arXiv subjects

Annie Paine

Publications and source records attributed to Annie Paine.

3 recordsLinked to original sources

arXiv2026

OpenQARP: a modular framework for quantum application research

We introduce OpenQARP, the Open Quantum Application Research Package: an open-source Python framework for quantum application research, built on a compiled C++ core. In OpenQARP, an application is assembled from an algorithm library that orchestrates three interchangeable layers: blocks describe circuits, primitives describe what to extract from them, and engines describe how they run. The release spans near-term through fault-tolerant methods, targeting problems from electronic structure to combinatorial optimization, with device-aware compilation, circuit cutting, and resource estimation over a single set of numerical conventions that the code must follow. Every numerical feature is checked against an independent oracle, and every published timing carries a correctness check. Against OpenFermion, Qiskit/Aer, PennyLane/Lightning, Qulacs, qsim, and pytket, OpenQARP is one to two orders of magnitude faster on operator algebra and among the fastest on simulation. It matches mature compilers on two-qubit gate count, and expresses a complete algorithm in the lines a framework needs rather than the plumbing a primitive stack demands.

quant-ph↗

arXiv2026

Benchmarking a machine-learning differential equations solver on a neutral-atom logical processor

We report on a performance comparison between physical and logical computations on a prototypical machine-learning application: solving differential equations using quantum kernel methods. The algorithm is implemented on an atom-based logical quantum processor, both at the physical and logical levels. We show that the kernel estimated from the logical implementation performs better than its physical counterpart on relevant metrics. We observe how such performance improvement can be traced back to specific noise-induced errors detected by the chosen encoding. We apply the computed quantum kernel to the task of solving differential equations, confirming how the superior performance of a logical quantum kernel is retained also at an end-to-end applicative level. Our findings show that experimental validation of end-to-end protocols can already highlight the positive impact of fault-tolerant implementations despite their higher quantum resource count, and guide application-informed architectural choices.

quant-ph↗

arXiv2025

Differential equation quantum solvers: engineering measurements to reduce cost

Quantum computers have been proposed as a solution for efficiently solving non-linear differential equations (DEs), a fundamental task across diverse technological and scientific domains. However, a crucial milestone in this regard is to design protocols that are hardware-aware, making efficient use of limited available quantum resources. We focus here on promising variational methods derived from scientific machine learning: differentiable quantum circuits (DQC), addressing specifically their cost in number of circuit evaluations. Reducing the number of quantum circuit evaluations is particularly valuable in hybrid quantum/classical protocols, where the time required to interface and run quantum hardware at each cycle can impact the total wall-time much more than relatively inexpensive classical post-processing overhead. Here, we propose and test two sample-efficient protocols for solving non-linear DEs, achieving exponential savings in quantum circuit evaluations. These protocols are based on redesigning the extraction of information from DQC in a ``measure-first" approach, by introducing engineered cost operators similar to the randomized-measurement toolbox (i.e. classical shadows). In benchmark simulations on one and two-dimensional DEs, we report up to $\sim$ 100 fold reductions in circuit evaluations. Our protocols thus hold the promise to unlock larger and more challenging non-linear differential equation demonstrations with existing quantum hardware.

quant-ph↗