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Data and simulation files for: "Estimating the electrical energy cost of performing arbitrary state preparation using qubits and qudits in integrated photonic circuits"

Maria Carolina Volpato, Pierre-Louis de Assis

Published Jan 1, 2024• 0 citations
Publisher

Abstract

Data and simulation files for: "Estimating the electrical energy cost of performing arbitrary state preparation using qubits and qudits in integrated photonic circuits"
DOI
10.5281/zenodo.13387079

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As quantum photonic hardware scales toward computationally relevant sizes, energy consumption has emerged as a key constraint. Programmable photonic integrated circuits, composed of interferometer meshes with tunable phase modulators, provide a flexible platform for quantum information processing using both qubits and qudits. In this work, we analyze the energetic cost of such devices by focusing on arbitrary quantum state preparation, a resource-intensive task central to quantum simulation and information processing. Using a common hardware, we benchmark qudit-based implementations, gate-based quantum computation, and measurement-based quantum computation. We find that while qudit encodings are attractive at small scale, their footprint and reconfiguration costs grow rapidly with system size, whereas qubit-based approaches incur significant overhead from entangling operations, feedforward, and reprogramming. Across all paradigms, scaling beyond a few tens of qubits renders either the energy consumption or the total preparation time prohibitive on fully programmable PICs. Our results highlight the need for optimized, task-specific photonic architectures to enable energy-efficient scaling.
Quantum amplitude estimation (QAE) can achieve a quadratic speedup for applications classically solved by Monte Carlo simulation. A key requirement to realize this advantage is efficient state preparation. If state preparation is too expensive, it can diminish the quantum advantage. Preparing arbitrary quantum states has exponential complexity with respect to the number of qubits, and thus, is not applicable. Currently known efficient techniques require problems based on log-concave probability distributions, involve learning an unknown distribution from empirical data, or fully rely on quantum arithmetic. In this paper, we introduce an approach to simplify state preparation, together with a circuit optimization technique, both of which can help reduce the circuit complexity for QAE state preparation significantly. We demonstrate the introduced techniques for a numerical integration example on real quantum hardware, as well as for option pricing under the Heston model, i.e., based on a stochastic volatility process, using simulation.