Scalable randomized benchmarking with mid-circuit measurements
Abstract not provided.
Publications
Search results
Jump to search filtersScalable Benchmarking of Quantum Chemistry Algorithms using Circuit Mirroring
Abstract not provided.
Validating models of quantum computers in practice
Abstract not provided.
A Simple Statistical Model for Randomized Benchmarking Data
Abstract not provided.
Predictive Models from Quantum Computer Benchmarks
Abstract not provided.
Learning a quantum computer's capability using convolutional neural networks
Abstract not provided.
A physics-based machine learning approach to quantum device characterization
Abstract not provided.
Validating models of quantum computer performance
Abstract not provided.
Experimental demonstration of mirror circuit fidelity estimation
Abstract not provided.
Modeling Low-Frequency Hamiltonian Noise
Abstract not provided.
Quantum circuit debugging and sensitivity analysis via local inversions
Quantum
As the width and depth of quantum circuits implemented by state-of-the-art quantum processors rapidly increase, circuit analysis and assessment via classical simulation are becoming unfeasible. It is crucial, therefore, to develop new methods to identify significant error sources in large and complex quantum circuits. In this work, we present a technique that pinpoints the sections of a quantum circuit that affect the circuit output the most and thus helps to identify the most significant sources of error. The technique requires no classical verification of the circuit output and is thus a scalable tool for debugging large quantum programs in the form of circuits. We demonstrate the practicality and efficacy of the proposed technique by applying it to example algorithmic circuits implemented on IBM quantum machines.
Predicting the success rates of quantum circuits with artificial neural networks
Abstract not provided.
Validation of QCVV Models for Quantum Computers
Abstract not provided.
How to Benchmark Quantum Computers with Any Quantum Algorithm
Abstract not provided.
Predicting the success rates of quantum circuits with artificial neural networks
Abstract not provided.
Randomized benchmarking in the quantum advantage regime
Abstract not provided.
Quantum circuit debugging and sensitivity analysis via local inversions
Abstract not provided.
High-level benchmarks and low-level characterization
Abstract not provided.
Predicting circuit success rates with artificial neural networks
Abstract not provided.
A Taxonomy of Small Markovian Errors
PRX Quantum
Errors in quantum logic gates are usually modeled by quantum process matrices (CPTP maps). But process matrices can be opaque and unwieldy. We show how to transform the process matrix of a gate into an error generator that represents the same information more usefully. We construct a basis of simple and physically intuitive elementary error generators, classify them, and show how to represent the error generator of any gate as a mixture of elementary error generators with various rates. Finally, we show how to build a large variety of reduced models for gate errors by combining elementary error generators and/or entire subsectors of generator space. We conclude with a few examples of reduced models, including one with just 9N2 parameters that describes almost all commonly predicted errors on an N-qubit processor.
Learning the Capabilities of Quantum Computers
Abstract not provided.
How to benchmark a 100-qubit quantum computer using fewer than 100 circuits
Abstract not provided.
Connecting randomized benchmarking results to physical error rates
Abstract not provided.
Demonstrating scalable benchmarking of universal gate sets
Abstract not provided.
Scalable verification of quantum algorithm circuits
Abstract not provided.
Results 1–25 of 71