Carbon Emissions of Quantum Circuit Simulation: More than You Would
Think
- URL: http://arxiv.org/abs/2307.05510v1
- Date: Tue, 4 Jul 2023 15:26:39 GMT
- Title: Carbon Emissions of Quantum Circuit Simulation: More than You Would
Think
- Authors: Jinyang Li, Qiang Guan, Dingwen Tao, Weiwen Jiang
- Abstract summary: We introduce for the first time the concept of environmental impact from quantum circuit simulation.
Our results indicate that large quantum circuit simulations (43 qubits) could lead to CO2e emissions 48 times greater than training a transformer machine learning model.
- Score: 5.509268096392859
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The rapid advancement of quantum hardware brings a host of research
opportunities and the potential for quantum advantages across numerous fields.
In this landscape, quantum circuit simulations serve as an indispensable tool
by emulating quantum behavior on classical computers. They offer easy access,
noise-free environments, and real-time observation of quantum states. However,
the sustainability aspect of quantum circuit simulation is yet to be explored.
In this paper, we introduce for the first time the concept of environmental
impact from quantum circuit simulation. We present a preliminary model to
compute the CO2e emissions derived from quantum circuit simulations. Our
results indicate that large quantum circuit simulations (43 qubits) could lead
to CO2e emissions 48 times greater than training a transformer machine learning
model.
Related papers
- Quantum Tunneling: From Theory to Error-Mitigated Quantum Simulation [49.1574468325115]
This study presents the theoretical background and the hardware aware circuit implementation of a quantum tunneling simulation.
We use error mitigation techniques (ZNE and REM) and multiprogramming of the quantum chip for solving the hardware under-utilization problem.
arXiv Detail & Related papers (2024-04-10T14:27:07Z) - QuantumSEA: In-Time Sparse Exploration for Noise Adaptive Quantum
Circuits [82.50620782471485]
QuantumSEA is an in-time sparse exploration for noise-adaptive quantum circuits.
It aims to achieve two key objectives: (1) implicit circuits capacity during training and (2) noise robustness.
Our method establishes state-of-the-art results with only half the number of quantum gates and 2x time saving of circuit executions.
arXiv Detail & Related papers (2024-01-10T22:33:00Z) - Quantum data learning for quantum simulations in high-energy physics [55.41644538483948]
We explore the applicability of quantum-data learning to practical problems in high-energy physics.
We make use of ansatz based on quantum convolutional neural networks and numerically show that it is capable of recognizing quantum phases of ground states.
The observation of non-trivial learning properties demonstrated in these benchmarks will motivate further exploration of the quantum-data learning architecture in high-energy physics.
arXiv Detail & Related papers (2023-06-29T18:00:01Z) - Scalable Simulation of Quantum Measurement Process with Quantum
Computers [13.14263204660076]
We propose qubit models to emulate the quantum measurement process.
One model is motivated by single-photon detection and the other by spin measurement.
We generate Schr"odinger cat-like state, and their corresponding quantum circuits are shown explicitly.
arXiv Detail & Related papers (2022-06-28T14:21:43Z) - Recompilation-enhanced simulation of electron-phonon dynamics on IBM
Quantum computers [62.997667081978825]
We consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems.
We perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling.
Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation.
arXiv Detail & Related papers (2022-02-16T19:00:00Z) - Towards Quantum Simulations in Particle Physics and Beyond on Noisy
Intermediate-Scale Quantum Devices [1.7242431149740054]
We review two algorithmic advances that bring us closer to reliable quantum simulations of model systems in high energy physics.
The first method is the dimensional expressivity analysis of quantum circuits, which allows for constructing minimal but maximally expressive quantum circuits.
The second method is an efficient mitigation of readout errors on quantum devices.
arXiv Detail & Related papers (2021-10-07T22:13:37Z) - Quantum walk processes in quantum devices [55.41644538483948]
We study how to represent quantum walk on a graph as a quantum circuit.
Our approach paves way for the efficient implementation of quantum walks algorithms on quantum computers.
arXiv Detail & Related papers (2020-12-28T18:04:16Z) - Optimal quantum simulation of open quantum systems [1.9551668880584971]
Digital quantum simulation on quantum systems require algorithms that can be implemented using finite quantum resources.
Recent studies have demonstrated digital quantum simulation of open quantum systems on Noisy Intermediate-Scale Quantum (NISQ) devices.
We develop quantum circuits for optimal simulation of Markovian and Non-Markovian open quantum systems.
arXiv Detail & Related papers (2020-12-14T14:00:36Z) - Engineering analog quantum chemistry Hamiltonians using cold atoms in
optical lattices [69.50862982117127]
We benchmark the working conditions of the numerically analog simulator and find less demanding experimental setups.
We also provide a deeper understanding of the errors of the simulation appearing due to discretization and finite size effects.
arXiv Detail & Related papers (2020-11-28T11:23:06Z) - Bit-Slicing the Hilbert Space: Scaling Up Accurate Quantum Circuit
Simulation to a New Level [10.765480856320018]
We enhance quantum circuit simulation in two dimensions: accuracy and scalability.
Experimental results demonstrate that our method can be superior to the state-of-the-art for various quantum circuits.
arXiv Detail & Related papers (2020-07-18T01:26:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.