Optimizing resource efficiencies for scalable full-stack quantum computers

The preprint “Optimizing resource efficiencies for scalable full-stack quantum computers” submitted on September 12th, 2022, by Marco Fellous-Asiani, Jing Hao Chai, Yvain Thonnart, Hui Khoon Ng, Robert Whitney and Alexia Auffèves implements the Metric-Noise-Resource (MNR) methodology that helps quantify and optimize various figures of merit of a quantum computer with a full-stack view, including their energy consumption.

The paper showcases what kind of research efforts are promoted by the Quantum Energy Initiative. It consolidates a broad number of disciplines and technologies: quantum physics, quantum thermodynamics, control electronics, wiring, filtering, cryogeny, quantum error correction codes, system design, all in a systemic approach.

One key goal here is to ensure that the energy consumption of a quantum computer is constrained, reasonable and not a showstopper for scalability. The paper describes various findings, some of which may be counter-intuitive, with the example of large scale superconducting qubits based quantum computers, using a detailed full-stack analysis.

  1. It shows that design choices are twofold: either use room temperature control electronics and find solutions for cabling multiplexing, or, use cryogenic electronics and preferably so-called SFQ, superconducting electronics.
  2. Control electronics will be the main source of power consumption in scalable superconducting qubits quantum computers, before cryogeny.
  3. Many significant research and industry efforts will be necessary to enable large scale QC: better qubits, lower energy consuming control electronics, simpler cabling, higher cooling power, better yield cryostats, and efficient error correcting codes
  4. With NISQ quantum computers (noisy qubits quantum computers), an energy advantage may show up before a computing advantage. This adds to another potential benefit of NISQ systems which could bring better quality results than traditional computing like with quantum machine learning. #QEI

The NMR methodology is applicable to other types of qubits and even other programming paradigms (quantum annealing and quantum simulation).