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Ugo Varetto

Publications and source records attributed to Ugo Varetto.

13 recordsLinked to original sources

QSimAdv: A Late-Bound, Vendor-Agnostic Architecture for High-Performance Quantum-Circuit Simulation

Portability in high-performance quantum-circuit simulation need not begin at the kernel. We present QSimAdv, which makes late binding, rather than a common kernel, the basis of vendor independence. Representation, operator lowering, and data placement are bound only when their required inputs become available. Before full-state allocation, circuit, noise, and output inspection can route eligible generic sampled-count requests to a stabiliser tableau; explicitly requested representations remain fixed. For full-state execution, backend constraints shape fusion; an ordered fused operator binds to a native lowering only after its physical targets are known. A first-class logical-to-physical layout map records non-canonical order across local and rank-address bits, so the dispatcher moves nonlocal targets only on demand. GPU, CPU, and Message Passing Interface (MPI) backends share these semantics while retaining native execution paths. We realize this design on NVIDIA GH200 and AMD MI250X/EPYC systems across local and distributed execution. With matched complex 32-bit floating-point state storage, QSimAdv leads both Aer Hopper configurations at $N=32$ and Aer's HIP backend at four shared MI250X sizes from $N=24$ to 30. Strong scaling exposes platform dependence: on setonix, QSimAdv leads both GPU and CPU comparisons at every measured rank, achieving $3.4\times$ and $2.8\times$ speedups, respectively, from one to eight ranks; neither the GH200 path nor the CPU path speeds up at eight ranks. Weak scaling reaches 256 ranks with 2 TiB GPU and 1 TiB CPU states. Together, these results support that portability can reside above the kernel boundary while execution remains native and extends across distributed memory.

quant-ph

Quantum walk-based optimisation for capacitated vehicle routing with homogeneous and heterogeneous fleets

The capacitated vehicle routing problem (CVRP) is an appealing candidate for quantum optimisation due to its combinatorial complexity and practical importance. However, the problem's constrained search space poses a challenge for such quantum algorithms. We introduce a quantum walk-based optimisation algorithm (QWOA) for the CVRP with homogeneous or heterogeneous vehicle fleets, addressing this challenge through a continuous-time quantum walk over a product space that coincides with combinatorial structures intrinsic to the CVRP solution space. Relative to the prior QWOA-based formulation, this approach reduces the per-layer gate complexity from $\mathcal{O}(n^{3}\log n)$ to $\mathcal{O}(n^{2}\log n)$ and supports a circuit parameterisation schedule generated by a fixed number of classical parameters. Exact state-vector simulation on instances with up to $n=8$ customers and $K=3$ vehicles demonstrates improved convergence to low-cost solutions using markedly fewer objective function evaluations, with the advantage broadening as problem size increases. These results identify structured product-space walks as a promising tool for optimisation over constrained combinatorial spaces.

quant-ph

Distributed Variational Quantum Optimisation by Entanglement-Selective Transport

Distributed quantum optimisation is challenging because computing the problem cost function across multiple quantum processors requires non-local gates, which can incur overhead in latency and fidelity. Here we introduce QESTO, a distributed variational ansatz for graph-based discrete optimisation that requires only persistent pre-shared Bell pairs for remote operations. Using local operations, it encodes local constraint information in the Bell pairs that is leveraged to produce amplitude transfer towards globally valid distributed solution states. QESTO requires one Bell pair per distributed edge of the problem graph and, after initialisation of the Bell states, uses no non-local gates. On two bounded weighted Wang tile-matching problem ensembles, QESTO achieves stronger convergence to low-cost tilings than equivalently partitioned QAOA with no distributed gates at ansatz depths of two or higher, and exceeds the mean performance of monolithic QAOA at the deepest studied depth in both ensembles. These results suggest that persistent entanglement can support useful variational communication while reducing per-layer non-local gate overhead.

quant-ph

Continuous-time quantum walk-based ansätze on neutral atom hardware

Continuous-time quantum walks offer provable speedups for certain computational problems, yet translating these advantages to near-term hardware remains challenging. We realize variational ansätze based on continuous-time quantum walks on an analog neutral-atom processor. For unentangled targets, we derive closed-form expressions for near-optimal control parameters that transfer directly to hardware with minimal calibration. On QuEra's Aquila processor we observe the super-quadratic convergence characteristic of efficient quantum walk algorithms, visible at low circuit depth, with theory predicting stronger speedups as hardware improves. For entangled targets, specifically symmetric superpositions in the Rydberg-blockaded subspace, we introduce an optimization protocol exploiting spectral properties of the walk dynamics. The required evolution time scales inversely with the spectral gap, offering an advantage over adiabatic protocols, whose evolution time scales as the inverse square of the spectral gap. We verify this scaling behavior on Aquila and confirm that the prepared states are coherent superpositions via quench dynamics. Our results establish a practical pathway from abstract quantum walk algorithms to analog quantum processors, demonstrating that the dynamics underlying their potential for super-quadratic quantum speedup are accessible on current devices.

quant-ph

HPC-vQPU: A Service-Export Architecture for Virtual QPUs on Batch-Scheduled HPC Systems

Device-aware quantum simulation increasingly requires HPC-scale accelerators, yet secure supercomputers expose batch-scheduled execution environments rather than the interactive, backend-oriented interfaces expected by quantum software. The key obstacle is not only remote job submission: an HPC-hosted virtual QPU must preserve topology, native-gate, and calibration semantics across queue delay, scheduler allocation, compute-node isolation, and partial execution-side failures, without opening inbound paths into the cluster. We present HPC-vQPU, a service-export architecture for virtual QPUs on batch-scheduled HPC systems. HPC-vQPU separates a cloud-facing control plane, which owns device identity, task lifecycle, snapshot binding, and event projection, from an HPC-resident execution plane, which claims work and realises it through scheduler-backed GPU jobs. Coordination is exclusively outbound and agent initiated. The central abstraction is a topology- and calibration-aware device snapshot bound atomically at claim time and carried into execution as an immutable contract, making each scheduled job hermetic while preserving fresh device semantics. We implement HPC-vQPU at the Pawsey Supercomputing Research Centre using Setonix GPUs, Qiskit-Aer/cuQuantum, and IBM Fez calibration data. Production experiments show that service overhead is bounded and additive, while workload scaling remains confined to the simulator; calibration-bearing snapshots produce measurable output shifts; claim-time binding prevents stale execution after pre-claim device mutation; concurrent agents complete 50/50 tasks exactly once; and explicit recovery restores stale running tasks after agent failure. These results show that secure, scheduler-mediated HPC infrastructure can export device-faithful quantum simulation as an interactive virtual-QPU service.

cs.DC

DynQ: A Dynamic Topology-Agnostic Quantum Virtual Machine via Quality-Weighted Community Detection

Quantum cloud platforms have scaled hardware capacity but not the abstraction exposed to users: small programs still monopolise entire processors, and existing Quantum Virtual Machine (QVM) designs often rely on fixed, topology-specific partitions that are brittle under calibration drift, spatial heterogeneity, and transient defects. We present DynQ, a dynamic topology-agnostic QVM that derives execution regions directly from live calibration data. DynQ models a processor as a quality-weighted coupling graph and formulates region discovery as community detection, turning high internal cohesion and low external coupling into a hardware-aware objective for quantum virtualisation. This produces regions that are compilation-friendly, quality-aware, and resilient to degraded couplers and unavailable qubits. DynQ separates offline region discovery from online allocation, enabling low-latency scheduling over pre-validated regions while allowing recomputation under changing hardware conditions. Across five IBM backends, real-device experiments on IBM Kingston and Torino, and cross-architecture evaluation on Rigetti Ankaa-3 via AWS Braket, DynQ improves execution quality, recovers workloads lost under transient defects, and maintains stable output under concurrent batching. It reduces L1 error by up to 45.1% and improves output similarity by up to 19.1% on heterogeneous hardware, while eliminating observed baseline failures on real devices. These results position quantum virtualisation as a graph-driven systems problem and show that adaptive, quality-aware QVMs enable reliable multi-tenant quantum cloud services.

quant-ph

Quantum Reservoir Computing with Neutral Atoms on a Small, Complex, Medical Dataset

Biomarker-based prediction of clinical outcomes is challenging due to nonlinear relationships, correlated features, and the limited size of many medical datasets. Classical machine-learning methods can struggle under these conditions, motivating the search for alternatives. In this work, we investigate quantum reservoir computing (QRC), using both noiseless emulation and hardware execution on the neutral-atom Rydberg processor \textit{Aquila}. We evaluate performance with six classical machine-learning models and use SHAP to generate feature subsets. We find that models trained on emulated quantum features achieve mean test accuracies comparable to those trained on classical features, but have higher training accuracies and greater variability over data splits, consistent with overfitting. When comparing hardware execution of QRC to noiseless emulation, the models are more robust over different data splits and often exhibit statistically significant improvements in mean test accuracy. This combination of improved accuracy and increased stability is suggestive of a regularising effect induced by hardware execution. To investigate the origin of this behaviour, we examine the statistical differences between hardware and emulated quantum feature distributions. We find that hardware execution applies a structured, time-dependent transformation characterised by compression toward the mean and a progressive reduction in mutual information relative to emulation.

quant-ph

Quantum-Assisted Design of Space-Terrestrial Integrated Networks

Achieving ubiquitous global connectivity requires integrating satellite and terrestrial networks, particularly to serve remote and underserved regions. In this work, we investigate the design and optimization of Space-Terrestrial Integrated Networks (STINs) using a hybrid quantum-classical approach. We formalize three key combinatorial optimization problems: the Satellite Selection Problem (SSP), the Gateway Selection Problem (GSP), and the Spectrum Assignment Problem (SAP), each capturing critical aspects of network deployment and operation. Leveraging neutral-atom quantum processors, we map the SSP onto a Maximum Weight Independent Set problem, embedding it onto the Aquila platform and solving it via the Quantum Adiabatic Algorithm (QAA). Postprocessing ensures feasible solutions that guide downstream GSP and SAP optimization. Benchmarking across 165 realistic remote regions shows that QAA solutions closely match classical exact solvers and outperform greedy heuristics, while subsequent GSP and SAP outcomes remain largely robust to differences in initial satellite selection. These results demonstrate that quantum optimization achieves performance broadly comparable to classical approaches for end-to-end STIN design, with rare instances where it can even surpass state-of-the-art solvers. This suggests that, while not yet consistently superior, quantum methods may offer competitive advantages for larger or more complex instances of the underlying combinatorial subproblems.

quant-ph

The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute

Quantum computing (QC) has gained significant attention over the past two decades due to its potential for speeding up classically demanding tasks. This transition from an academic focus to a thriving commercial sector is reflected in substantial global investments. While advancements in qubit counts and functionalities continues at a rapid pace, current quantum systems still lack the scalability for practical applications, facing challenges such as too high error rates and limited coherence times. This perspective paper examines the relationship between QC and high-performance computing (HPC), highlighting their complementary roles in enhancing computational efficiency. It is widely acknowledged that even fully error-corrected QCs will not be suited for all computational task. Rather, future compute infrastructures are anticipated to employ quantum acceleration within hybrid systems that integrate HPC and QC. While QCs can enhance classical computing, traditional HPC remains essential for maximizing quantum acceleration. This integration is a priority for supercomputing centers and companies, sparking innovation to address the challenges of merging these technologies. The Accelerated Data Analytics and Computing Institute (ADAC) is comprised of globally leading HPC centers. ADAC has established a Quantum Computing Working Group to promote and catalyze collaboration among its members. This paper synthesizes insights from the QC Working Group, supplemented by findings from a member survey detailing ongoing projects and strategic directions. By outlining the current landscape and challenges of QC integration into HPC ecosystems, this work aims to provide HPC specialists with a deeper understanding of QC and its future implications for computationally intensive endeavors.

quant-ph

TRAM: A Transverse Relaxation Time-Aware Qubit Mapping Algorithm for NISQ Devices

Noisy intermediate-scale quantum (NISQ) devices impose dual challenges on quantum circuit execution: limited qubit connectivity requires extensive SWAP-gate routing, while time-dependent decoherence progressively degrades quantum information. Existing qubit mapping algorithms optimize for hardware topology and static calibration metrics but systematically neglect transverse relaxation dynamics (T2), creating a fundamental gap between compiler decisions and evolving noise characteristics. We present TRAM (Transverse Relaxation Time-Aware Qubit Mapping), a coherence-guided compilation framework that elevates decoherence mitigation to a primary optimization objective. TRAM integrates calibration-informed community detection to construct noise-resilient qubit partitions, generates time-weighted initial mappings that anticipate coherence decay, and dynamically schedules SWAP operations to minimize cumulative error accumulation. Evaluated on Qiskit-based simulators with realistic noise models, TRAM outperforms SABRE by 3.59% in fidelity, reduces gate count by 11.49%, and shortens circuit depth by 12.28%, establishing coherence-aware optimization as essential for practical quantum compilation in the NISQ era.

quant-ph

Accelerating cosmological simulations on GPUs: a step towards sustainability and green-awareness

The increasing complexity and scale of cosmological N-body simulations, driven by astronomical surveys like Euclid, call for a paradigm shift towards more sustainable and energy-efficient high-performance computing (HPC). The rising energy consumption of supercomputing facilities poses a significant environmental and financial challenge. In this work, we build upon a recently developed GPU implementation of pinocchio, a widely-used tool for the fast generation of dark matter (DM) halo catalogues, to investigate energy consumption. Using a different resource configuration, we confirmed the time-to-solution behavior observed in a companion study, and we use these runs to compare time-to-solution with energy-to-solution. By profiling the code on various HPC platforms with a newly developed implementation of the Power Measurement Toolkit (PMT), we demonstrate an 8x reduction in energy-to-solution and 8x speed-up in time-to-solution compared to the CPU-only version. Taken together, these gains translate into an overall efficiency improvement of up to 64x. Our results show that the GPU-accelerated pinocchio not only achieves substantial speed-up, making the generation of large-scale mock catalogues more tractable, but also significantly reduces the energy footprint of the simulations. This work represents an step towards ``green-aware" scientific computing in cosmology, proving that performance and sustainability can be simultaneously achieved.

astro-ph.IM

Green computing toward SKA era with RICK

Square Kilometer Array is expected to generate hundreds of petabytes of data per year, two orders of magnitude more than current radio interferometers. Data processing at this scale necessitates advanced High Performance Computing (HPC) resources. However, modern HPC platforms consume up to tens of M W , i.e. megawatts, and energy-to-solution in algorithms will become of utmost importance in the next future. In this work we study the trade-off between energy-to-solution and time-to-solution of our RICK code (Radio Imaging Code Kernels), which is a novel approach to implement the w-stacking algorithm designed to run on state-of-the-art HPC systems. The code can run on heterogeneous systems exploiting the accelerators. We did both single-node tests and multi-node tests with both CPU and GPU solutions, in order to study which one is the greenest and which one is the fastest. We then defined the green productivity, i.e. a quantity which relates energy-to-solution and time-to-solution in different code configurations compared to a reference one. Configurations with the highest green productivities are the most efficient ones. The tests have been run on the Setonix machine available at the Pawsey Supercomputing Research Centre (PSC) in Perth (WA), ranked as 28th in Top500 list, updated at June 2024.

cs.DC

A Generic Library for Stencil Computations

In this era of diverse and heterogeneous computer architectures, the programmability issues, such as productivity and portable efficiency, are crucial to software development and algorithm design. One way to approach the problem is to step away from traditional sequential programming languages and move toward domain specific programming environments to balance between expressivity and efficiency. In order to demonstrate this principle, we developed a domain specific C++ generic library for stencil computations, like PDE solvers. The library features high level constructs to specify computation and allows the development of parallel stencil computations with very limited effort. The high abstraction constructs (like do_all and do_reduce) make the program shorter and cleaner with increased contextual information for better performance exploitation. The results show good performance from Windows multicores, to HPC clusters and machines with accelerators, like GPUs.

cs.MS