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Sam Nerenberg

Publications and source records attributed to Sam Nerenberg.

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Energy efficiency of quantum computers

How much energy does a quantum computer consume? Are they more efficient than their classical counterparts? In this work, we make a step towards answering these questions. We define the energy efficiency of a quantum computer as the ratio of the number of algorithms it can perform during a given time over the energy consumed by the hardware during this time. We analyze the most representative physical platforms currently envisioned to be used as building blocks of quantum computers: superconducting qubits, silicon spin qubits, trapped ions, neutral atoms and photonic qubits. Including insights from experts in all these technologies and taking into account algorithm compilation constraints, we discuss the advantages and inconveniences of each platform from an energy standpoint. Beyond providing concrete values of the energy consumption of current quantum computers, we lay the foundation of a framework to benchmark the energy efficiency of any future quantum computing architecture.

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Photon Number-Resolving Quantum Reservoir Computing

Neuromorphic processors improve the efficiency of machine learning algorithms through the implementation of physical artificial neurons to perform computations. However, whilst efficient classical neuromorphic processors have been demonstrated in various forms, practical quantum neuromorphic platforms are still in the early stages of development. Here we propose a fixed optical network for photonic quantum reservoir computing that is enabled by photon number-resolved detection of the output states. This significantly reduces the required complexity of the input quantum states while still accessing a high-dimensional Hilbert space. The approach is implementable with currently available technology and lowers the barrier to entry to quantum machine learning.

quant-ph