Efficient re-sampling in quasi-probability decompositions

• URL: http://arxiv.org/abs/2608.02075v1
• Date: Mon, 03 Aug 2026 11:24:32 GMT
• Title: Efficient re-sampling in quasi-probability decompositions
• Abstract summary: Near-term quantum devices are limited by noise and hardware constraints, motivating algorithmic approaches that trade circuit complexity for increased sampling overhead.<n>We introduce a reweighting strategy for QPDs for circuits with the same variational structure across parameter settings, reusing samples and thereby reducing the sampling overhead.
• Score: 0.4666493857924357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Near-term quantum devices are limited by noise and hardware constraints, motivating algorithmic approaches that trade circuit complexity for increased sampling overhead. Quasi-probability decompositions (QPDs), for example, allow replacing non-local operations by multiple circuits with local operations, but the associated sampling overhead generally scales exponentially and limits their practicality. In this work, we introduce a reweighting strategy for QPDs for circuits with the same variational structure across parameter settings, reusing samples and thereby reducing the sampling overhead. We first demonstrate this approach by estimating fidelities between parameterized quantum states, a key primitive in variational time evolution and quantum kernel methods. Importantly, this setup allows controlling the exponential QPD sampling overhead while preserving the structure of the state-encoding ansatz. We then apply the method to estimate the real part of the quantum geometric tensor using the simultaneous perturbation stochastic approximation and find that, in the presence of realistic hardware noise, our method outperforms other standard estimation techniques. These results highlight the potential of reweighting strategies to extend the applicability of QPD-based methods in variational quantum algorithms.

Related papers

• Quantum Signal Processing for Linear PDEs: Circuit Design and Experimental Validation [1.814099487268094]
  We develop end-to-end implementations of quantum circuits compiled to machine-level instructions.<n>We experimentally demonstrate that the QSP-augmented algorithm can provide accurate solutions under realistic hardware constraints.
  arXiv  Detail & Related papers  (2026-05-29T21:21:32Z)
• Continual Quantum Architecture Search with Tensor-Train Encoding: Theory and Applications to Signal Processing [68.35481158940401]
  CL-QAS is a continual quantum architecture search framework.<n>It mitigates challenges of costly encoding amplitude and forgetting in variational quantum circuits.<n>It achieves controllable robustness expressivity, sample-efficient generalization, and smooth convergence without barren plateaus.
  arXiv  Detail & Related papers  (2026-01-10T02:36:03Z)
• 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)
• Provably Robust Training of Quantum Circuit Classifiers Against Parameter Noise [49.97673761305336]
  Noise remains a major obstacle to achieving reliable quantum algorithms.<n>We present a provably noise-resilient training theory and algorithm to enhance the robustness of parameterized quantum circuit classifiers.
  arXiv  Detail & Related papers  (2025-05-24T02:51:34Z)
• Adaptive variational quantum dynamics simulations with compressed circuits and fewer measurements [4.2643127089535104]
  We show an improved version of the adaptive variational quantum dynamics simulation (AVQDS) method, which we call AVQDS(T)<n>The algorithm adaptively adds layers of disjoint unitary gates to the ansatz circuit so as to keep the McLachlan distance, a measure of the accuracy of the variational dynamics, below a fixed threshold.<n>We also show a method based on eigenvalue truncation to solve the linear equations of motion for the variational parameters with enhanced noise resilience.
  arXiv  Detail & Related papers  (2024-08-13T02:56:43Z)
• Characterizing randomness in parameterized quantum circuits through expressibility and average entanglement [39.58317527488534]
  Quantum Circuits (PQCs) are still not fully understood outside the scope of their principal application.<n>We analyse the generation of random states in PQCs under restrictions on the qubits connectivities.<n>We place a connection between how steep is the increase on the uniformity of the distribution of the generated states and the generation of entanglement.
  arXiv  Detail & Related papers  (2024-05-03T17:32:55Z)
• Real-time error mitigation for variational optimization on quantum hardware [45.935798913942904]
  We define a Real Time Quantum Error Mitigation (RTQEM) algorithm to assist in fitting functions on quantum chips with VQCs. Our RTQEM routine can enhance VQCs' trainability by reducing the corruption of the loss function.
  arXiv  Detail & Related papers  (2023-11-09T19:00:01Z)
• 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)
• Trainability Enhancement of Parameterized Quantum Circuits via Reduced-Domain Parameter Initialization [3.031137751464259]
  We show that by reducing the initial domain of each parameter proportional to the square root of circuit depth, the magnitude of the cost gradient decays at most inversely to qubit count and circuit depth. This strategy can protect specific quantum neural networks from exponentially many spurious local minima.
  arXiv  Detail & Related papers  (2023-02-14T06:41:37Z)
• Decomposition of Matrix Product States into Shallow Quantum Circuits [62.5210028594015]
  tensor network (TN) algorithms can be mapped to parametrized quantum circuits (PQCs) We propose a new protocol for approximating TN states using realistic quantum circuits. Our results reveal one particular protocol, involving sequential growth and optimization of the quantum circuit, to outperform all other methods.
  arXiv  Detail & Related papers  (2022-09-01T17:08:41Z)
• Parameter-Parallel Distributed Variational Quantum Algorithm [7.255056332088222]
  Variational quantum algorithms (VQAs) have emerged as a promising near-term technique to explore practical quantum advantage on noisy devices. Here, we propose a parameter-parallel distributed variational quantum algorithm (PPD-VQA) to accelerate the training process by parameter-parallel training with multiple quantum processors. The achieved results suggest that the PPD-VQA could provide a practical solution for coordinating multiple quantum processors to handle large-scale real-word applications.
  arXiv  Detail & Related papers  (2022-07-31T15:09:12Z)
• Adaptive pruning-based optimization of parameterized quantum circuits [62.997667081978825]
  Variisy hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. We propose a strategy for such ansatze used in variational quantum algorithms, which we call "Efficient Circuit Training" (PECT) Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms.
  arXiv  Detail & Related papers  (2020-10-01T18:14:11Z)

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.