Is quantum computing green? An estimate for an energy-efficiency quantum advantage
arXiv:2205.12092 · doi:10.1088/2058-9565/acae3e
Abstract
The quantum advantage threshold determines when a quantum processing unit (QPU) is more efficient with respect to classical computing hardware in terms of algorithmic complexity. The "green" quantum advantage threshold based on a comparison of energetic efficiency between the two is going to play a fundamental role in the comparison between quantum and classical hardware. Indeed, its characterization would enable better decisions on energy-saving strategies, e.g. for distributing the workload in hybrid quantum-classical algorithms. Here, we show that the green quantum advantage threshold crucially depends on (i) the quality of the experimental quantum gates and (ii) the entanglement generated in the QPU. Indeed, for NISQ hardware and algorithms requiring a moderate amount of entanglement, a classical tensor network emulation can be more energy-efficient at equal final state fidelity than quantum computation. We compute the green quantum advantage threshold for a few paradigmatic examples in terms of algorithms and hardware platforms, and identify algorithms with a power-law decay of singular values of bipartitions with power-law exponent as the green quantum advantage threshold in the near future.
12 pages, 7 figures, 2 table; minor revisions in comparison to v1, e.g., one additional table
References in corpus (9)
- Supplementary information for "Quantum supremacy using a programmable superconducting processor"
- The density-matrix renormalization group in the age of matrix product states
- Quantum computational advantage using photons
- Quantum trajectories and open many-body quantum systems
- Quantum computing with neutral atoms
- A concise review of Rydberg atom based quantum computation and quantum simulation
- A density-matrix renormalization group algorithm for simulating quantum circuits with a finite fidelity
- Quantum Fourier Transform Has Small Entanglement
- Exascale Deep Learning for Scientific Inverse Problems
Cited by in corpus (17)
- Tensor networks for quantum machine learning
- Entanglement entropy production in Quantum Neural Networks
- Energy-Consumption Advantage of Quantum Computation
- How quantum computing can enhance biomarker discovery
- Strong Simulation of Linear Optical Processes
- Ab-initio tree-tensor-network digital twin for quantum computer benchmarking in 2D
- A Holistic Approach to Quantum Ethics Education
- Digital quantum simulation of lattice fermion theories with local encoding
- Quantum circuit compilation with quantum computers
- Observation of partial and infinite-temperature thermalization induced by repeated measurements on a quantum hardware
- The Role of Quantum Computing in Advancing Scientific High-Performance Computing: A perspective from the ADAC Institute
- Classical Half-Adder using Trapped-ion Quantum Bits: Towards Energy-efficient Computation
- Autonomous Quantum Processing Unit: An Autonomous Thermal Computing Machine & its Physical Limitations
- Sample Complexity of Black Box Work Extraction
- Harnessing Quantum Computing for Energy Materials: Opportunities and Challenges
- Full Quantum Work Statistics for Non-Homogeneous Many-Body Systems
- Hyperspectral image segmentation with a machine learning model trained using quantum annealer