Dynamic Resource Allocation with Quantum Error Detection

• URL: http://arxiv.org/abs/2408.05565v4
• Date: Thu, 12 Dec 2024 16:41:43 GMT
• Title: Dynamic Resource Allocation with Quantum Error Detection
• Authors: Quinn Langfitt, Alvin Gonzales, Joshua Gao, Ji Liu, Zain H. Saleem, Nikos Hardavellas, Kaitlin N. Smith,
• Abstract summary: We propose a novel resource allocation framework that applies Pauli checks. In this paper, we highlight their potential to infer the noise characteristics of a quantum system. We show that dynamic quantum resource allocation with Pauli checks can outperform state-of-art mapping techniques.
• Score: 8.436934066461625
• License:
• Abstract: Quantum processing units (QPUs) are highly heterogeneous in terms of physical qubit performance. To add even more complexity, drift in quantum noise landscapes has been well-documented. This makes resource allocation a challenging problem whenever a quantum program must be mapped to hardware. As a solution, we propose a novel resource allocation framework that applies Pauli checks. Pauli checks have demonstrated their efficacy at error mitigation in prior work, and in this paper, we highlight their potential to infer the noise characteristics of a quantum system. Circuits with embedded Pauli checks can be executed on different regions of qubits, and the syndrome data created by error-detecting Pauli checks can be leveraged to guide quantum program outcomes toward regions that produce higher-fidelity final distributions. Using noisy simulation and a real QPU testbed, we show that dynamic quantum resource allocation with Pauli checks can outperform state-of-art mapping techniques, such as those that are noise-aware. Further, when applied toward the Quantum Approximate Optimization Algorithm, techniques guided by Pauli checks demonstrate the ability to increase circuit fidelity 11% on average, and up to 33%.

Related papers

• Efficient simulation of parametrized quantum circuits under non-unital noise through Pauli backpropagation [4.903915603499684]
  Pauli backpropagation algorithms have already demonstrated their ability to efficiently simulate certain classes of parameterized quantum circuits. Here, we close this gap by adapting Pauli backpropagation to non-unital noise.
  arXiv  Detail & Related papers  (2025-01-22T17:58:59Z)
• Noise-tolerant learnability of shallow quantum circuits from statistics and the cost of quantum pseudorandomness [0.0]
  We show the natural robustness of quantum statistical queries for learning quantum processes. We adapt a learning algorithm for constant-depth quantum circuits to the quantum statistical query setting. We prove that pseudorandom unitaries (PRUs) cannot be constructed using circuits of constant depth.
  arXiv  Detail & Related papers  (2024-05-20T14:55:20Z)
• 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)
• 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)
• Circuit Symmetry Verification Mitigates Quantum-Domain Impairments [69.33243249411113]
  We propose circuit-oriented symmetry verification that are capable of verifying the commutativity of quantum circuits without the knowledge of the quantum state. In particular, we propose the Fourier-temporal stabilizer (STS) technique, which generalizes the conventional quantum-domain formalism to circuit-oriented stabilizers.
  arXiv  Detail & Related papers  (2021-12-27T21:15:35Z)
• Learnability of the output distributions of local quantum circuits [53.17490581210575]
  We investigate, within two different oracle models, the learnability of quantum circuit Born machines. We first show a negative result, that the output distributions of super-logarithmic depth Clifford circuits are not sample-efficiently learnable. We show that in a more powerful oracle model, namely when directly given access to samples, the output distributions of local Clifford circuits are computationally efficiently PAC learnable.
  arXiv  Detail & Related papers  (2021-10-11T18:00:20Z)
• Mitigating errors by quantum verification and post-selection [0.0]
  We present a technique for quantum error mitigation based on quantum verification, the so-called accreditation protocol, together with post-selection. We discuss the sample complexity of our procedure and provide rigorous guarantees of errors being mitigated under some realistic assumptions on the noise. Our technique also allows for time dependant behaviours, as we allow for the output states to be different between different runs of the accreditation protocol.
  arXiv  Detail & Related papers  (2021-09-29T10:29:39Z)
• Pulse-level noisy quantum circuits with QuTiP [53.356579534933765]
  We introduce new tools in qutip-qip, QuTiP's quantum information processing package. These tools simulate quantum circuits at the pulse level, leveraging QuTiP's quantum dynamics solvers and control optimization features. We show how quantum circuits can be compiled on simulated processors, with control pulses acting on a target Hamiltonian.
  arXiv  Detail & Related papers  (2021-05-20T17:06:52Z)
• Sampling Overhead Analysis of Quantum Error Mitigation: Uncoded vs. Coded Systems [69.33243249411113]
  We show that Pauli errors incur the lowest sampling overhead among a large class of realistic quantum channels. We conceive a scheme amalgamating QEM with quantum channel coding, and analyse its sampling overhead reduction compared to pure QEM.
  arXiv  Detail & Related papers  (2020-12-15T15:51:27Z)
• 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)
• Policy Gradient based Quantum Approximate Optimization Algorithm [2.5614220901453333]
  We show that policy-gradient-based reinforcement learning algorithms are well suited for optimizing the variational parameters of QAOA in a noise-robust fashion. We analyze the performance of the algorithm for quantum state transfer problems in single- and multi-qubit systems.
  arXiv  Detail & Related papers  (2020-02-04T00:46:51Z)

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.