Quantum Computing

The Open Science Prize: Solve for SWAP gates and graph states

Share this post:

Today, we’re excited to announce the IBM Quantum Awards: Open Science Prize, an award totaling $100,000 for any person or team who can devise an open source solution — using IBM quantum systems — to two important challenges at the forefront of quantum computing based on superconducting qubits:

  1. reducing gate errors,
  2. and increasing circuit fidelity for graph state preparation.

Learn more and get started: IBM Quantum Awards: Open Science Prize 

Back in May of 2016, the IBM Quantum team made waves when we put the first quantum computer on the cloud for anyone to use. Today, a community of 275,000 registered users — including scientists, software engineers, and students all over the world — run more than 1 billion circuit executions every day on a suite of quantum computers, pushing the limits of what today’s quantum computers can do, with the help of the Qiskit open source software development kit. However, during the early era of quantum technology, hardware considerations place constraints on our quantum computers’ abilities. The quantum community will need to devise hardware-aware approaches to quantum computation if we hope to maximize the potential of these devices. That’s why the Open Science Prize will award $50,000 each to the best solutions to two difficult, cutting edge problems that sit at the boundary of quantum hardware and software (deadline is April 16, 2021).

SWAP gate /swäp gāt/

noun
The SWAP gate swaps the quantum state of two qubits.

The first problem asks researchers to reduce errors in a SWAP gate by 50% or better from our current measurements on a specific IBM device. On our IBM Quantum processors, qubits can only interact with neighboring qubits – but several quantum circuits, such as those required for measuring Quantum Volume, frequently require operations between non-neighboring qubits. Quantum computers implement these operations by first using the SWAP gate to bring the quantum states of qubits closer on the chip, and then acting on these qubits with nearest-neighbor quantum gates. IBM Quantum Experience users will use Qiskit Pulse in order to devise SWAP gates of their own, learn how to best overcome connectivity imitations to best perform this nearest-neighbor interaction, and then characterize the gate’s fidelity, using a Jupyter notebook supplied by IBM Quantum. The goal is to improve the fidelity of the currently implemented SWAP gates.

If we can come up with a SWAP gate that’s better than the current SWAP gate, it can improve the performance of almost every quantum algorithm, from measuring quantum volume to Grover’s search algorithm.

Author Jin-Sung Kim (right) participating in the 2019 Qiskit Hackathon-Africa.

Author Jin-Sung Kim (right) participating in the 2019 Qiskit Hackathon-Africa.

 

Graph state /ɡraf stāt/

noun
A graph state is a kind of quantum state that can be represented by a mathematical graph, or a diagram of points (“vertexes,” representing the qubits) connected by lines (“edges,” representing entanglement between qubits).

The second problem is a challenge to increase circuit fidelity for graph state preparation on an IBM Quantum processor. We implement graph states using a fairly simple quantum circuit where each qubit on the chip is a vertex, and each pair of connected qubits is an edge in a mathematical graph. The graph state is highly dependent on the device’s connectivity, as the circuit that prepares graph states applies two-qubit CZ gates wherever there is coupling between two qubits. Applying these CZ gates by accounting for the errors on the device allows the creation of graph states with the best fidelity.

A key feature of graph states is that they entangle all of the qubits, and these entangled quantum states could be important for error correction in the future. The goal of this challenge is to create the largest graph states using the same benchmarking and error mitigation techniques that are also used to improve the individual quantum gates, ultimately looking for the best fidelity graph state as estimated by stabilizer measurements, using a Jupyter notebook supplied by IBM Quantum.

An improvement in the fidelity of the graph state has relevance for error correction and studying entanglement on larger systems.

The intricacies of IBM Quantum hardware may sit at the center of these problems, but we think that a hardware-aware approach to application and software development is the only way we’ll be able to maximize the abilities of near-term quantum devices, regardless of their architecture. For this reason, we’re requiring all entries to this contest be published open-source for the benefit of all quantum hardware and software developers. We hope this challenge will lead to new collaborations, and spark honest conversations about the capabilities of today’s quantum hardware.

We hope you’ll take part in this competition as we all work to push the field of quantum computing — including both hardware and software — further into the future.

The deadline is April 16, 2021. Winners will be announced on IBM’s fifth anniversary of putting the first quantum computer on the cloud: May 4, 2021.

Start solving for the Open Science Prize.

 

IBM Quantum

Quantum starts here

 

Quantum Research Advocate, IBM Quantum

Jin-Sung Kim

Research Staff Member, IBM Quantum

Sarah Sheldon

Research Staff Member, Experimental Quantum Computing, IBM Quantum

More Quantum Computing stories

Using iter8 and Kiali to evolve your cloud applications while gaining insights into their behavior

IBM Research has partnered with Red Hat to bring iter8 into Kiali. Iter8 lets developers automate the progressive rollout of new microservice versions. From Kiali, developers can launch these rollouts interactively, watch their progress while iter8 shifts user traffic to the best microservice version, gain real-time insights into how competing versions (two or more) perform, and uncover trends on service metrics across versions.

Continue reading

AI Data Tracker Encourages Scientific Research into COVID-19 Non-Pharmaceutical Interventions

What impact do measures such as shelter-in-place, mask wearing, and social distancing have on the number of COVID-19 cases? How do the COVID-19 quarantine measures that have been implemented by North American countries compare to South American countries? These are just a few questions about the wide range of non-pharmaceutical interventions (NPIs) that have been applied by governments, globally.

Continue reading

IBM Federated Learning – machine learning where the data is

IBM Research recently announced the community edition of a framework for federated learning.

Continue reading