raises the question of how much we can rely on the output of a QC, i.e., the fidelity of the QC. In this paper, we purpose a simple yet intuitive metric to measure the fidelity of a QC. By using this metric, we can observe the evolution of fidelity with time as the QC interacts with its external environment. Consequently, we can frame fidelity prediction as a Time Series Forecasting problem and use Long Short-Term Memory (LSTM) neural networks to better estimate the fidelity of a QC. This gives the user better opportunities to optimize the mapping of qubits into the quantum hardware for larger gains. We introduce the LSTM architecture and present a complete workflow to build the training circuit dataset. The trained LSTM system, Q-fid, can predict the output fidelity of a QC running on a specific quantum processor, without the need for any separate input of hardware calibration data or gate error rates. Evaluated on the QASMbench NISQ benchmark suite, Q-fid's prediction achieves an average RMSE of 0.0515, up to 24.7x more accurate than the default Qiskit transpile tool mapomatic. When used to find the high-fidelity circuit layouts from the available circuit transpilations, Q-fid predicts the fidelity for the top 10% layouts with an average RMSE of 0.0252, up to 32.8x more accurate than mapomatic.

Related papers

• Variational Quantum Machine Learning with Quantum Error Detection [0.6435156676256051]
  Quantum machine learning (QML) is an emerging field that promises advantages such as faster training, improved reliability and superior extraction over classical counterparts. Its implementation on quantum hardware is challenging due to the noise inherent in these systems, necessitating the use of quantum error correction (QEC) codes. Current QML research remains primarily theoretical, often assuming noise-free environments and offering little insight into the integration of QEC with QML.
  arXiv  Detail & Related papers  (2025-04-09T10:56:21Z)
• An Accurate and Efficient Analytic Model of Fidelity Under Depolarizing Noise Oriented to Large Scale Quantum System Design [1.80755313284025]
  We present a comprehensive theoretical framework to predict the fidelity of quantum circuits under depolarizing noise. We propose an efficient fidelity estimation algorithm based on device calibration data. The proposed approach provides a scalable and practical tool for benchmarking quantum hardware.
  arXiv  Detail & Related papers  (2025-03-09T16:59:24Z)
• Bayesian Quantum Amplitude Estimation [49.1574468325115]
  We introduce BAE, a noise-aware Bayesian algorithm for quantum amplitude estimation. We show that BAE achieves Heisenberg-limited estimation and benchmark it against other approaches.
  arXiv  Detail & Related papers  (2024-12-05T18:09:41Z)
• Quantum Data Centers in the Presence of Noise [0.0]
  Single-processor monolithic quantum computers are affected by increased cross talk and difficulty of implementing gates when the number of qubits is increased. In a QDC, multiple quantum processing units (QPUs) are linked together over short distances, allowing the total number of computational qubits to be increased without increasing the number of qubits on any one processor. In doing so, the error incurred by operations at each QPU can be kept small, however additional noise will be added to the system due to the latency cost and errors incurred during inter-QPU entanglement distribution.
  arXiv  Detail & Related papers  (2024-07-15T14:50:20Z)
• Bayesian Parameterized Quantum Circuit Optimization (BPQCO): A task and hardware-dependent approach [49.89480853499917]
  Variational quantum algorithms (VQA) have emerged as a promising quantum alternative for solving optimization and machine learning problems. In this paper, we experimentally demonstrate the influence of the circuit design on the performance obtained for two classification problems. We also study the degradation of the obtained circuits in the presence of noise when simulating real quantum computers.
  arXiv  Detail & Related papers  (2024-04-17T11:00:12Z)
• QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum Circuits [82.50620782471485]
  QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits. It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness. Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
  arXiv  Detail & Related papers  (2024-01-10T22:33:00Z)
• Learning Quantum Phase Estimation by Variational Quantum Circuits [0.9208007322096533]
  We develop a variational quantum circuit (VQC) approximation to reduce the depth of the Quantum Phase Estimation circuit. Our experiments demonstrated that the VQC outperformed both Noisy QPE and standard QPE on real hardware by reducing circuit noise. This VQC integration into quantum compilers holds significant promise for quantum algorithms with deep circuits.
  arXiv  Detail & Related papers  (2023-11-08T13:57:24Z)
• QKSAN: A Quantum Kernel Self-Attention Network [53.96779043113156]
  A Quantum Kernel Self-Attention Mechanism (QKSAM) is introduced to combine the data representation merit of Quantum Kernel Methods (QKM) with the efficient information extraction capability of SAM. A Quantum Kernel Self-Attention Network (QKSAN) framework is proposed based on QKSAM, which ingeniously incorporates the Deferred Measurement Principle (DMP) and conditional measurement techniques. Four QKSAN sub-models are deployed on PennyLane and IBM Qiskit platforms to perform binary classification on MNIST and Fashion MNIST.
  arXiv  Detail & Related papers  (2023-08-25T15:08:19Z)
• Adaptive mitigation of time-varying quantum noise [0.1227734309612871]
  Current quantum computers suffer from non-stationary noise channels with high error rates. We propose a Bayesian inference-based adaptive algorithm that can learn and mitigate quantum noise in response to changing channel conditions.
  arXiv  Detail & Related papers  (2023-08-16T01:33:07Z)
• Scaling Limits of Quantum Repeater Networks [62.75241407271626]
  Quantum networks (QNs) are a promising platform for secure communications, enhanced sensing, and efficient distributed quantum computing. Due to the fragile nature of quantum states, these networks face significant challenges in terms of scalability. In this paper, the scaling limits of quantum repeater networks (QRNs) are analyzed.
  arXiv  Detail & Related papers  (2023-05-15T14:57:01Z)
• Quantum Imitation Learning [74.15588381240795]
  We propose quantum imitation learning (QIL) with a hope to utilize quantum advantage to speed up IL. We develop two QIL algorithms, quantum behavioural cloning (Q-BC) and quantum generative adversarial imitation learning (Q-GAIL) Experiment results demonstrate that both Q-BC and Q-GAIL can achieve comparable performance compared to classical counterparts.
  arXiv  Detail & Related papers  (2023-04-04T12:47:35Z)
• QuEst: Graph Transformer for Quantum Circuit Reliability Estimation [32.89844497610906]
  Python library called TorchQuantum can construct, simulate, and train PQC for machine learning tasks. We propose to leverage a graph transformer model to predict noise impact on circuit fidelity. Compared with circuit simulators, the predictor has over 200X speedup for estimating the fidelity.
  arXiv  Detail & Related papers  (2022-10-30T02:35:31Z)
• Error Mitigation-Aided Optimization of Parameterized Quantum Circuits: Convergence Analysis [42.275148861039895]
  Variational quantum algorithms (VQAs) offer the most promising path to obtaining quantum advantages via noisy processors. gate noise due to imperfections and decoherence affects the gradient estimates by introducing a bias. Quantum error mitigation (QEM) techniques can reduce the estimation bias without requiring any increase in the number of qubits. QEM can reduce the number of required iterations, but only as long as the quantum noise level is sufficiently small.
  arXiv  Detail & Related papers  (2022-09-23T10:48:04Z)
• Deterministic and random features for large-scale quantum kernel machine [0.9404723842159504]
  We show that the quantum kernel method (QKM) can be made scalable by using our proposed deterministic and random features. Our numerical experiment, using datasets including $O(1,000) sim O(10,000)$ training data, supports the validity of our method.
  arXiv  Detail & Related papers  (2022-09-05T13:22:34Z)
• QSAN: A Near-term Achievable Quantum Self-Attention Network [73.15524926159702]
  Self-Attention Mechanism (SAM) is good at capturing the internal connections of features. A novel Quantum Self-Attention Network (QSAN) is proposed for image classification tasks on near-term quantum devices.
  arXiv  Detail & Related papers  (2022-07-14T12:22:51Z)
• Quantum Machine Learning for Software Supply Chain Attacks: How Far Can We Go? [5.655023007686363]
  This paper analyzes speed up performance of QC when applied to machine learning algorithms, known as Quantum Machine Learning (QML) Due to limitations of real quantum computers, the QML methods were implemented on open-source quantum simulators such as Qiskit and IBM Quantum. Interestingly, the experimental results differ to the speed up promises of QC by demonstrating higher computational time and lower accuracy in comparison to the classical approaches for SSC attacks.
  arXiv  Detail & Related papers  (2022-04-04T21:16:06Z)
• Measurement based estimator scheme for continuous quantum error correction [52.77024349608834]
  Canonical discrete quantum error correction (DQEC) schemes use projective von Neumann measurements on stabilizers to discretize the error syndromes into a finite set. Quantum error correction (QEC) based on continuous measurement, known as continuous quantum error correction (CQEC), can be executed faster than DQEC and can also be resource efficient. We show that by constructing a measurement-based estimator (MBE) of the logical qubit to be protected, it is possible to accurately track the errors occurring on the physical qubits in real time.
  arXiv  Detail & Related papers  (2022-03-25T09:07:18Z)
• QuantumNAT: Quantum Noise-Aware Training with Noise Injection, Quantization and Normalization [19.822514659801616]
  Quantum Circuits (PQC) are promising towards quantum advantage on near-term quantum hardware. However, due to the large quantum noises (errors), the performance of PQC models has a severe degradation on real quantum devices. We present QuantumNAT, a PQC-specific framework to perform noise-aware optimizations in both training and inference stages to improve robustness.
  arXiv  Detail & Related papers  (2021-10-21T17:59:19Z)
• Quantum circuit architecture search for variational quantum algorithms [88.71725630554758]
  We propose a resource and runtime efficient scheme termed quantum architecture search (QAS) QAS automatically seeks a near-optimal ansatz to balance benefits and side-effects brought by adding more noisy quantum gates. We implement QAS on both the numerical simulator and real quantum hardware, via the IBM cloud, to accomplish data classification and quantum chemistry tasks.
  arXiv  Detail & Related papers  (2020-10-20T12:06:27Z)
• On the learnability of quantum neural networks [132.1981461292324]
  We consider the learnability of the quantum neural network (QNN) built on the variational hybrid quantum-classical scheme. We show that if a concept can be efficiently learned by QNN, then it can also be effectively learned by QNN even with gate noise.
  arXiv  Detail & Related papers  (2020-07-24T06:34:34Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.