Fault-Resilience of Dissipative Processes for Quantum Computing

• URL: http://arxiv.org/abs/2502.20374v1
• Date: Thu, 27 Feb 2025 18:50:37 GMT
• Title: Fault-Resilience of Dissipative Processes for Quantum Computing
• Authors: James Purcell, Abhishek Rajput, Toby Cubitt,
• Abstract summary: We prove two main results concerning the error-resilience capabilities of two types of dissipative algorithms.<n>The first result is that under circuit-level depolarizing noise, a version of the DQE algorithm can suppress the additive error in the ground space overlap of the final output state exponentially in the code distance.<n>The second result proves that DQC is no more robust to noise than the standard quantum circuit model.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dissipative processes have long been proposed as a means of performing computational tasks on quantum computers that may be intrinsically more robust to noise. In this work, we prove two main results concerning the error-resilience capabilities of two types of dissipative algorithms: dissipative ground state preparation in the form of the dissipative quantum eigensolver (DQE), and dissipative quantum computation (DQC). The first result is that under circuit-level depolarizing noise, a version of the DQE algorithm applied to the geometrically local, stabilizer-encoded Hamiltonians that arise naturally when fermionic Hamiltonians are represented in qubits, can suppress the additive error in the ground space overlap of the final output state exponentially in the code distance. This enables us to get closer to fault-tolerance for this task without the associated overhead. In contrast, for computation as opposed to ground state preparation, the second result proves that DQC is no more robust to noise than the standard quantum circuit model.

Related papers

• Extending Quantum Perceptrons: Rydberg Devices, Multi-Class Classification, and Error Tolerance [67.77677387243135]
  Quantum Neuromorphic Computing (QNC) merges quantum computation with neural computation to create scalable, noise-resilient algorithms for quantum machine learning (QML) At the core of QNC is the quantum perceptron (QP), which leverages the analog dynamics of interacting qubits to enable universal quantum computation.
  arXiv  Detail & Related papers  (2024-11-13T23:56:20Z)
• Quantum subspace expansion in the presence of hardware noise [0.0]
  Finding ground state energies on current quantum processing units (QPUs) continues to pose challenges. Hardware noise severely affects both the expressivity and trainability of parametrized quantum circuits. We show how to integrate VQE with a quantum subspace expansion, allowing for an optimal balance between quantum and classical computing capabilities and costs.
  arXiv  Detail & Related papers  (2024-04-14T02:48:42Z)
• 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)
• A Lie Algebraic Theory of Barren Plateaus for Deep Parameterized Quantum Circuits [37.84307089310829]
  Variational quantum computing schemes train a loss function by sending an initial state through a parametrized quantum circuit. Despite their promise, the trainability of these algorithms is hindered by barren plateaus. We present a general Lie algebra that provides an exact expression for the variance of the loss function of sufficiently deep parametrized quantum circuits.
  arXiv  Detail & Related papers  (2023-09-17T18:14:10Z)
• On the feasibility of performing quantum chemistry calculations on quantum computers [0.0]
  We propose two criteria for evaluating two leading quantum approaches for finding the ground state of molecules. The first criterion applies to the variational quantum eigensolver (VQE) algorithm. The second criterion applies to the quantum phase estimation (QPE) algorithm.
  arXiv  Detail & Related papers  (2023-06-05T06:41:22Z)
• Limitations of variational quantum algorithms: a quantum optimal transport approach [11.202435939275675]
  We obtain extremely tight bounds for standard NISQ proposals in both the noisy and noiseless regimes. The bounds limit the performance of both circuit model algorithms, such as QAOA, and also continuous-time algorithms, such as quantum annealing.
  arXiv  Detail & Related papers  (2022-04-07T13:58:44Z)
• Exploring entanglement resource in Si quantum dot systems with operational quasiprobability approach [0.9137554315375919]
  We characterize the quantum entanglement of the realistic two-qubit signals that are sensitive to charge noises. We employ the marginal operational quasiprobability (OQ) approach that allows negative values of the probability function if a given state is entangled.
  arXiv  Detail & Related papers  (2022-03-30T08:05:57Z)
• Reducing the cost of energy estimation in the variational quantum eigensolver algorithm with robust amplitude estimation [50.591267188664666]
  Quantum chemistry and materials is one of the most promising applications of quantum computing. Much work is still to be done in matching industry-relevant problems in these areas with quantum algorithms that can solve them.
  arXiv  Detail & Related papers  (2022-03-14T16:51:36Z)
• Numerical Simulations of Noisy Quantum Circuits for Computational Chemistry [51.827942608832025]
  Near-term quantum computers can calculate the ground-state properties of small molecules. We show how the structure of the computational ansatz as well as the errors induced by device noise affect the calculation.
  arXiv  Detail & Related papers  (2021-12-31T16:33:10Z)
• Simulating the Mott transition on a noisy digital quantum computer via Cartan-based fast-forwarding circuits [62.73367618671969]
  Dynamical mean-field theory (DMFT) maps the local Green's function of the Hubbard model to that of the Anderson impurity model. Quantum and hybrid quantum-classical algorithms have been proposed to efficiently solve impurity models. This work presents the first computation of the Mott phase transition using noisy digital quantum hardware.
  arXiv  Detail & Related papers  (2021-12-10T17:32:15Z)
• Approximate Equivalence Checking of Noisy Quantum Circuits [8.36229449571485]
  We study the problem of equivalence checking in the NISQ (Noisy Intermediate-Scale Quantum) computing realm where quantum noise is present inevitably. The notion of approximate equivalence of (possibly noisy) quantum circuits is defined based on the Jamiolkowski fidelity. We present two algorithms, aiming at different situations where the number of noises varies, for computing the fidelity between an ideal quantum circuit and its noisy implementation.
  arXiv  Detail & Related papers  (2021-03-22T05:47:41Z)
• Continuous-time dynamics and error scaling of noisy highly-entangling quantum circuits [58.720142291102135]
  We simulate a noisy quantum Fourier transform processor with up to 21 qubits. We take into account microscopic dissipative processes rather than relying on digital error models. We show that depending on the dissipative mechanisms at play, the choice of input state has a strong impact on the performance of the quantum algorithm.
  arXiv  Detail & Related papers  (2021-02-08T14:55:44Z)
• Mitigating realistic noise in practical noisy intermediate-scale quantum devices [0.5872014229110214]
  Quantum error mitigation (QEM) is vital for noisy intermediate-scale quantum (NISQ) devices. Most conventional QEM schemes assume discrete gate-based circuits with noise appearing either before or after each gate. We show it can be effectively suppressed by a novel QEM method.
  arXiv  Detail & Related papers  (2020-01-14T16:51:35Z)

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.