Estimating Non-Stabilizerness Dynamics Without Simulating It
- URL: http://arxiv.org/abs/2405.06054v1
- Date: Thu, 9 May 2024 18:57:55 GMT
- Title: Estimating Non-Stabilizerness Dynamics Without Simulating It
- Authors: Alessio Paviglianiti, Guglielmo Lami, Mario Collura, Alessandro Silva,
- Abstract summary: Iterative Clifford Circuit Renormalization (I CCR) is designed to efficiently handle the dynamics of non-stabilizerness in quantum circuits.
I CCR embeds the complex dynamics of non-stabilizerness in the flow of an effective initial state.
We implement the I CCR algorithm to evaluate the non-stabilizerness dynamics for systems of size up to N = 1000.
- Score: 43.80709028066351
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We introduce the Iterative Clifford Circuit Renormalization (ICCR), a novel technique designed to efficiently handle the dynamics of non-stabilizerness (a.k.a. quantum magic) in generic quantum circuits. ICCR iteratively adjusts the starting circuit, transforming it into a Clifford circuit where all elements that can alter the non-stabilizerness, such as measurements or T gates, have been removed. In the process the initial state is renormalized in such a way that the new circuit outputs the same final state as the original one. This approach embeds the complex dynamics of non-stabilizerness in the flow of an effective initial state, enabling its efficient evaluation while avoiding the need for direct and computationally expensive simulation of the original circuit. The initial state renormalization can be computed explicitly using an approximation that can be systematically improved. We implement the ICCR algorithm to evaluate the non-stabilizerness dynamics for systems of size up to N = 1000. We validate our method by comparing it to tensor networks simulations. Finally, we employ the ICCR technique to study a magic purification circuit, where a measurement-induced transition is observed.
Related papers
- 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)
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.
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) - Scalable approach to monitored quantum dynamics and entanglement phase transitions [0.0]
Measurement-induced entanglement phase transitions in monitored quantum circuits have stimulated activity in a diverse research community.
We present a solution by introducing a scalable protocol in $U(1)$ symmetric circuits.
arXiv Detail & Related papers (2024-06-27T09:59:58Z) - Large-Scale OD Matrix Estimation with A Deep Learning Method [70.78575952309023]
The proposed method integrates deep learning and numerical optimization algorithms to infer matrix structure and guide numerical optimization.
We conducted tests to demonstrate the good generalization performance of our method on a large-scale synthetic dataset.
arXiv Detail & Related papers (2023-10-09T14:30:06Z) - Sub-linear Regret in Adaptive Model Predictive Control [56.705978425244496]
We present STT-MPC (Self-Tuning Tube-based Model Predictive Control), an online oracle that combines the certainty-equivalence principle and polytopic tubes.
We analyze the regret of the algorithm, when compared to an algorithm initially aware of the system dynamics.
arXiv Detail & Related papers (2023-10-07T15:07:10Z) - Stabilizer circuit verification [0.0]
We propose a set of efficient classical algorithms to fully characterize and exhaustively verify stabilizer circuits.
We provide an algorithm for checking the equivalence of stabilizer circuits.
All of our algorithms provide relations of measurement outcomes among corresponding circuit representations.
arXiv Detail & Related papers (2023-09-15T18:06:17Z) - Hybrid algorithm simulating non-equilibrium steady states of an open
quantum system [10.752869788647802]
Non-equilibrium steady states are a focal point of research in the study of open quantum systems.
Previous variational algorithms for searching these steady states have suffered from resource-intensive implementations.
We present a novel variational quantum algorithm that efficiently searches for non-equilibrium steady states by simulating the operator-sum form of the Lindblad equation.
arXiv Detail & Related papers (2023-09-13T01:57:27Z) - Adaptive Planning Search Algorithm for Analog Circuit Verification [53.97809573610992]
We propose a machine learning (ML) approach, which uses less simulations.
We show that the proposed approach is able to provide OCCs closer to the specifications for all circuits.
arXiv Detail & Related papers (2023-06-23T12:57:46Z) - D4FT: A Deep Learning Approach to Kohn-Sham Density Functional Theory [79.50644650795012]
We propose a deep learning approach to solve Kohn-Sham Density Functional Theory (KS-DFT)
We prove that such an approach has the same expressivity as the SCF method, yet reduces the computational complexity.
In addition, we show that our approach enables us to explore more complex neural-based wave functions.
arXiv Detail & Related papers (2023-03-01T10:38:10Z) - 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) - A Dynamical Systems Approach for Convergence of the Bayesian EM
Algorithm [59.99439951055238]
We show how (discrete-time) Lyapunov stability theory can serve as a powerful tool to aid, or even lead, in the analysis (and potential design) of optimization algorithms that are not necessarily gradient-based.
The particular ML problem that this paper focuses on is that of parameter estimation in an incomplete-data Bayesian framework via the popular optimization algorithm known as maximum a posteriori expectation-maximization (MAP-EM)
We show that fast convergence (linear or quadratic) is achieved, which could have been difficult to unveil without our adopted S&C approach.
arXiv Detail & Related papers (2020-06-23T01:34:18Z)
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.