Quantum Computing Resource Estimation May Overcome Current Limitations

Quantum computing has made significant progress in recent years, with advancements in both software and hardware. However, the application of quantum computing to real-world problems is currently limited due to the complexity of quantum algorithms and the limitations of quantum computing hardware. A new technique, Resource Estimation (RE), could help overcome these challenges. RE provides an estimate of the resources required to execute a quantum program, allowing researchers and developers to explore possible optimizations and make more informed decisions about the design of quantum computing applications. This could lead to the development of more practical and efficient quantum computing applications in the future.

What is the Current State of Quantum Computing?

Quantum computing has made significant strides in recent years, with advancements in both software and hardware. This progress has led to an expanding range of quantum devices with progressively enhanced qubit quality. As a result, there is increased interest in academia and industry for tackling diverse problems across multiple application domains. This trend is indicative of the growing potential of quantum computing to offer transformative solutions to complex problems that are beyond the capabilities of classical computing.

The utilization of quantum computers to solve complex problems involves a multistage process. The first step involves selecting or developing a quantum algorithm that offers a quantum advantage, i.e., can solve the problem better compared to the best-known classical algorithm. The second step entails the encoding of the problem in terms of a quantum program that can be compiled into machine instructions for a quantum computer. This process factors in various constraints on quantum gate sets and qubit connectivities that arise from different quantum computing technologies being explored. The third step involves executing the compiled program on the quantum computer or a corresponding simulator, and the final step covers the interpretation of the measurement results from the quantum computer as the solution to the original problem.

What are the Limitations of Quantum Computing?

Unfortunately, this process fails as soon as practically relevant applications are considered. This is because the application of the described workflow currently relies on the use of quantum simulators and near-term quantum computers. The former deploy classical computers to simulate quantum algorithms—a computationally complex task which is limited to a few dozen qubits. The latter supports a larger number of qubits, but their scalability is limited as well. Importantly, the noise rates in these near-term quantum computers currently are still too high to support high-fidelity executions of practically relevant applications.

A comprehensive evaluation of the potential of quantum computing for a considered problem necessitates the consideration of quantum error correction to scale to larger problem instances closer to real-world problem sizes. This induces an overhead in the required resources, such as the number of qubits, which effectively exceeds the limits of current devices by far. All of that could lead to a situation in which certain quantum algorithms might not deliver any practical quantum advantage once the presumed characteristics of the fault-tolerant quantum computer and the efficacy of error correction protocols are factored in. Because of this, it is often still not clear what applications are suitable to be solved using quantum computing—constituting a major bottleneck in the progress of quantum computing application development.

What is Resource Estimation in Quantum Computing?

Recently, a complementary approach to the execution on simulators or quantum computing hardware emerged: Resource Estimation (RE). Instead of actually executing a given quantum program, RE gives an estimate of the resources necessary to execute it in a fault-tolerant fashion. Although this approach does not return the actual solution, it allows one to determine an early resource estimate—a procedure that has been exploited in conventional computing for decades, for example, in classical hardware/software co-design where cost estimates are used to guide which functionalities are realized in hardware and which in software.

How Can Resource Estimation Improve Quantum Computing?

In this work, the authors demonstrate how to utilize Resource Estimation to improve the situation. They show how the current workflow relying on simulation and/or execution can be complemented with an estimation step. This allows end-users to consider real-world problem instances already today, also considering error correction schemes and correspondingly required hardware resources. It also enables users to start exploring possible optimizations of those instances across the entire design space and incorporate hypotheses of hardware development trends to derive more informed and thus better design space parameters. Overall, this enables end-users already today to check out the promises of possible future quantum computing applications, even if the corresponding hardware to execute them is not available yet.

What is the Future of Quantum Computing?

The future of quantum computing is promising, but it is also fraught with challenges. The current limitations of quantum computing hardware and the complexity of quantum algorithms make it difficult to apply quantum computing to real-world problems. However, the development of new techniques such as Resource Estimation could help overcome these challenges. By providing an estimate of the resources required to execute a quantum program, Resource Estimation allows researchers and developers to explore possible optimizations and make more informed decisions about the design of quantum computing applications. This could pave the way for the development of more practical and efficient quantum computing applications in the future.