# Resource-Efficient Quantum Computing by Breaking Abstractions

arXiv: https://arxiv.org/abs/2011.00028 · HTML: https://mlsanity.com/paper/2011.00028/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2020.

## Built on by (most cited first, 4 of 4)

- [qgym: A Gym for Training and Benchmarking RL-Based Quantum Compilation](https://mlsanity.com/paper/2308.02536/) (2023)
- [Differentiable Analog Quantum Computing for Optimization and Control](https://mlsanity.com/paper/2210.15812/) (2022)
- [Training Quantum Boltzmann Machines with Coresets](https://mlsanity.com/paper/2307.14459/) (2023)
- [AutoQuREO: A Framework for Automated Quantum Resource Estimation and Optimization](https://mlsanity.com/paper/2608.12936/) (2026)

## Abstract (arXiv)

Building a quantum computer that surpasses the computational power of its classical counterpart is a great engineering challenge. Quantum software optimizations can provide an accelerated pathway to the first generation of quantum computing applications that might save years of engineering effort. Current quantum software stacks follow a layered approach similar to the stack of classical computers, which was designed to manage the complexity. In this review, we point out that greater efficiency of quantum computing systems can be achieved by breaking the abstractions between these layers. We review several works along this line, including two hardware-aware compilation optimizations that break the quantum Instruction Set Architecture (ISA) abstraction and two error-correction/information-processing schemes that break the qubit abstraction. Last, we discuss several possible future directions.