Toward Reliability in the NISQ Era: Robust Interval Guarantee for
Quantum Measurements on Approximate States
- URL: http://arxiv.org/abs/2110.09793v2
- Date: Mon, 9 Jan 2023 11:23:19 GMT
- Title: Toward Reliability in the NISQ Era: Robust Interval Guarantee for
Quantum Measurements on Approximate States
- Authors: Maurice Weber, Abhinav Anand, Alba Cervera-Lierta, Jakob S. Kottmann,
Thi Ha Kyaw, Bo Li, Al\'an Aspuru-Guzik, Ce Zhang, Zhikuan Zhao
- Abstract summary: We develop robustness intervals which are guaranteed to contain the output in the ideal setting.
We demonstrate our results in the context of the variational quantum eigensolver (VQE) on noisy and noiseless simulations.
- Score: 11.58897666429261
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Near-term quantum computation holds potential across multiple application
domains. However, imperfect preparation and evolution of states due to
algorithmic and experimental shortcomings, characteristic in the near-term
implementation, would typically result in measurement outcomes deviating from
the ideal setting. It is thus crucial for any near-term application to quantify
and bound these output errors. We address this need by deriving robustness
intervals which are guaranteed to contain the output in the ideal setting. The
first type of interval is based on formulating robustness bounds as
semi-definite programs, and uses only the first moment and the fidelity to the
ideal state. Furthermore, we consider higher statistical moments of the
observable and generalize bounds for pure states based on the non-negativity of
Gram matrices to mixed states, thus enabling their applicability in the NISQ
era where noisy scenarios are prevalent. Finally, we demonstrate our results in
the context of the variational quantum eigensolver (VQE) on noisy and noiseless
simulations.
Related papers
- Quantum Fidelity Estimation in the Resource Theory of Nonstabilizerness [11.386506926570442]
fidelity estimation is essential for benchmarking quantum states and processes on noisy quantum devices.<n>We propose several efficient protocols for both quantum states and channels within the resource theory of nonstabilizerness.
arXiv Detail & Related papers (2025-06-15T18:51:09Z) - General detectability measure [53.64687146666141]
Distinguishing resource states from resource-free states is a fundamental task in quantum information.
We derived the optimal exponential decay rate of the failure probability for detecting a given $n$-tensor product state.
arXiv Detail & Related papers (2025-01-16T05:39:22Z) - Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise [51.87307904567702]
Quantile regression is a leading approach for obtaining such intervals via the empirical estimation of quantiles in the distribution of outputs.
We propose Relaxed Quantile Regression (RQR), a direct alternative to quantile regression based interval construction that removes this arbitrary constraint.
We demonstrate that this added flexibility results in intervals with an improvement in desirable qualities.
arXiv Detail & Related papers (2024-06-05T13:36:38Z) - Asymptotic behavior of continuous weak measurement and its application
to real-time parameter estimation [4.329298109272387]
The quantum trajectory of weak continuous measurement for the magnetometer is investigated.
We find that the behavior is insensitive to the initial state in the following sense: given one realization, the quantum trajectories starting from arbitrary initial statesally converge to the em same realization-specific em pure state.
arXiv Detail & Related papers (2023-11-03T17:50:45Z) - Score Matching-based Pseudolikelihood Estimation of Neural Marked
Spatio-Temporal Point Process with Uncertainty Quantification [59.81904428056924]
We introduce SMASH: a Score MAtching estimator for learning markedPs with uncertainty quantification.
Specifically, our framework adopts a normalization-free objective by estimating the pseudolikelihood of markedPs through score-matching.
The superior performance of our proposed framework is demonstrated through extensive experiments in both event prediction and uncertainty quantification.
arXiv Detail & Related papers (2023-10-25T02:37:51Z) - PAPAL: A Provable PArticle-based Primal-Dual ALgorithm for Mixed Nash Equilibrium [58.26573117273626]
We consider the non-AL equilibrium nonconptotic objective function in two-player zero-sum continuous games.
Our novel insights into the particle-based algorithms for continuous distribution strategies are presented.
arXiv Detail & Related papers (2023-03-02T05:08:15Z) - Certifying randomness in quantum state collapse [4.5070885135627226]
In this paper, we explore the quantitive connection between the randomness generation and the state collapse.
We provide a randomness verification protocol under the assumptions: (I) independence between the source and the measurement devices and (II) the L"uders' rule for collapsing state.
arXiv Detail & Related papers (2022-10-29T15:31:16Z) - Improved Quantum Algorithms for Fidelity Estimation [77.34726150561087]
We develop new and efficient quantum algorithms for fidelity estimation with provable performance guarantees.
Our algorithms use advanced quantum linear algebra techniques, such as the quantum singular value transformation.
We prove that fidelity estimation to any non-trivial constant additive accuracy is hard in general.
arXiv Detail & Related papers (2022-03-30T02:02:16Z) - Quantum key distribution with non-ideal heterodyne detection: composable
security of discrete-modulation continuous-variable protocols [6.85316573653194]
Continuous-variable quantum key distribution exploits coherent measurements of the electromagnetic field.
In doing this, we establish for the first time the composable security of discrete-modulation continuous-variable quantum key distribution in the finite-size regime.
arXiv Detail & Related papers (2021-08-01T11:00:10Z) - The Variational Method of Moments [65.91730154730905]
conditional moment problem is a powerful formulation for describing structural causal parameters in terms of observables.
Motivated by a variational minimax reformulation of OWGMM, we define a very general class of estimators for the conditional moment problem.
We provide algorithms for valid statistical inference based on the same kind of variational reformulations.
arXiv Detail & Related papers (2020-12-17T07:21:06Z) - Amortized Conditional Normalized Maximum Likelihood: Reliable Out of
Distribution Uncertainty Estimation [99.92568326314667]
We propose the amortized conditional normalized maximum likelihood (ACNML) method as a scalable general-purpose approach for uncertainty estimation.
Our algorithm builds on the conditional normalized maximum likelihood (CNML) coding scheme, which has minimax optimal properties according to the minimum description length principle.
We demonstrate that ACNML compares favorably to a number of prior techniques for uncertainty estimation in terms of calibration on out-of-distribution inputs.
arXiv Detail & Related papers (2020-11-05T08:04:34Z) - Near Optimality of Finite Memory Feedback Policies in Partially Observed
Markov Decision Processes [0.0]
We study a planning problem for POMDPs where the system dynamics and measurement channel model is assumed to be known.
We find optimal policies for the approximate belief model under mild non-linear filter stability conditions.
We also establish a rate of convergence result which relates the finite window memory size and the approximation error bound.
arXiv Detail & Related papers (2020-10-15T00:37:51Z)
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.