Introduction to Quantum Gate Set Tomography
read the original abstract
Quantum gate set tomography (GST) has emerged as a promising method for the full characterization of quantum logic gates. In contrast to quantum process tomography (QPT), GST self-consistently and correctly accounts for state preparation and measurement (SPAM) errors. It therefore provides significantly more accurate estimates than QPT as gate fidelities increase into the fault-tolerant regime. We give a detailed review of GST and provide a self-contained guide to its implementation. The method is presented in a step-by-step fashion and relevant mathematical background material is included. Our goal is to demonstrate the utility of GST as both an accurate characterization technique and a simple and effective diagnostic tool. As an illustration, we compare the output of GST and QPT using simulated example data for a single qubit. In agreement with the original literature, we find that coherent errors are poorly estimated by QPT near quantum error correction thresholds, while GST is accurate in this regime.
This paper has not been read by Pith yet.
Forward citations
Cited by 6 Pith papers
-
From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data
A generative QMLC framework tokenizes GST data, embeds it via curriculum-trained set-vision transformers into a context-aware latent space, and uses diffusion models to synthesize circuits conditioned on desired measu...
-
Learning to Concatenate Quantum Codes
A machine-learning approach adaptively chooses quantum code sequences for concatenation to achieve target logical error rates with far fewer qubits than standard methods for structured noise.
-
Quantifying Irreversibility via Bayesian Subjectivity for Classical & Quantum Linear Maps
Irreversibility of linear maps is quantified via Bayesian subjectivity, defined as the sensitivity of retrodiction to the reference prior.
-
Benchmarking Single-Qubit Gates on a Neutral Atom Quantum Processor
DRB and GST benchmarking on neutral-atom single-qubit gates yields 99.963% average fidelity, with consistent results on a 25-qubit array and a new gauge optimization for GST.
-
Designing a Machine Learning-Driven, Cross-Hardware Emulator for Noisy Quantum Computers with Gate-Based Protocols
Supervised ML trained on simulated gate set tomography data predicts noise models to build cross-hardware quantum emulators, validated by matching H2 unitary coupled cluster energy results to real hardware within 0.12...
-
Understanding Quantum Instruments
Quantum instrument errors are represented by outcome-specific d²×d² superoperators, but the joint quantum-classical nature requires careful interpretation beyond standard process matrices.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.