# IBM Quantum Computers: Evolution, Performance, and Future Directions

arXiv: https://arxiv.org/abs/2410.00916 · HTML: https://mlsanity.com/paper/2410.00916/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 70 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 8 of 8)

- [Variational Quantum Algorithms](https://mlsanity.com/paper/2012.09265/) (2020)
- [Supervised learning with quantum enhanced feature spaces](https://mlsanity.com/paper/1804.11326/) (2018)
- [The power of quantum neural networks](https://mlsanity.com/paper/2011.00027/) (2020)
- [Power of data in quantum machine learning](https://mlsanity.com/paper/2011.01938/) (2020)
- [Learning to Prescribe Interventions for Tuberculosis Patients Using Digital Adherence Data](https://mlsanity.com/paper/1902.01506/) (2019)
- [Quantum Computing at the Frontiers of Biological Sciences](https://mlsanity.com/paper/1911.07127/) (2019)
- [Quantum Artificial Life in an IBM Quantum Computer](https://mlsanity.com/paper/1711.09442/) (2017)
- [Comprehensive characterization of three-qubit Grover search algorithm on IBM's 127-qubit superconducting quantum computers](https://mlsanity.com/paper/2406.16018/) (2024)

## Built on by (most cited first, 11 of 11)

- [Quantum Federated Learning: A Comprehensive Survey](https://mlsanity.com/paper/2508.15998/) (2025)
- [Quantum autoencoders for image classification](https://mlsanity.com/paper/2502.15254/) (2025)
- [Differentially Private Federated Quantum Learning via Quantum Noise](https://mlsanity.com/paper/2508.20310/) (2025)
- [Hybrid Quantum Neural Networks for Efficient Protein-Ligand Binding Affinity Prediction](https://mlsanity.com/paper/2509.11046/) (2025)
- [Scalable Quantum Reinforcement Learning on NISQ Devices with Dynamic-Circuit Qubit Reuse and Grover Optimization](https://mlsanity.com/paper/2509.16002/) (2025)
- [Enhanced Prediction of CAR T-Cell Cytotoxicity with Quantum-Kernel Methods](https://mlsanity.com/paper/2507.22710/) (2025)
- [The Efficiency Frontier: Classical Shadows versus Direct Quantum Measurement](https://mlsanity.com/paper/2509.06218/) (2025)
- [Data-Driven Learnability Transition of Measurement-Induced Entanglement](https://mlsanity.com/paper/2512.01317/) (2025)
- [Q-Tag: Watermarking Quantum Circuit Generative Models](https://mlsanity.com/paper/2602.23085/) (2026)
- [Conformalized Quantum DeepONet Ensembles: Towards Scalable Operator Learning with Distribution-Free Guarantees](https://mlsanity.com/paper/2605.00330/) (2026)
- [Characterizing Privacy Risks of Quantum Machine Learning with Emergent Quantum-Native Access](https://mlsanity.com/paper/2609.05702/) (2026)

## Abstract (arXiv)

Quantum computers represent a transformative frontier in computational technology, promising exponential speedups beyond classical computing limits. IBM Quantum has led significant advancements in both hardware and software, providing access to quantum hardware via IBM Cloud since 2016, achieving a milestone with the world's first accessible quantum computer. This article explores IBM's quantum computing journey, focusing on the development of practical quantum computers. We summarize the evolution and advancements of IBM Quantum's processors across generations, including their recent breakthrough surpassing the 1,000-qubit barrier. The paper reviews detailed performance metrics across various hardware, tracing their evolution over time and highlighting IBM Quantum's transition from the noisy intermediate-scale quantum (NISQ) computing era towards fault-tolerant quantum computing capabilities.