Tootfinder

Quantencomputer mit Fehlerkorrektur bis 2028: IBM mit ambitionierter Roadmap
IBM will bis 2028 einen Quantencomputer mit Fehlerkorrektur und deutlich höherer Rechenleistung bauen. Der soll über die Cloud verfügbar gemacht werden.

Majorana Signatures in the Tripartite Uncertainty Relations with Quantum Memory
D. Maroulakos (Doctoral School at the University of Rzeszow), C. Jasiukiewicz (Department of Physics and Medical Engineering Rzeszow University of Technology), A. Wal (Institute of Physics Faculty of Exact and Technical Sciences University of Rzeszow), A. Sinner (Institute of Physics University of Opole), I. Weymann (Institute of Spintronics and Quantum Information Faculty of Physics and Astronomy A. Mickie…

Majorana Signatures in the Tripartite Uncertainty Relations with Quantum Memory
Quantumness imposes a fundamental limit on measurement accuracy. The paradigmatic cases are Heisenberg's uncertainty relation in the original formulation, Robertson's formulation, and improved uncertainty relations. However, the more universal measures are given in terms of quantum entropies. Uncertainties of measurements done on one quantum system correlated with another quantum system constitute a more intriguing question. Quantum correlations can influence the lower bound of uncertainties, a…

Toward Scalable Quantum Compilation for Modular Architecture: Qubit Mapping and Reuse via Deep Reinforcement Learning
Sokea Sang, Leanghok Hour, Youngsun Han
https://arxiv.org/abs/2506.09323

Toward Scalable Quantum Compilation for Modular Architecture: Qubit Mapping and Reuse via Deep Reinforcement Learning
Modular quantum architectures have emerged as a promising approach for scaling quantum computing systems by connecting multiple Quantum Processing Units (QPUs). However, this approach introduces significant challenges due to costly inter-core operations between chips and quantum state transfers, which contribute to noise and quantum decoherence. This paper presents QARMA, a novel Qubit mapping using Attention-based deep Reinforcement learning (DRL) for Modular quantum Architectures, along with …

Qymera: Simulating Quantum Circuits using RDBMS
Tim Littau, Rihan Hai
https://arxiv.org/abs/2506.08759 https://arxiv.org/pdf/2506.087…

Qymera: Simulating Quantum Circuits using RDBMS
Quantum circuit simulation is crucial for quantum computing such as validating quantum algorithms. We present Qymera, a system that repurposes relational database management systems (RDBMSs) for simulation by translating circuits into SQL queries, allowing quantum operations to run natively within an RDBMS. Qymera supports a wide range of quantum circuits, offering a graphical circuit builder and code-based interfaces to input circuits. With a benchmarking framework, Qymera facilitates comparis…

Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models
Maja Franz, Melvin Strobl, Leonid Chaichenets, Eileen Kuehn, Achim Streit, Wolfgang Mauerer
https://arxiv.org/abs/2506.09527

Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models
The field of variational quantum algorithms, in particular quantum machine learning (QML), produced numerous theoretical and empirical insights in recent years. As variational quantum circuits (VQCs) can be represented by Fourier series that contain an exponentially large spectrum in the number of input features, hope for quantum advantage remains. Nevertheless, properties of quantum Fourier models (QFMs) are not yet fully understood, in particular how they could potentially outperform classica…

Scaling Portfolio Diversification with Quantum Circuit Cutting Techniques
Vicente P. Soloviev, Antonio M\'arquez Romero, Josh Kirsopp, Michal Krompiec
https://arxiv.org/abs/2506.08947

Scaling Portfolio Diversification with Quantum Circuit Cutting Techniques
Quantum Approximate Optimization Algorithms (QAOA) have demonstrated a strong potential in addressing graph-based optimization problems. However, the execution of large-scale quantum circuits remains constrained by the limitations of current quantum hardware. In this work, we introduce QuantCut, an automatic framework for circuit cutting that enables efficient execution of large quantum circuits by decomposing entangling two-qubit gates into manageable sub-circuits. Specifically, we focus on ga…

A Sagnac-based arbitrary time-bin state encoder for quantum communication applications
Kannan Vijayadharan, Mat\'ias Rub\'en Bola\~nos, Marco Avesani, Giuseppe Vallone, Paolo Villoresi, Costantino Agnesi
https://arxiv.org/abs/2506.08971

A Sagnac-based arbitrary time-bin state encoder for quantum communication applications
Time-bin encoding of quantum information is highly advantageous for long-distance quantum communication protocols over optical fibres due to its inherent robustness in the channel and the possibility of generating high-dimensional quantum states. The most common implementation of time-bin quantum states using unbalanced interferometers presents challenges in terms of stability and flexibility of operation. In particular, a limited number of states can be generated without modifying the optical …

Quantifying imaginarity of quantum operations
Chuanfa Wu, Zhaoqi Wu
https://arxiv.org/abs/2506.09747 https://arxiv.org/pdf/2506.09747…

Quantifying imaginarity of quantum operations
Complex numbers are theoretically proved and experimentally confirmed as necessary in quantum mechanics and quantum information, and a resource theory of imaginarity of quantum states has been established. In this work, we establish a framework to quantify the imaginarity of quantum operations from the perspective of the ability to create or detect imaginarity, following the idea by Theurer {\it et al.} [Phys. Rev. Lett. \textbf{122}, 190405 (2019)] used in coherence theory. We present two type…

Local surrogates for quantum machine learning
Sreeraj Rajindran Nair, Christopher Ferrie
https://arxiv.org/abs/2506.09425 https://arx…

Local surrogates for quantum machine learning
Surrogates have been proposed as classical simulations of the pretrained quantum learning models, which are capable of mimicking the input-output relation inherent in the quantum model. Quantum hardware within this framework is used for training and for generating the classical surrogates. Inference is relegated to the classical surrogate, hence alleviating the extra quantum computational cost once training is done. Taking inspiration from interpretable models, we introduce a local surrogation …

Speeding up adiabatic holonomic quantum gates via $\pi$-pulse modulation
Jiang Zhang, Tonghao Xing, Guilu Long
https://arxiv.org/abs/2506.09326 https://

Speeding up adiabatic holonomic quantum gates via $π$-pulse modulation
Holonomic quantum computation (HQC) offers an inherently robust approach to quantum gate implementation by exploiting quantum holonomies. While adiabatic HQC benefits from robustness against certain control errors, its long runtime limits practical utility due to increased exposure to environmental noise. Nonadiabatic HQC addresses this issue by enabling faster gate operations but compromises robustness. In this work, we propose a scheme for fast holonomic quantum gates based on the $π$-pulse …