On the Fundamental Resource for Exponential Advantage in Quantum Channel Learning
Minsoo Kim, Changhun Oh

TL;DR
This paper analyzes the distinct roles of entanglement and ancilla qubits in quantum channel learning, revealing that quantum memory dimension is crucial for exponential learning advantage, while entanglement alone is insufficient.
Contribution
It distinguishes between entanglement and ancilla qubits as resources, showing the importance of quantum memory dimension over entanglement in quantum learning.
Findings
Small entanglement suffices for polynomial sample complexity in Pauli channel learning.
Insufficient ancilla qubits lead to exponential sample complexity for partial channel information.
Quantum memory dimension is a key resource for exponential learning advantage.
Abstract
Quantum resources enable us to achieve an exponential advantage in learning the properties of unknown physical systems by employing quantum memory. While entanglement with quantum memory is recognized as a necessary qualitative resource, its quantitative role remains less understood. In this work, we distinguish between two fundamental resources provided by quantum memory -- entanglement and ancilla qubits -- and analyze their separate contributions to the sampling complexity of quantum learning. Focusing on the task of Pauli channel learning, a prototypical example of quantum channel learning, remarkably, we prove that vanishingly small entanglement in the input state already suffices to accomplish the learning task with only a polynomial number of channel queries in the number of qubits. In contrast, we show that without a sufficient number of ancilla qubits, even learning partial…
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