Hardware-efficient quantum annealing with error mitigation via classical shadow

• URL: http://arxiv.org/abs/2503.22269v1
• Date: Fri, 28 Mar 2025 09:34:42 GMT
• Title: Hardware-efficient quantum annealing with error mitigation via classical shadow
• Authors: Takaharu Yoshida, Yuta Shingu, Chihaya Shimada, Tetsuro Nikuni, Hideaki Hakoshima, Yuichiro Matsuzaki,
• Abstract summary: Localized virtual purification (LVP) was proposed to suppress decoherence in the context of noisy quantum devices.<n>In this work, we propose a method to mitigate decoherence errors in QA using LVP.<n>Unlike the previous schemes to mitigate decoherence error for QA, we do not need either two-qubit gates or mid-circuit measurements.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantum annealing (QA) is an efficient method for finding the ground-state energy of the problem Hamiltonian. However, in practical implementation, the system suffers from decoherence. On the other hand, recently, ``Localized virtual purification" (LVP) was proposed to suppress decoherence in the context of noisy intermediate-scale quantum (NISQ) devices. Suppose observables have spatially local support in the lattice. In that case, the requirement for LVP is to calculate the expectation value with a reduced density matrix on a portion of the total system. In this work, we propose a method to mitigate decoherence errors in QA using LVP. The key idea is to use the so-called classical shadow method to construct the reduced density matrix. Thanks to the CS, unlike the previous schemes to mitigate decoherence error for QA, we do not need either two-qubit gates or mid-circuit measurements, which means that our method is hardware-efficient.

Related papers

• Decentralized Optimization on Compact Submanifolds by Quantized Riemannian Gradient Tracking [45.147301546565316]
  This paper considers the problem of decentralized optimization on compact submanifolds.<n>We propose an algorithm where agents update variables using quantized variables.<n>To the best of our knowledge, this is the first algorithm to achieve an $mathcalO (1/K)$ convergence rate in the presence of quantization.
  arXiv  Detail & Related papers  (2025-06-09T01:57:25Z)
• Preparation Circuits for Matrix Product States by Classical Variational Disentanglement [0.0]
  We study the classical compilation of quantum circuits for the preparation of matrix product states (MPS) Our algorithm represents a near-term alternative to previous sequential approaches by reverse application of a disentangler. We show numerical results for ground states of one-dimensional, local Hamiltonians as well as artificially spread out entanglement among multiple qubits.
  arXiv  Detail & Related papers  (2025-04-30T04:13:01Z)
• Matrix encoding method in variational quantum singular value decomposition [49.494595696663524]
  Conditional measurement is involved to avoid small success probability in ancilla measurement. The objective function for the algorithm can be obtained probabilistically via measurement of the state of a one-qubit subsystem.
  arXiv  Detail & Related papers  (2025-03-19T07:01:38Z)
• Resource-Efficient Quantum Correlation Measurement: A Multicopy Neural Network Approach for Practical Applications [0.0]
  We propose an alternative strategy that reduces the required information by combining multicopy measurements with artificial neural networks. We have successfully measured two-qubit quantum correlations of Bell states subjected to a depolarizing channel. Our experiments, conducted with transmon qubits on IBMQ processors, quantified the violation of Bell's inequality and the negativity of two-qubit entangled states.
  arXiv  Detail & Related papers  (2024-11-08T18:07:23Z)
• Solving lattice gauge theories using the quantum Krylov algorithm and qubitization [0.0]
  We investigate using the quantum subspace expansion algorithm to compute the groundstate of the Schwinger model.<n>We present a full analysis of the resources required to compute LGT vacuum states using a quantum algorithm using qubitization.
  arXiv  Detail & Related papers  (2024-03-13T18:00:01Z)
• Solving Systems of Linear Equations: HHL from a Tensor Networks Perspective [39.58317527488534]
  We present an algorithm for solving systems of linear equations based on the HHL algorithm with a novel qudits methodology. We perform a quantum-inspired version on tensor networks, taking advantage of their ability to perform non-unitary operations such as projection.
  arXiv  Detail & Related papers  (2023-09-11T08:18:41Z)
• Randomized semi-quantum matrix processing [0.0]
  We present a hybrid quantum-classical framework for simulating generic matrix functions. The method is based on randomization over the Chebyshev approximation of the target function. We prove advantages on average depths, including quadratic speed-ups on costly parameters.
  arXiv  Detail & Related papers  (2023-07-21T18:00:28Z)
• GRAPE optimization for open quantum systems with time-dependent decoherence rates driven by coherent and incoherent controls [77.34726150561087]
  The GRadient Ascent Pulse Engineering (GRAPE) method is widely used for optimization in quantum control. We adopt GRAPE method for optimizing objective functionals for open quantum systems driven by both coherent and incoherent controls. The efficiency of the algorithm is demonstrated through numerical simulations for the state-to-state transition problem.
  arXiv  Detail & Related papers  (2023-07-17T13:37:18Z)
• Iterative Qubit Coupled Cluster using only Clifford circuits [36.136619420474766]
  An ideal state preparation protocol can be characterized by being easily generated classically. We propose a method that meets these requirements by introducing a variant of the iterative qubit coupled cluster (iQCC) We demonstrate the algorithm's correctness in ground-state simulations and extend our study to complex systems like the titanium-based compound Ti(C5H5)(CH3)3 with a (20, 20) active space.
  arXiv  Detail & Related papers  (2022-11-18T20:31:10Z)
• On the practical usefulness of the Hardware Efficient Ansatz [0.40498500266986387]
  One-dimensional layered Hardware Efficient Ansatz (HEA) seeks to minimize the effect of hardware noise by using native gates and connectives. We show that in these cases a shallow HEA is always trainable and that there exists an anti-concentration of loss function values.
  arXiv  Detail & Related papers  (2022-11-02T20:57:55Z)
• Accelerating the training of single-layer binary neural networks using the HHL quantum algorithm [58.720142291102135]
  We show that useful information can be extracted from the quantum-mechanical implementation of Harrow-Hassidim-Lloyd (HHL) This paper shows, however, that useful information can be extracted from the quantum-mechanical implementation of HHL, and used to reduce the complexity of finding the solution on the classical side.
  arXiv  Detail & Related papers  (2022-10-23T11:58:05Z)
• 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)
• Quantum Approximate Optimization Algorithm Based Maximum Likelihood Detection [80.28858481461418]
  Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices. Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices.
  arXiv  Detail & Related papers  (2021-07-11T10:56:24Z)
• AQD: Towards Accurate Fully-Quantized Object Detection [94.06347866374927]
  We propose an Accurate Quantized object Detection solution, termed AQD, to get rid of floating-point computation. Our AQD achieves comparable or even better performance compared with the full-precision counterpart under extremely low-bit schemes.
  arXiv  Detail & Related papers  (2020-07-14T09:07:29Z)

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.