travishumble starred BBN-Q/Auspex: Automated system for python-based experiments
Automated system for python-based experiments. Stars: 34
Chronological feed of everything captured from Travis Humble.
Automated system for python-based experiments. Stars: 34
Quantum Gate Language (QGL) is a domain specific language embedded in python for specifying quantum gate sequences.. Stars: 33
A superconducting quantum processor with up to 50 qubits can quantitatively simulate quantum materials, specifically KCuF$_3$, by computing dynamical structure factors (DSFs). The simulations benchmark against inelastic neutron scattering data, validating the accuracy of pre-fault-tolerant devices for material science applications. This workflow establishes a method for simulating complex quantum materials in regimes typically challenging for classical computation.
A leading-edge control system for quantum information experiments. Stars: 514
Quantum assembly language for extended quantum circuits. Stars: 1456
Modern C++ quantum computing library. Stars: 650
NWChem: Open Source High-Performance Computational Chemistry. Stars: 595
QuTiP: Quantum Toolbox in Python. Stars: 1987
SST Architectural Simulation Components and Libraries. Stars: 119
Open vSwitch. Stars: 3930
Comprehensive, GPU accelerated framework for developing universal virtual quantum processors. Stars: 219
Qiskit is an open-source SDK for working with quantum computers at the level of extended quantum circuits, operators, and primitives.. Stars: 7238
An Open Source Machine Learning Framework for Everyone. Stars: 194674
A 50-qubit superconducting quantum processor simulates dynamical structure factors (DSFs) of KCuF3, a gapless Luttinger liquid, yielding quantitative agreement with inelastic neutron-scattering experiments via a quantum-classical workflow. Simulation accuracy is assessed using multiple metrics, revealing impacts from circuit depth and fidelity. The approach extends to gapped spectra in anisotropic 1D XXZ Heisenberg models relevant to CsCoX3 compounds, establishing a testable framework for strongly entangled quantum materials.
QASMTrans is a self-contained C++ quantum compiler that achieves over 100x faster transpilation than Qiskit for large, high-depth circuits while maintaining comparable quality, enabling JIT deployment on FPGA/CPU-integrated QPUs. It provides end-to-end compilation from QASM to device pulses with QICK integration, supporting latency-aware Application-tailored Gate Sets (AGS) that optimize critical path pulse schedules and improve fidelity by up to 12% via QuTiP simulation. Noise-adaptive transpilation uses calibration data for qubit placement and critical path focus, plus device partitioning for concurrent circuit execution, facilitating real-time adaptive algorithms like ADAPT-VQE.
Classical computational methods for energy materials face scaling and time-complexity limits, especially for high-dimensional or strongly correlated systems. Quantum computing leverages superposition and entanglement to tackle intractable problems, enabling hybrid QC-classical approaches for practical material simulation and design. Fault-tolerant QC promises predictive accuracy and quantum advantage for complex systems, though significant challenges persist.
Sample-based quantum diagonalization (SQD) is a hybrid quantum-HPC algorithm that encodes molecular Hamiltonians into quantum circuits, measures electronic configurations on quantum hardware, and performs iterative diagonalization on filtered subspaces using classical HPC systems. The diagonalization step, handled by the Davidson algorithm on selected electron configurations, is the primary computational bottleneck. By leveraging GPU thread-level parallelism via OpenMP offload on heterogeneous systems like Frontier, the approach achieves ~100x per-node performance gains, reducing classical processing from hours to minutes and enabling efficient ground/excited state energy extraction.
Quantum computing leverages quantum mechanical principles like superposition to revolutionize computation, offering solutions to problems intractable for classical computers. This field is moving from isolated research to integrated systems, seeking to enhance existing high-performance computing infrastructure rather than replace it. Key applications include material science simulations, with a focus on areas like efficient ammonia synthesis, and the potential adoption of quantum cloud platforms for research and education.
Heterogeneous Quantum Architectures (HQA) integrate superconducting (SC) qubits' speed with neutral-atom (NA) qubits' scalability to overcome limitations of homogeneous systems in fault-tolerant computing. MagicAcc offloads latency-critical Magic State Factories to SC while computing on NA arrays; MCSep uses NA for dense qLDPC memory and SC for fast surface-code processing. End-to-end cost modeling shows 752x average speedup over NA-only baselines and over 10x physical qubit reduction versus SC-only systems via cross-modality interconnects.
Q-IRIS integrates the IRIS task-based runtime with XACC via QIR-EE to enable asynchronous execution of QIR programs across heterogeneous quantum backends and simulators. It supports concurrent classical and quantum tasks, demonstrated by scheduling multiple quantum workloads. Quantum circuit cutting decomposes circuits into subcircuits, reducing simulation load and improving throughput on early quantum hardware.
Researchers demonstrate certified randomness amplification over a network using Quantinuum's 98-qubit Helios trapped-ion processor by dynamically streaming quantum gates and delaying measurement basis revelation. The protocol maintains coherence for ~0.9 seconds, limits classical spoofing to 30 ms, and constrains adversaries to a 4,500 km radius. It achieves 0.586 fidelity on 64-qubit random circuits with 276 two-qubit gates, enabling amplification of low-entropy sources into nearly perfect randomness secure against malicious remote devices.
NVQLink proposes a platform connecting HPC resources to QPU control systems via commercial Ethernet, achieving 3.96 μs max round-trip latency for real-time tasks under 10s of μs tolerance. It supports all QPU modalities and controllers through CUDA-Q extensions enabling real-time callbacks and unified C++ programming of CPU/GPU/FPGA subsystems in the QSC, bypassing slow HTTP interfaces. QSC integration uses MLIR dialects and progressive lowering for kernel-based heterogeneous execution.
Quantum computing complements rather than replaces HPC, with hybrid systems integrating QC acceleration into classical infrastructures deemed essential for practical scalability. Current QC faces high error rates and limited coherence, necessitating traditional HPC to maximize quantum benefits. The ADAC Institute's Quantum Computing Working Group synthesizes member surveys highlighting ongoing projects and strategic priorities for QC-HPC integration at leading supercomputing centers.
QuBound is a data-driven workflow that decomposes historical quantum performance traces to isolate noise sources, embeds circuit and noise data via a novel encoder, and uses LSTM to predict computational performance bounds under fluctuating noise. It outperforms state-of-the-art learning-based predictors, which produce single values falling outside QuBound's bounds, and analytical methods with 10x narrower ranges. QuBound achieves over 10^6 speedup versus noisy simulation while enabling efficient noise characterization for quantum system management like job scheduling.
Researchers introduce Quantum Properties Trojans (QuPTs), novel backdoor attacks on fully quantum neural networks (QNNs) that leverage unitary properties of quantum gates for noise insertion and Hadamard gates to induce superposition. These QuPTs achieve superior stealthiness compared to prior methods while severely degrading QNN performance, with the most effective variant reducing binary classifier accuracy by 23% in experiments. This marks the first Trojan attack on purely quantum architectures, independent of hybrid classical-quantum designs.
Hybrid quantum machine learning with quantum kernel-based SVM exceeds classical ML in classifying emotions from physiological signals of wearable sensors. Achieves F1 scores over 80% across all emotion categories, with up to 36% recall improvement on limited datasets. Offers privacy-preserving, unobtrusive monitoring for ADRD and PTSD patients in clinical settings.
FluxTrap is a SIMD-aware compiler framework for modular trapped-ion quantum machines, unifying segmented intra-trap shift SIMD (S3) and global junction transfer SIMD (JT-SIMD) operations via a SIMD-enriched architectural graph that accounts for transport synchronization, gate-zone locality, and topological constraints. It employs SIMD aggregation and scheduling passes to optimize grouped ion transport and gate execution. On NISQ benchmarks, it achieves up to 3.82x reduction in execution time and orders-of-magnitude fidelity improvements, while scaling to fault-tolerant workloads and informing hardware design.
OneAdapt introduces a novel intermediate representation (IR) and optimization passes for measurement-based quantum computing (MBQC) tailored to resource-constrained photonic platforms. The compiler adaptively minimizes required hardware size and execution time while enforcing user-defined limits on fusion devices. It integrates with quantum error correction to enhance efficiency in photonic fault-tolerant quantum computing.
Flexion proposes a hybrid encoding for trapped-ion quantum computers using bare physical qubits for high-fidelity single-qubit gates and QEC-encoded logical qubits only for two-qubit gates, avoiding full encoding overheads like gate synthesis and magic state distillation. It introduces a low-noise bare-to-logical conversion protocol, a tailored hybrid ISA for 2D grid TIQC, and an optimizing compiler that minimizes conversions and improves scheduling. Evaluations on VQA and FTQC benchmarks demonstrate superior performance with reduced resource demands, paving the way for megaquop-scale fault-tolerant computing.
XACC provides a service-oriented, system-level software infrastructure for heterogeneous quantum-classical computing, shifting from high-level REST APIs to low-level co-processor models. It exposes modular interfaces for quantum programming, compilation, and execution, remaining hardware-agnostic for both NISQ and future architectures. The framework enables tight integration of quantum and classical workflows, demonstrated through paradigmatic tasks, and supports development of compilers and runtimes.
Hybrid models integrate deterministic classical algorithms with quantum computing to tackle combinatorial complexity in large-scale mixed-integer programming. Applied to molecular conformation, job-shop scheduling, manufacturing cell formation, and vehicle routing problems, these approaches yield superior solution quality and computation time. Results demonstrate that leveraging quantum features complements classical methods for computationally challenging instances.
Researchers developed a VQE-based quantum chemistry benchmark incorporating active space reduction, reduced unitary coupled cluster ansatz, and McWeeny density purification for NISQ devices. Simulations of alkali metal hydrides (NaH, KH, RbH) on IBM Tokyo (20 qubits) and Rigetti Aspen (16 qubits) highlight characteristic high noise in superconducting hardware. Post-processing error mitigation, particularly density purification, dramatically improves ground-state energy accuracy, enabling chemical accuracy for specific settings via cloud access.
Quantum annealing on D-Wave 2000Q solves Nurse Scheduling Problem (NSP) instances by mapping to an Ising-type Hamiltonian, yielding solutions that satisfy hard constraints. Empirical tests show the method produces diverse, practical solutions. Reverse annealing significantly improves solution quality by refining initial results through a second annealing pass.
Continuous-time quantum walks (CTQWs) extend from static to dynamic graphs, where a sequence of graphs drives the walk's free evolution. Perfect state transfer in these walks designs dynamic graphs implementing a universal set of quantum logic gates, demonstrated for a complete logical basis. Numerical simulations validate implementations for quantum teleportation and addition circuits. Realization is feasible using actively controlled quantum optical waveguides.
Presents an iterative quantum algorithm for maximizing a function in dynamic models where prior search results refine the acceptable input set. Builds on quantum search with a dynamic oracle that marks items based on updated constraints. Demonstrates correctness via numerical simulations of quantum circuits for the Knapsack problem using explicit arithmetic oracles and comparators up to 30 qubits.
Researchers introduce a direct quantum annealing method to solve general polynomial equation systems, bypassing iterative solvers' variable convergence tied to condition numbers. Validated on second-order polynomials using a commercial annealer, it applies to linear regression and scales with problem size, condition number, and precision. An iterative annealing variant achieves 10^{-8} tolerance for linear systems.