arXiv subjects

Thomas Germain

Publications and source records attributed to Thomas Germain.

4 recordsLinked to original sources

arXiv2026

Python in the front, party in the Backline: compiling quantum workloads across CPUs, GPUs, and FPGAs

Moving from quantum research and development to production-grade, fault-tolerant quantum workload execution remains one of the most significant challenges facing quantum platform builders. While Python frameworks have enabled an easy entry point for quantum algorithm design, the low-latency requirements for real-time quantum error correction (QEC) demand performance that traditional interpreted environments cannot provide. FPGAs and ASICs play a central role at these layers, but their specialized programming models make development rigid and time-consuming. CPUs, GPUs, and other accelerators introduce a different challenge: as infrastructure becomes increasingly heterogeneous, programming across different devices and their associated abstractions becomes more complex. Allowing researchers to write workloads in high-level languages that map to low-latency execution across diverse distributed target platforms will enable the development of key infrastructure for utility-scale quantum systems. For this, we introduce $\textit{Backline}$, a heterogeneous compilation and runtime framework built within PennyLane and Catalyst. Backline allows us to design and build quantum-classical workloads for high-performance and low-latency devices, with compilation directly from a Python interface through MLIR. We demonstrate the compilation and execution of several quantum workloads with low-latency data movement across a mix of CPUs, GPUs, and FPGAs, for both local and distributed remote hardware targets, all from a vendor-agnostic Python frontend. With an AMD VPK120 FPGA board as the controller, issuing each round from its hardware-handshake engine, we measured median steady-state round-trip latencies over RoCE v2 of $2.305~\mu$s to an AMD Ryzen Threadripper PRO CPU and $4.5~\mu$s to an AMD Instinct MI210 GPU across $10^6-1$ rounds per path, demonstrating microsecond-scale synchronous co-processing.

quant-ph↗

arXiv2026

Practical Estimation of Trotter Error for Hamiltonian Simulation

Trotter product formulas are a leading approach for Hamiltonian simulation on quantum computers, yet their practical performance has remained difficult to assess due to the challenge of accurately estimating the Trotter error. In this work, we develop new theoretical results, algorithms, and software tools that advance the state-of-the-art in Trotter error estimation by orders of magnitude in both scale and accuracy. On the theoretical side, we prove that in the asymptotic limit the error of a product formula depends on the diagonal elements of the Baker-Campbell-Hausdorff (BCH) error operator in the eigenbasis of the Hamiltonian, rather than its full spectral norm -- yielding an improved scaling for Hamiltonian simulation using product formulas. On the algorithmic side, we introduce a compact representation of the BCH expansion that reduces the number of commutators from $\mathcal{O}(n^3)$ to $\mathcal{O}(n)$ for second-order, and from $\mathcal{O}(n^5)$ to $\mathcal{O}(n^2)$ for fourth-order formulas on $n$ fragments, complemented by an importance sampling scheme to further reduce the computational cost. We provide implementations of these techniques in software and demonstrate their power on two applications: (i) X-ray absorption spectroscopy of an electronic Hamiltonian (Li$_4$Mn$_2$O) at up to 56 qubits using tensor networks; and (ii) vibronic dynamics of naphthalene at over 100 qubits using ML-MCTDH, where we find that naive analytical bounds overestimate the required number of Trotter steps by nearly five orders of magnitude. Our framework enables, for the first time, the accurate estimation of Trotter error at practically relevant system sizes, providing a foundation for fair algorithmic comparisons and rational design of product formulas.

quant-ph↗

arXiv2024

Solving the Turbine Balancing Problem using Quantum Annealing

Quantum computing has the potential for disruptive change in many sectors of industry, especially in materials science and optimization. In this paper, we describe how the Turbine Balancing Problem can be solved with quantum computing, which is the NP-hard optimization problem of analytically balancing rotor blades in a single plane as found in turbine assembly. Small yet relevant instances occur in industry, which makes the problem interesting for early quantum computing benchmarks. We model it as a Quadratic Unconstrained Binary Optimization problem and compare the performance of a classical rule-based heuristic and D-Wave Systems' Quantum Annealer Advantage_system4.1. In this case study, we use real-world as well as synthetic datasets and observe that the quantum hardware significantly improves an actively used heuristic's solution for small-scale problem instances with bare disk imbalance in terms of solution quality. Motivated by this performance gain, we subsequently design a quantum-inspired classical heuristic based on simulated annealing that achieves extremely good results on all given problem instances, essentially solving the optimization problem sufficiently well for all considered datasets, according to industrial requirements.

quant-ph↗

arXiv2024

Sen2Chain: An Open-Source Toolbox for Processing Sentinel-2 Satellite Images and Producing Time-Series of Spectral Indices

The increasing availability of free high-resolution earth observation data covering any point on the globe every few days led to the emergence of new remote sensing tools that can manipulate the very large volumes of data generated by those satellites. We present Sen2Chain, an open-source Python tool that can automate the processing of large time series of Sentinel-2 images for their use in various fields (e.g., environmental health, natural hazards, ecology). Sen2Chain allows downloading images from various earth observation data suppliers, applying geometric and atmospheric corrections using ESA's Sen2Cor tool, and generating and applying cloud masks. Sen2Chain's ability to extract time series of spectral indices (e.g NDVI, NDWI) provides simplified access to value-added environmental information for a wide range of end-users and applications. Sen2Chain enables all data processing stages to be customized and chained together, with the possibility to automate and parallelize the processing, and optimize data management. Sen2Chain is paving the way for the creation and processing of a large earth observation image database dedicated to users who require time-series and/or perform regular environmental observations. The Web tool Sen2Extract is also presented, which enables end-users with no expertise in remote sensing to easily extract time-series for 11 spectral indices values for specific regions of interest.

physics.geo-ph↗