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T.B. Propp
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Blind Quantum Machine Learning
Transpilation, Resource Estimation, and Experimental Outlook
The growing societal demand for privacy, driven by rapid advances in information technologies and machine learning, motivates the development of approaches that reconcile privacy preservation with computational efficiency. This thesis addresses this challenge by bridging two seemingly disparate paradigms: universal blind quantum computation (UBQC), based on measurement-based quantum computation, and quantum machine learning (QML) algorithms such as the Harrow–Hassidim–Lloyd (HHL) algorithm and quantum recommendation systems.
To this end, we developed a transpiler that maps Qiskit quantum circuits into computational brickwork graphs (Graphix Pattern objects), the underlying resource states of UBQC. This enables systematic evaluation of the depth and cost incurred by blind implementations. From these constructions, we established a general upper bound on the depth scaling of the computational graph corresponding to blind algorithms as O(mn), where m is the circuit width and n its original complexity. Building on this framework, we provide detailed analyses of blind implementations of the quantum Fourier transform, HHL, recommendation systems, and quantum transformers.
Finally, the thesis proposes a minimal experimental design for Blind Quantum Machine Learning with resource estimates requiring a total of 750 remotely prepared qubit states, but with only 12 coherent qubits in memory at any time using a conveyor-belt architecture, making the resource requirements compatible with near-term implementations on a quantum internet.
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To this end, we developed a transpiler that maps Qiskit quantum circuits into computational brickwork graphs (Graphix Pattern objects), the underlying resource states of UBQC. This enables systematic evaluation of the depth and cost incurred by blind implementations. From these constructions, we established a general upper bound on the depth scaling of the computational graph corresponding to blind algorithms as O(mn), where m is the circuit width and n its original complexity. Building on this framework, we provide detailed analyses of blind implementations of the quantum Fourier transform, HHL, recommendation systems, and quantum transformers.
Finally, the thesis proposes a minimal experimental design for Blind Quantum Machine Learning with resource estimates requiring a total of 750 remotely prepared qubit states, but with only 12 coherent qubits in memory at any time using a conveyor-belt architecture, making the resource requirements compatible with near-term implementations on a quantum internet.
...
The growing societal demand for privacy, driven by rapid advances in information technologies and machine learning, motivates the development of approaches that reconcile privacy preservation with computational efficiency. This thesis addresses this challenge by bridging two seemingly disparate paradigms: universal blind quantum computation (UBQC), based on measurement-based quantum computation, and quantum machine learning (QML) algorithms such as the Harrow–Hassidim–Lloyd (HHL) algorithm and quantum recommendation systems.
To this end, we developed a transpiler that maps Qiskit quantum circuits into computational brickwork graphs (Graphix Pattern objects), the underlying resource states of UBQC. This enables systematic evaluation of the depth and cost incurred by blind implementations. From these constructions, we established a general upper bound on the depth scaling of the computational graph corresponding to blind algorithms as O(mn), where m is the circuit width and n its original complexity. Building on this framework, we provide detailed analyses of blind implementations of the quantum Fourier transform, HHL, recommendation systems, and quantum transformers.
Finally, the thesis proposes a minimal experimental design for Blind Quantum Machine Learning with resource estimates requiring a total of 750 remotely prepared qubit states, but with only 12 coherent qubits in memory at any time using a conveyor-belt architecture, making the resource requirements compatible with near-term implementations on a quantum internet.
To this end, we developed a transpiler that maps Qiskit quantum circuits into computational brickwork graphs (Graphix Pattern objects), the underlying resource states of UBQC. This enables systematic evaluation of the depth and cost incurred by blind implementations. From these constructions, we established a general upper bound on the depth scaling of the computational graph corresponding to blind algorithms as O(mn), where m is the circuit width and n its original complexity. Building on this framework, we provide detailed analyses of blind implementations of the quantum Fourier transform, HHL, recommendation systems, and quantum transformers.
Finally, the thesis proposes a minimal experimental design for Blind Quantum Machine Learning with resource estimates requiring a total of 750 remotely prepared qubit states, but with only 12 coherent qubits in memory at any time using a conveyor-belt architecture, making the resource requirements compatible with near-term implementations on a quantum internet.
Quantum Communication Complexity on Near-Term Networks
Solving the Equality Problem with Realistic Noise
Quantum computers allow us to solve certain problems that are unsolvable using classical computers. In this study we focus on solving the equality problem by simulating a three quantum computer network and using the communication complexity to determine if our theoretical quantum advantage is still there in practice. We want to know how the noise from realistic quantum networks that already exist affect this communication complexity. We found that we can beat the classical solution when simulating a laboratory setup in which the quantum computers are in close proximity to each other and when using only a small bit strings. However, when moving to setups in which there are kilometres between quantum computers instead of metres or when using larger bit strings as input to our problem we see that the noise becomes too much to simulate.
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Quantum computers allow us to solve certain problems that are unsolvable using classical computers. In this study we focus on solving the equality problem by simulating a three quantum computer network and using the communication complexity to determine if our theoretical quantum advantage is still there in practice. We want to know how the noise from realistic quantum networks that already exist affect this communication complexity. We found that we can beat the classical solution when simulating a laboratory setup in which the quantum computers are in close proximity to each other and when using only a small bit strings. However, when moving to setups in which there are kilometres between quantum computers instead of metres or when using larger bit strings as input to our problem we see that the noise becomes too much to simulate.