8 research outputs found
Identification of heart rate-associated loci and their effects on cardiac conduction and rhythm disorders
No full textElevated resting heart rate is associated with greater risk of cardiovascular disease and mortality. In a 2-stage meta-analysis of genome-wide association studies in up to 181,171 individuals, we identified 14 new loci associated with heart rate and confirmed associations with all 7 previously established loci. Experimental downregulation of gene expression in Drosophila melanogaster and Danio rerio identified 20 genes at 11 loci that are relevant for heart rate regulation and highlight a role for genes involved in signal transmission, embryonic cardiac development and the pathophysiology of dilated cardiomyopathy, congenital heart failure and/or sudden cardiac death. In addition, genetic susceptibility to increased heart rate is associated with altered cardiac conduction and reduced risk of sick sinus syndrome, and both heart rate-increasing and heart rate-decreasing variants associate with risk of atrial fibrillation. Our findings provide fresh insights into the mechanisms regulating heart rate and identify new therapeutic targets
Adaptive quantum tomography of high-dimensional bipartite systems
No full textAdaptive measurements have recently been shown to significantly improve the performance of quantum state and process tomography. However, the existing methods either cannot be straightforwardly applied to high-dimensional systems or are prohibitively computationally expensive. Here we propose and experimentally implement a novel tomographic protocol specially designed for the reconstruction of high-dimensional quantum states. The protocol shows qualitative improvement in infidelity scaling with the number of measurements and is fast enough to allow for complete state tomography of states with dimensionality up to 36
QuEST-Kit/QuEST: v4.0.0
No full textWhat's new QuEST v4 has completely overhauled the API, software architecture, algorithms, implementations and testing. This page details the new features, divided into those relevant to users, developers who integrate QuEST into larger software stacks, and contributors who develop QuEST or otherwise peep at the source code! TOC: For users For developers For contributors Acknowledgements For users auto-deployer Functions like createQureg() and createFullStateDiagMatr() will automatiaclly decide whether to make use of the compiled and available hardware facilities, like multithreading, GPU-acceleration and distribution. The user no longer needs to consider which deployments are optimal for their simulation sizes, nor which devices have sufficient memory to fit their Qureg! new deployments QuEST can now make use of multiple GPUs, distributed across different machines on a network, or tightly-coupled by a high-bandwidth interconnect (like NVLink); or both! This is true independent of whether you're also using cuQuantum, NVIDIA or AMD GPUs. Further, used deployments are heterogeneous; simultaneous Quregs may use different facilities at runtime! much faster Practically all backend algorithms have been replaced with novel, optimised, bespoke routines - primarily documented in arXiv 2311.0512. Further, special care has been paid to enabling compile-time optimisations, giving structures (like matrices) persistent GPU memory, and lazily evaluating properties like matrix unitarity only once! cleaner interface The API has been polished; function names are more consistent, accept an arithmetic-overloaded complex scalar type (called qcomp), and more natural data structures. Matrices, Pauli tensors and their weighted sums are now easier to initialise and populate, accepting even matrix and string literals! Some functions now have type overloads - even the C ones! - and additional C++ overloads accepting native containers like std::vector. reporters The API also includes utilities for prettily printing all QuEST data structures (like states, operators and scalars) and reporting on the environment and Qureg hardware accelerations being used, the memory available, and the maximum possible simulation sizes. Input validation has also been massively broadened and error messages made precise and dynamic. Usability is through the roof! new functions The set of supported quantum operations has greatly expanded. All unitaries can be effected with any number of control qubits (in any state), diagonal matrices can be raised to powers, density matrices can undergo partial tracing and inhomogeneous Pauli channels (in addition to general Kraus maps and superoperatos), and multi-qubit projectors can now be performed, with and without renormalisation. more control Extensive new debugging facilities allow disabling or changing the validation precision and error response at runtime, and controlling how many amplitudes and significant figures of Qureg and matrices are printed. better documentation The documentation has been rewritten from the ground-up, and the API doc grouped into sub-categories and aesthetically overhauled with Doxygen Awesome. It is now more consistently structured, mathematically explicit, and is a treat on the eyes! For developers new build The CMake build has been overhauled and modernised, with wider platform support and facilities to ease QuEST's integration into larger stacks. The build is more modular, limiting specialised compilers (like nvcc and mpicc) to compiling only their essential files. This minimises friction and widens QuEST's compiler support. easier integration QuEST's backend now uses the standard C++ complex primitive to represent quantum amplitudes and matrix elements, made precision agnostic via new qcomp) type. Further, dense matrices now have both 1D row-major and 2D (aliasing the 1D) memory pointers. This permits Qureg and matrix data to be seamlessly accessed by third-party libraries, such as for linear algebra, without the need for adapters nor expensive copying. For contributors modular architecture QuEST's new software architecture is highly modular, separating the responsibilities of interfacing, validating user input, core pre-processing, localising distributed data, choosing which accelerator to use (CPUs or GPUs), and modifying local data using an accelerator. The core pre-processing is further modularised into modules responsible for autodeploying, inlining, performing maths and bitwise routines, probing available memory, checking internal preconditions, parsing user text, printing output, managing data structures and generating random numbers. See architecture.md for more information. C++ backend While QuEST's frontend remains C and C++ agnostic, the backend has become consistently C++17, affording development luxuries like overloading, templating, type inference, namespaces, smart pointers, constant expressions, type-traiting, structured bindings, range-based looping and use of standard lists like vector. We have however endeavoured to keep the use of C++ facilities simple so that the code remains readable and editable by C programmers. internal preconditions QuEST's defensive design has massively improved by the extensive use of precondition checks, which cheaply validate that internal functions receive correct inputs, where there is room for insidious bugs or future changes. This greatly aids the development process and helps spot bugs earlier, as well as making the assumptions more explicit and ergo the code easier to read and understand. Acknowledgements QuEST v4 development was lead by Tyson Jones, with notable contributions from Oliver Thomson Brown, Richard Meister, Erich Essmann, Ali Rezaei and Simon C. Benjamin. Development was financially supported by the UK National Quantum Computing centre (NQCC200921), the UKRI SEEQA project, the University of Oxford, and the University of Edinburgh's Chancellor's Fellowship scheme. Developer time was contributed by AMD, the QTechTheory group at the University of Oxford, the EPCC of the University of Edinburgh, and Quantum Motion Technologies. Many helpful discussions were had with, and troubleshooting support given by, NVIDIA's cuQuantum team. In addition, Tyson sincerely thanks Zoë Holmes of EPFL's QIC lab for her endless patience while juggling his development and postdoctoral duties! So too he thanks Simon Benjamin for his limitless support, and Oliver Brown for help accessing the tested supercomputers - in addition to his fantastic code contributions! Tyson further apologizes to Richard Meister, Sinan Shi, and Chris Whittle for collective hours (if not days) of rubber duck debugging.Tyson Jones, aniabrown, Jacob Wilkins, Richard Meister, Fergus Cooper, BalintKoczor, Nicolas Vogt, Oliver Thomson Brown, Drew Silcock, Erich Essmann, Christopher, Gleb Struchalin, Kshitij Chhabra, Milos Prokop, & bruno villasenor. (2025). QuEST-Kit/QuEST: v4.0.0 (v4.0.0). Zenodo. https://doi.org/10.5281/zenodo.1516464
QuEST-Kit/QuEST: v3.5.0
No full textOverview
<p>This release adds some quality-of-life functions, mostly to the benefit of larger software stacks which integrate QuEST. It also includes some bug patches and internal software architectural clean-ups, primarily by @rrmeister.</p>
New features
<ul>
<li><code>setDensityAmps()</code> allowing direct modification of the amplitudes of a density matrix.</li>
<li><code>copySubstateToGPU()</code> allowing <em>partial</em> overwriting of a statevector in GPU memory</li>
<li><code>copySubstateFromGPU()</code> allowing <em>partial</em> loading of a GPU statevector into accessible memory</li>
<li><code>mixNonTPKrausMap()</code> to simulate Kraus maps which are not necessarily trace-preserving</li>
</ul>
Other changes
<ul>
<li>Updated the Bernstein-Vazirani demo to simulate more analogously to the experimental method</li>
<li>Updated demo codes to use precision-agnostic string formatters</li>
<li>Improved CMake build (f8747ca10a966d9c3ba7bc73824e48c3b4159f2b)</li>
<li>Improved the internal validation architecture</li>
</ul>
Bug fixes
<ul>
<li>patched rare distributed bug in <code>calcTotalProb()</code> of density matrices (#326)</li>
<li>patched rare GPU build bug in <code>applyPhaseFunc()</code></li>
<li>patched rare memory leak during failed validation of <code>Qureg</code> and <code>DiagonalOp</code> validation</li>
<li>patched GPU build flags (512966a5851b85cbbf70f2246785afc3efb32e30)</li>
<li>patched <code>invalidQuESTInputError()</code> build problem on Windows (#314)</li>
<li>patched build warnings related to precision of internal non-simulation functions (fc25c34abc53bf8842ff39356ed94dd1012b41e3)</li>
</ul>
QuEST-Kit/QuEST: v3.6.0
No full textOverview
<p>This release makes QuEST compatible with <strong>AMD GPUs</strong> , makes the unit tests compatible with MSVC on Windows, and adds sub-diagonal operators.</p>
New features
<ul>
<li>Compatibility with AMD GPUs, thanks to @bvillasen!</li>
<li><code>SubDiagonalOp</code>; a non-distributed structure representing a diagonal unitary (specified only through the diagonal elements) upon a <em>subset</em> of a qureg's qubits. This can be passed to new functions:<ul>
<li><code>createSubDiagonalOp()</code></li>
<li><code>destroySubDiagonalOp()</code></li>
<li><code>diagonalUnitary()</code></li>
<li><code>applySubDiagonalOp()</code></li>
<li><code>applyGateSubDiagonalOp()</code></li>
</ul>
</li>
<li>Functions for applying gates specified as non-unitary matrices. Specifically:<ul>
<li><code>applyGateMatrixN()</code></li>
<li><code>applyMultiControlledGateMatrixN()</code></li>
</ul>
</li>
<li><code>setQuregToPauliHamil()</code> for casting a Pauli Hamiltonian into a dense Z-basis matrix, stored in a density matrix <code>Qureg</code>.</li>
<li>Phase function <code>SCALED_INVERSE_SHIFTED_WEIGHTED_DISTANCE</code> recognised by <code>applyParamNamedPhaseFunc()</code> (and related functions)</li>
</ul>
Other changes
<ul>
<li>Unit tests are now compatible with MSVC on Windows, and are automatically ran from Github Actions.</li>
<li>Documentation is automatically regenerated whenever the master branch is updated.</li>
</ul>
Bug fixes
<ul>
<li>Math functions used internally (like <code>cos()</code>) now explicitly use quad precision when compiled with <code>PRECISION=4</code>, thanks to @Milos9304 </li>
<li>Fixed overflow and other precision issues in the unit tests, thanks to @rrmeister </li>
</ul>
QuEST-Kit/QuEST: v3.7.0
No full textOverview
<p>This release integrates a <a href="https://developer.nvidia.com/cuquantum-sdk">cuQuantum</a> backend, optimises distributed communication, and improves the unit tests.</p>
New features
<ul>
<li>QuEST gained a new backend which integrates <a href="https://developer.nvidia.com/cuquantum-sdk">cuQuantum</a> and <a href="https://developer.nvidia.com/thrust">Thrust</a> for optimised simulation on modern NVIDIA GPUs. This is compiled with cmake argument <code>-DUSE_CUQUANTUM=1</code>, as detailed in the <a href="https://github.com/QuEST-Kit/QuEST/blob/master/examples/README.md#compiling">compile doc</a>. Unlike QuEST's other backends, this <em>does</em> require prior installation of cuQuantum, outlined <a href="https://developer.nvidia.com/cuQuantum-downloads">here</a>. This deployment mode should run much faster than QuEST's custom GPU backend, and will soon enable multi-GPU simulation. The entirety of QuEST's API is supported! :tada:</li>
</ul>
Other changes
<ul>
<li>QuEST's distributed communication has been optimised when exchanging states via many maximum-size messages, thanks to the work of <a href="https://github.com/jjacobx">Jakub Adamski</a> as per <a href="https://arxiv.org/abs/2308.07402">this manuscript</a>.</li>
<li>Functions like <code>multiQubitUnitary()</code> and <code>mixMultiQubitKrausMap()</code> have <em>relaxed</em> the precision of their unitarity and CPTP checks, so they will complain less about user matrices. Now, for example, a unitarity matrix <code>U</code> is deemed valid only if every element of <code>U*dagger(U)</code> has a Euclidean distance of at most <code>REAL_EPS</code> from its expected identity-matrix element. </li>
<li>Unit tests now <em>check</em> that their initial register states are as expected before testing an operator. This ensures that some tests do not <em>accidentally pass</em> when they <em>should</em> be failing (like when run with an incorrectly specified GPU compute capability) due to an unexpected all-zero initial state.</li>
<li>Unit tests now use an improved and numerically stable function for generating random unitaries and Kraus maps, so should trigger fewer precision errors and false test failures.</li>
</ul>
Benchmarking quantum tomography completeness and fidelity with machine learning
No full textWe train convolutional neural networks to predict whether or not a set of measurements is informationally complete to uniquely reconstruct any given quantum state with no prior information. In addition, we perform fidelity benchmarking based on this measurement set without explicitly carrying out state tomography. The networks are trained to recognize the fidelity and a reliable measure for informational completeness. By gradually accumulating measurements and data, these trained convolutional networks can efficiently establish a compressive quantum-state characterization scheme by accelerating runtime computation and greatly reducing systematic drifts in experiments. We confirm the potential of this machine-learning approach by presenting experimental results for both spatial-mode and multiphoton systems of large dimensions. These predictions are further shown to improve when the networks are trained with additional bootstrapped training sets from real experimental data. Using a realistic beam-profile displacement error model for Hermite-Gaussian sources, we further demonstrate numerically that the orders-of-magnitude reduction in certification time with trained networks greatly increases the computation yield of a large-scale quantum processor using these sources, before state fidelity deteriorates significantly.11Ysciescopu
A genome-wide association study of upper aerodigestive tract cancers conducted within the INHANCE consortium
No full textGenome-wide association studies (GWAS) have been successful in identifying common genetic variation involved in susceptibility to etiologically complex disease. We conducted a GWAS to identify common genetic variation involved in susceptibility to upper aero-digestive tract (UADT) cancers. Genome-wide genotyping was carried out using the Illumina HumanHap300 beadchips in 2,091 UADT cancer cases and 3,513 controls from two large European multi-centre UADT cancer studies, as well as 4,821 generic controls. The 19 top-ranked variants were investigated further in an additional 6,514 UADT cancer cases and 7,892 controls of European descent from an additional 13 UADT cancer studies participating in the INHANCE consortium. Five common variants presented evidence for significant association in the combined analysis (p≤5×10−7). Two novel variants were identified, a 4q21 variant (rs1494961, p = 1×10−8) located near DNA repair related genes HEL308 and FAM175A (or Abraxas) and a 12q24 variant (rs4767364, p = 2×10−8) located in an extended linkage disequilibrium region that contains multiple genes including the aldehyde dehydrogenase 2 (ALDH2) gene. Three remaining variants are located in the ADH gene cluster and were identified previously in a candidate gene study involving some of these samples. The association between these three variants and UADT cancers was independently replicated in 5,092 UADT cancer cases and 6,794 controls non-overlapping samples presented here (rs1573496-ADH7, p = 5×10−8; rs1229984-ADH1B, p = 7×10−9; and rs698-ADH1C, p = 0.02). These results implicate two variants at 4q21 and 12q24 and further highlight three ADH variants in UADT cancer susceptibility
