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quant-ph2026

Scalable linearized gate set tomography

Ashe Miller, Corey Ostrove, Jordan Hines +4

Characterizing errors on many-qubit quantum computers remains a key challenge to understanding and improving the performance of these devices. Current characterization methods eith…

quant-ph2026

Simulating Quantum Error Correction beyond Pauli Stochastic Errors

Jordan Hines, Corey Ostrove, Kenneth Rudinger +4

Quantum error correction (QEC), the lynchpin of fault-tolerant quantum computing (FTQC), is designed and validated against well-behaved Pauli stochastic error models. But in real-w…

quant-ph2025

Software for Creating Scalable Benchmarks from Quantum Algorithms

Noah Siekierski, Stefan Seritan, Neer Patel +3

Creating scalable, reliable, and well-motivated benchmarks for quantum computers is challenging: straightforward approaches to benchmarking suffer from exponential scaling, are ins…

quant-ph2025

Platform-Agnostic Modular Architecture for Quantum Benchmarking

Neer Patel, Anish Giri, Hrushikesh Pramod Patil +6

We present a platform-agnostic modular architecture that addresses the increasingly fragmented landscape of quantum computing benchmarking by decoupling problem generation, circuit…

quant-ph2025

Benchmarking quantum computers with any quantum algorithm

Stefan K. Seritan, Aditya Dhumuntarao, Aidan Q. Wilber-Gauthier +5

Application-based benchmarks are increasingly used to quantify and compare quantum computers' performance. However, because contemporary quantum computers cannot run utility-scale…

quant-ph2025

Featuremetric benchmarking: Quantum computer benchmarks based on circuit features

Timothy Proctor, Anh Tran, Xingxin Liu +4

Benchmarks that concisely summarize the performance of many-qubit quantum computers are essential for measuring progress towards the goal of useful quantum computation. In this wor…