Abstract

As the nascent quantum computing paradigm matures and quantum devices become widely available, discrete "eras" are emerging which characterize evolving quantum computing technologies, much like semiconductor technology node classifications. The current period has been coined the Noisy Intermediate-Scale Quantum (NISQ) era, as devices above 50 qubits in size exist but computations using these devices accumulate error quickly from classical interference sources and state decoherence. NISQ machines may surpass the capabilities of modern classical computers in ideal circumstances, but the accumulation of error from physical noise limits the size and implementability of reliable quantum algorithms. Because of these limitations, strategies are under development that can improve the results of computations on NISQ devices or identify characteristics of the accurate solution space that might be preserved in the noisy data. These are known as error mitigation strategies. One such method that has shown promise is the use of classical machine learning to extract information about the pre-measurement output of a NISQ device. This work proposes a new use of machine learning to identify the accurate solutions of basis-encoded quantum algorithms in the presence of noise. Methods of encoding the probabilistic solution space of a basis-encoded quantum algorithm are researched to identify the characteristics that represent good ML training inputs. A multilayer preceptron artificial neural network (MLP ANN) was trained on the results of 8-state and 16-state basis-encoded quantum algorithms both in the presence of noise and in noise-free simulation. It is demonstrated using simulated quantum hardware and probabilistic noise models that a sufficiently trained model may identify accurate solutions to quantum applications with over 90% precision and 80% recall on select data. The model makes confident predictions even with enough noise that the solutions cannot be determined by direct observation, and when it cannot, it can identify the inconclusive experiments as candidates for other error mitigation techniques.

Publication Date

5-2024

Document Type

Thesis

Student Type

Graduate

Degree Name

Computer Engineering (MS)

Department, Program, or Center

Computer Engineering

College

Kate Gleason College of Engineering

Advisor

Sonia Lopez Alarcon

Advisor/Committee Member

Andres Kwasinski

Advisor/Committee Member

Dongfang Liu

Campus

RIT – Main Campus

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