Quantum error mitigation using energy sampling and extrapolation enhanced Clifford data regression

• URL: http://arxiv.org/abs/2511.03556v1
• Date: Wed, 05 Nov 2025 15:37:33 GMT
• Title: Quantum error mitigation using energy sampling and extrapolation enhanced Clifford data regression
• Authors: Zhongqi Zhao, Erik Rosendahl Kjellgren, Sonia Coriani, Jacob Kongsted, Stephan P. A. Sauer, Karl Michael Ziems,
• Abstract summary: This work explores and extends Clifford Data Regression (CDR) to mitigate noise in quantum chemistry simulations.<n>Two improvements to the CDR framework are proposed.
• Score: 0.6274666302833676
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Error mitigation is essential for the practical implementation of quantum algorithms on noisy intermediate-scale quantum (NISQ) devices. This work explores and extends Clifford Data Regression (CDR) to mitigate noise in quantum chemistry simulations using the Variational Quantum Eigensolver (VQE). Using the H$_4$ molecule with the tiled Unitary Product State (tUPS) ansatz, we perform noisy simulations with the ibm torino noise model to investigate in detail the effect of various hyperparameters in CDR on the error mitigation quality. Building on these insights, two improvements to the CDR framework are proposed. The first, Energy Sampling (ES), improves performance by selecting only the lowest-energy training circuits for regression, thereby further biasing the sample energies toward the target state. The second, Non-Clifford Extrapolation (NCE), enhances the regression model by including the number of non-Clifford parameters as an additional input, enabling the model to learn how the noisy-ideal mapping evolves as the circuit approaches the optimal one. Our numerical results demonstrate that both strategies outperform the original CDR.

Related papers

• Noise Hypernetworks: Amortizing Test-Time Compute in Diffusion Models [57.49136894315871]
  New paradigm of test-time scaling has yielded remarkable breakthroughs in reasoning models and generative vision models.<n>We propose one solution to the problem of integrating test-time scaling knowledge into a model during post-training.<n>We replace reward guided test-time noise optimization in diffusion models with a Noise Hypernetwork that modulates initial input noise.
  arXiv  Detail & Related papers  (2025-08-13T17:33:37Z)
• A purely Quantum Generative Modeling through Unitary Scrambling and Collapse [6.647966634235082]
  Quantum Scrambling and Collapse Generative Model (QGen) is a purely quantum paradigm that eliminates classical dependencies.<n>We introduce a measurement-based training principle that decomposes learning into tractable subproblems, mitigating barren plateaus.<n> Empirically, QGen outperforms classical and hybrid baselines under matched parameter budget, while maintaining robustness under finite-shot sampling.
  arXiv  Detail & Related papers  (2025-06-12T11:00:21Z)
• Clifford-Dressed Variational Principles for Precise Loschmidt Echoes [44.99833362998488]
  We extend the recently introduced Clifford dressed Time-Dependent Variational Principle (TDVP) to efficiently compute many-body wavefunction amplitudes in the computational basis.<n>By incorporating Clifford disentangling gates during TDVP evolution, our method effectively controls entanglement growth while keeping the computation of these amplitudes accessible.
  arXiv  Detail & Related papers  (2025-02-03T22:43:32Z)
• A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution [50.13564338607482]
  We propose a novel optimization framework for the hyperspectral deconvolution problem, called DeepMix.<n>It consists of three distinct modules, namely, a data consistency module, a module that enforces the effect of the handcrafted regularizers, and a denoising module.<n>This work proposes a context aware denoising module designed to sustain the advancements achieved by the cooperative efforts of the other modules.
  arXiv  Detail & Related papers  (2023-06-10T08:25:16Z)
• Machine Learning Aided Dimensionality Reduction towards a Resource Efficient Projective Quantum Eigensolver [0.0]
  Recently developed Projective Quantum Eigensolver (PQE) has been demonstrated as an elegant methodology to compute the ground state energy of molecular systems. We have exploited the collective interplay of these two sets of parameters via machine learning techniques to bring out the synergistic inter-relationship.
  arXiv  Detail & Related papers  (2023-03-20T16:49:56Z)
• Are Quantum Circuits Better than Neural Networks at Learning Multi-dimensional Discrete Data? An Investigation into Practical Quantum Circuit Generative Models [0.0]
  We show that multi-layer parameterized quantum circuits (MPQCs) are more expressive than classical neural networks (NNs) We organize available sources into a systematic proof on why MPQCs are able to generate probability distributions that cannot be efficiently simulated classically. We address practical issues such as how to efficiently train a quantum circuit with only limited samples, how to efficiently calculate the gradient (quantum) and how to alleviate modal collapse.
  arXiv  Detail & Related papers  (2022-12-13T05:31:31Z)
• 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)
• 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)
• MoEfication: Conditional Computation of Transformer Models for Efficient Inference [66.56994436947441]
  Transformer-based pre-trained language models can achieve superior performance on most NLP tasks due to large parameter capacity, but also lead to huge computation cost. We explore to accelerate large-model inference by conditional computation based on the sparse activation phenomenon. We propose to transform a large model into its mixture-of-experts (MoE) version with equal model size, namely MoEfication.
  arXiv  Detail & Related papers  (2021-10-05T02:14:38Z)
• Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks [41.702175127106784]
  Energy-based models (EBMs) are generative models that are usually trained via maximum likelihood estimation. We propose a dual formulation of an EBMs algorithm in which the particles are sometimes restarted at random samples drawn from the data set, and show that performing these restarts corresponds to a score every step. These results are illustrated in simple numerical experiments.
  arXiv  Detail & Related papers  (2021-07-11T21:43:18Z)
• Simulating quench dynamics on a digital quantum computer with data-driven error mitigation [62.997667081978825]
  We present one of the first implementations of several Clifford data regression based methods which are used to mitigate the effect of noise in real quantum data. We find in general Clifford data regression based techniques are advantageous in comparison with zero-noise extrapolation. This is the largest systems investigated so far in a study of this type.
  arXiv  Detail & Related papers  (2021-03-23T16:56:14Z)
• Modeling and mitigation of cross-talk effects in readout noise with applications to the Quantum Approximate Optimization Algorithm [0.0]
  Noise mitigation can be performed up to some error for which we derive upper bounds. Experiments on 15 (23) qubits using IBM's devices to test both the noise model and the error-mitigation scheme. We show that similar effects are expected for Haar-random quantum states and states generated by shallow-depth random circuits.
  arXiv  Detail & Related papers  (2021-01-07T02:19:58Z)
• Unified approach to data-driven quantum error mitigation [0.7046417074932257]
  We propose a novel, scalable error mitigation method that conceptually unifies ZNE and CDR. Our approach, called variable-noise Clifford data regression (vnCDR), significantly outperforms these individual methods in numerical benchmarks. For the problem of correcting observables from random quantum circuits with 64 qubits, vnCDR improves the error by factors of 2.7 and 1.5 over ZNE and CDR, respectively.
  arXiv  Detail & Related papers  (2020-11-02T17:56:02Z)

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.