The Hubbard model in various sizes.

Quantum Zeno Monte Carlo for computing observables

The Quantum Zeno Monte Carlo algorithm bridges the gap between noisy intermediate-scale quantum and fault-tolerant quantum computing eras by offering polynomial computational complexity and resilience to both device noise and Trotter errors without requiring initial state overlap or variational parameters, as demonstrated on IBM’s NISQ devices with up to 12 qubits.

Conceptual plot of the non-thermal states classified by QCNN.

Uncovering quantum many-body scars with quantum machine learning

Quantum convolutional neural networks successfully identify known quantum many-body scars with over 99% accuracy in simulations and 63% on IBM quantum hardware, while also discovering new non-thermal states that can be characterized as spin-wave modes of specific quasiparticles in complex quantum systems.

A new guide to programming quantum algorithms walks programmers through every step, from theory to implementing the algorithms on IBM’s publicly available 5-qubit ibmqx4 quantum computer and others. Credit: Dreamstime

IBM Quantum Computers Roadmap

There have been a lot of articles and papers about the post of Jay Gambetta, IBM Fellow and Vice President, IBM Quantum. We’d like to emphasize on major information embedded in this post. First, this […]