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Randomized benchmarking is a technique for estimating the average fidelity of a set of quantum gates. For general gatesets, however, it is difficult to draw robust conclusions from the resulting data. Here we propose a new method based on…

Quantum Physics · Physics 2019-07-31 Jonas Helsen , Xiao Xue , Lieven M. K. Vandersypen , Stephanie Wehner

To build a fault-tolerant quantum computer, it is necessary to implement a quantum error correcting code. Such codes rely on the ability to extract information about the quantum error syndrome while not destroying the quantum information…

We generalize the polynomial-time outcome-complete simulation algorithm for stabilizer circuits in arXiv:2309.08676 to track global phases exactly, yielding what we call phased outcome-complete simulation. The original algorithm enabled…

Quantum Physics · Physics 2026-03-27 Vadym Kliuchnikov , Adam Paetznick , Marcus P. da Silva

Developing space- and time-efficient logical magic state preparation protocols will likely be an essential step towards building a large-scale fault-tolerant quantum computer. Motivated by this need, we introduce a scalable method for…

Quantum Physics · Physics 2026-05-26 Samyak Surti , Lucas Daguerre , Isaac H. Kim

Stabilization is a key dependability property for dealing with unanticipated transient faults, as it guarantees that even in the presence of such faults, the system will recover to states where it satisfies its specification. One of the…

Distributed, Parallel, and Cluster Computing · Computer Science 2017-06-12 Vidhya Tekken Valapil , Sandeep S. Kulkarni

We describe a method to use measurements and correction operations in order to implement the Clifford group in a stabilizer code, generalising a result from [Bombin,2011] for topological subsystem colour codes. In subsystem stabilizer codes…

Quantum Physics · Physics 2025-02-10 Darren Banfield , Heather Leitch , Alastair Kay

A key requirement for scalable quantum computing is that elementary quantum gates can be implemented with sufficiently low error. One method for determining the error behavior of a gate implementation is to perform process tomography.…

Magic-state resource theory is a fundamental framework with far-reaching applications in quantum error correction and the classical simulation of quantum systems. Recent advances have significantly deepened our understanding of magic as a…

Quantum Physics · Physics 2026-04-15 Lennart Bittel , Lorenzo Leone

In this paper, we introduce a class of highly entangled real quantum states that cannot be approximated by circuits with $\log$-many non-Clifford gates and prove that Bell sampling enables efficient cross-device verification (or distributed…

Quantum Physics · Physics 2025-08-07 Janek Denzler , Santiago Varona , Tommaso Guaita , Jose Carrasco

A common requirement of quantum simulations and algorithms is the preparation of complex states through sequences of 2-qubit gates. For a generic quantum state, the number of gates grows exponentially with the number of qubits, becoming…

Quantum Physics · Physics 2024-07-08 Matan Ben Dov , David Shnaiderov , Adi Makmal , Emanuele G. Dalla Torre

Quantum entanglement is essential for modern quantum information processing. Entanglement gates convert initially non-entangled states into entangled ones by applying time-dependent parametric pulses. While Bell state preparation has been…

Quantum Physics · Physics 2026-05-27 Jia-Nan Wu , Bingsuo Zou , Guojun Jin , Yongyou Zhang

We show that qubit stabilizer states can be represented by non-negative quasi-probability distributions associated with a Wigner-Weyl-Moyal formalism where Clifford gates are positive state-independent maps. This is accomplished by…

Quantum Physics · Physics 2018-01-03 Lucas Kocia , Peter Love

Conventional fault-tolerant quantum error-correction schemes require a number of extra qubits that grows linearly with the code's maximum stabilizer generator weight. For some common distance-three codes, the recent "flag paradigm" uses…

Quantum Physics · Physics 2020-09-07 Rui Chao , Ben W. Reichardt

Stabilizer states are a central resource in quantum information processing, underpinning a wide range of applications. While they can be efficiently generated via Clifford circuits, the presence of coherent errors, such as small-angle…

Quantum Physics · Physics 2025-10-24 Giovanni Di Bartolomeo , Giulio Crognaletti , Angelo Bassi , Michele Vischi

The stabilizer rank of a quantum state $\psi$ is the minimal $r$ such that $\left| \psi \right \rangle = \sum_{j=1}^r c_j \left|\varphi_j \right\rangle$ for $c_j \in \mathbb{C}$ and stabilizer states $\varphi_j$. The running time of several…

Quantum Physics · Physics 2022-03-09 Shir Peleg , Amir Shpilka , Ben Lee Volk

A decision diagram (DD) is a graph-like data structure for homomorphic compression of Boolean and pseudo-Boolean functions. Over the past decades, decision diagrams have been successfully applied to verification, linear algebra, stochastic…

Quantum Physics · Physics 2026-02-23 Arend-Jan Quist , Tim Coopmans , Alfons Laarman

Topological quantum computing promises intrinsic fault tolerance by encoding quantum information in non-Abelian anyons, where quantum gates are implemented via braiding. While braiding operations are robust against local perturbations, a…

Quantum Physics · Physics 2025-08-15 Themba Hodge , Philipp Frey , Stephan Rachel

It is a fundamental property of quantum mechanics that information is lost as a result of performing measurements. Indeed, with every quantum measurement one can associate a number -- its POVM norm constant -- that quantifies how much the…

Quantum Physics · Physics 2016-09-28 Richard Kueng , Huangjun Zhu , David Gross

A defining feature in the field of quantum computing is the potential of a quantum device to outperform its classical counterpart for a specific computational task. By now, several proposals exist showing that certain sampling problems can…

Quantum Physics · Physics 2020-09-23 Rawad Mezher , Joe Ghalbouni , Joseph Dgheim , Damian Markham

Uniform stability of a learning algorithm is a classical notion of algorithmic stability introduced to derive high-probability bounds on the generalization error (Bousquet and Elisseeff, 2002). Specifically, for a loss function with range…

Machine Learning · Computer Science 2019-03-19 Vitaly Feldman , Jan Vondrak