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quantum-computingCleveland Clinic, RIKEN, and IBM Model a 12635-Atom Protein – the Largest Known to Be Simulated with Quantum Computers - Cleveland Clinic
Cleveland Clinic, RIKEN, and IBM Model a 12635-Atom Protein – the Largest Known to Be Simulated with Quantum Computers Cleveland Clinic
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quantum-computingAssessing non-Gaussian quantum state conversion with the stellar rank
AbstractState conversion is a fundamental task in quantum information processing. Quantum resource theories allow for analyzing and bounding conversions that use restricted sets of operations. In the context of continuous-variable systems, state conversions restricted to Gaussian operations are crucial for both fundamental and practical reasons, particularly in state preparation and quantum computing with bosonic codes. However, previous analysis did not consider the relevant case of approximate state conversion. In this work, we introduce a framework for assessing approximate Gaussian state conversion by extending the stellar rank to the approximate stellar rank, which serves as an operational measure of non-Gaussianity. We derive bounds for Gaussian state conversion and distillation under approximate and probabilistic conditions, yielding new no-go results for non-Gaussian state preparation and enabling a reliable assessment of the performance of Gaussian conversion protocols. We also provide an open-source Python library to compute stellar-rank-related quantities and to assess Gaussian conversion.Popular summaryIn quantum optics, we typically distinguish between "easy" Gaussian states and operations and "difficult" non-Gaussian ones. Non-Gaussian states and operations are necessary to build quantum computers capable of outperforming their classical counterparts. Non-Gaussian states and operations are thus conceivable as resources for quantum information processing. Various metrics have been introduced to quantify these non-Gaussian resources, such as the stellar rank. However, these metrics only worked in perfect, idealized settings. In the lab, we deal with noise and other imperfections, while we only need to get "close enough" to a target non-Gaussian state. In this article, we introduce the approximate stellar rank. This is a more realistic non-Gaussian measure that can account for the small errors inherent in experimental physics. We use this new measure to e
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quantum-computingPhase Transitions and Noise Robustness of Quantum Graph States
AbstractGraph states are entangled states that are essential for quantum information processing. As experimental advances enable the realization of large-scale graph states, efficient fidelity estimation methods are crucial for assessing their robustness against noise. However, calculations of exact fidelity become intractable for large systems due to the exponential growth in the number of stabilizers. In this work, we show that the fidelity between any ideal graph state and its noisy counterpart under IID Pauli noise can be mapped to the partition function of a classical spin system, enabling efficient computation via statistical mechanical techniques. Using this approach, we analyze the fidelity for regular graph states under depolarizing noise and uncover the emergence of phase transitions in fidelity between the pure-state regime and the noise-dominated regime. Specifically, in 2D, phase transitions occur only when the degree satisfies $d\ge 6$, while in 3D they already appear at $d\ge 5$. However, for graph states with excessively high degree, such as fully connected graphs, the phase transition disappears. Robustness of graph states against noise is thus determined by their connectivity and spatial dimensionality. Graph states with lower degree and/or dimensionality, which exhibit a smooth crossover, demonstrate greater robustness, while highly connected or higher-dimensional graph states are more fragile. Extreme connectivity, as the fully connected graph state possesses, restores robustness. Furthermore, we show that the fidelity can be rewritten in the form of the partition function of a constraint-percolation problem. Within this picture, we discuss the qualitative difference between 2D regular graph states with $d=6$ and $d=5$ regarding the presence or absence of a phase transition, as well as the suppressed critical behavior of fully connected graph states.Featured image: Two-dimensional regular graph states with degree $d=4$ (square lattice) and $d=6$
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quantum-computingEfficient Simulation of High-Level Quantum Gates
AbstractQuantum circuit simulation is paramount to the verification and optimization of quantum algorithms, and considerable research efforts have been made towards efficient simulators. While circuits often contain high-level gates such as oracles and multi-controlled $X$ ($C^kX$) gates, existing simulation methods require compilation to a low-level gate-set before simulation. This, however, increases circuit size and incurs a considerable (typically exponential) overhead, even when the number of high-level gates is small. Here we present a gadget-based simulator which simulates high-level gates directly, thereby allowing to avoid or reduce the blowup of compilation. Our simulator uses a stabilizer decomposition of the magic state of non-stabilizer gates, with improvements in the rank of the magic state directly improving performance. We then proceed to establish a small stabilizer rank for a range of high-level gates that are common in various quantum algorithms. Using these bounds in our simulator, we improve both the theoretical complexity of simulating circuits containing such gates, and the practical running time compared to standard simulators found in IBM's Qiskit Aer library. We also derive exponential lower-bounds for the stabilizer rank of some gates under common complexity-theoretic hypotheses. In certain cases, our lower-bounds are asymptotically tight on the exponent.► BibTeX data@article{Kjelstrom2026efficientsimulation, doi = {10.22331/q-2026-05-05-2093}, url = {https://doi.org/10.22331/q-2026-05-05-2093}, title = {Efficient {S}imulation of {H}igh-{L}evel {Q}uantum {G}ates}, author = {Kjelstr{\o{}}m, Adam Husted and Pavlogiannis, Andreas and Pol, Jaco van de}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {10}, pages = {2093}, month = may, year = {2026} }► References [1] Leonidas Lampropoulos, Zoe Paraskevopoulou, and Benjamin C. Pierce. ``Gene
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quantum-computingHere’s Why Quantum Computing (QUBT) is One of The Best Quantum Computing Stocks To Buy and Hold For 10 Years - Yahoo Finance
Here’s Why Quantum Computing (QUBT) is One of The Best Quantum Computing Stocks To Buy and Hold For 10 Years Yahoo Finance
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Hyper-optimized Quantum Lego Contraction Schedules
AbstractCalculating the quantum weight enumerator polynomial (WEP) is a valuable tool for characterizing quantum error-correcting (QEC) codes, but it is computationally hard for large or complex codes. The Quantum LEGO (QL) framework provides a tensor network approach for WEP calculation, in some cases offering superpolynomial speedups over brute-force methods, provided the code exhibits area law entanglement, that a good QL layout is used, and an efficient tensor network contraction schedule is found. We analyze the performance of a hyper-optimized contraction schedule framework across QL layouts for diverse stabilizer code families. We find that the intermediate tensors in the QL networks for stabilizer WEPs are often highly sparse, invalidating the dense-tensor assumption of standard cost functions. To address this, we introduce an exact, polynomial-time Sparse Stabilizer Tensor (SST) cost function based on the rank of the parity check matrices for intermediate tensors. The SST cost function correlates perfectly with the true contraction cost, providing a significant advantage over the default cost function, which exhibits large uncertainty. Optimizing contraction schedules using the SST cost function yields substantial performance gains, achieving up to orders of magnitude improvement in actual contraction cost compared to using the dense tensor cost function. Furthermore, the precise cost estimation from the SST function offers an efficient metric to decide whether the QL-based WEP calculation is computationally superior to brute force for a given QL layout. These results, enabled by PlanqTN, a new open-source QL implementation, validate hyper-optimized contraction as a crucial technique for leveraging the QL framework to explore the QEC code design space.Popular summaryQuantum error correction is one of the central tools for making large-scale quantum computers reliable. A major challenge, however, is that the space of possible error-correcting codes is enormo
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quantum-computingNonclassical nullifiers for quantum hypergraph states
AbstractQuantum hypergraph states form a generalisation of the graph state formalism that goes beyond the pairwise (dyadic) interactions imposed by remaining inside the Gaussian approximation. Networks of such states are able to achieve universality for continuous variable measurement based quantum computation with only Gaussian measurements. For normalised states, the simplest hypergraph states are formed from $k$-adic interactions among a collection of $k$ harmonic oscillator ground states. However such powerful resources have not yet been observed in experiments and their robustness and scalability have not been tested. Here we develop and analyse necessary criteria for hypergraph nonclassicality based on simultaneous nonlinear squeezing in the nullifiers of hypergraph states. We put forward an essential analysis of their robustness to realistic scenarios involving thermalisation or loss and suggest several basic proof-of-principle options for experiments to observe nonclassicality in hypergraph states.► BibTeX data@article{Ravikumar2026nonclassical, doi = {10.22331/q-2026-05-05-2091}, url = {https://doi.org/10.22331/q-2026-05-05-2091}, title = {Nonclassical nullifiers for quantum hypergraph states}, author = {Ravikumar, Abhijith and Moore, Darren W. and Filip, Radim}, journal = {{Quantum}}, issn = {2521-327X}, publisher = {{Verein zur F{\"{o}}rderung des Open Access Publizierens in den Quantenwissenschaften}}, volume = {10}, pages = {2091}, month = may, year = {2026} }► References [1] Ri Qu, Juan Wang, Zong-shang Li, and Yan-ru Bao. ``Encoding hypergraphs into quantum states''. Physical Review A 87, 022311 (2013). https://doi.org/10.1103/PhysRevA.87.022311 [2] M. Rossi, M. Huber, D. Bruß, and C. Macchiavello. ``Quantum hypergraph states''. New Journal of Physics 15, 113022 (2013). https://doi.org/10.1088/1367-2630/15/11/113022 [3] Robert Raussendorf and Hans J. Briegel. ``A One-Way Quantum Computer''. Physical Review Letters 86, 5188–5191 (2001). http
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