What is resource estimation? Bridging theoretical algorithms and practical realization

Today's quantum computers cannot execute many algorithms that are widely studied for their potential value, with applications ranging from drug research to green energy. But how do you determine which quantum algorithms could actually fit on hardware, with reasonable runtimes? First, you need an understanding of their computational requirements.

This process is called resource estimation, and PennyLane's estimator module makes it a smooth one.

By estimating the resources required by a particular algorithm, you can determine how many qubits, how low of an error rate, and how much time a physical quantum computer would need to reliably execute it. Such information affects both algorithm research and hardware development. These insights determine where algorithmic optimizations or hardware innovations are needed to make useful quantum applications a reality.

Resource estimation of quantum algorithms is not a problem of the future, but is important here and now. As Jens Eisert and John Preskill have said, any proposed application of quantum computing must have "quantifiable and reachable resource requirements," in part because we need to strengthen the likelihood of near-term quantum utility. It is not only a gap between noisy and fault-tolerant hardware which needs to be filled, but a gap between the resource requirements of algorithms which may prove useful and the computational limitations of quantum hardware; this chasm needs to be bridged from both ends.

The goal of quantum computing in itself is not simply to reach "the fault-tolerant era," but rather "to leverage the limited available resources to perform classically challenging tasks," which provide "a useful answer to a question that someone cares about." Resource estimation is by no means an afterthought. Rather, it is a crucial piece of the puzzle in making quantum computing useful and available to people everywhere.

Resource estimation in practice

PennyLane's estimator module provides a suite of tools which accelerate quantum algorithm research. By pairing quantum algorithms with precise resource estimates, we can make judgements on not only their theoretical utility, but their practical utility. Here's a small sample of publications in which PennyLane's resource estimator is used to assess the feasibility of promising quantum algorithms:

How PennyLane makes it easy

The resources needed to execute a quantum program can be expressed at various levels of abstraction: logical gate counts, physical instructions (how many?), temporal cost (how long?), monetary cost (how much?). PennyLane's resource estimator was designed with a particular use case in mind: Understanding the resource requirements of algorithms and how they scale before devoting extensive time and effort to preparing the algorithms for compilation and execution. This module makes use of highly efficient resource operators which demand the minimal information necessary to generate a reliable resource estimate. However, by providing more information, such as the particular wires an operator acts upon or the distribution of Pauli words in a Hamiltonian, you can expect an even tighter estimate.

These resource operators are designed for speed and ease of use, and make it possible for PennyLane to estimate the resources of circuits which are far too complex to simulate classically! Further, if you already have your circuit written for execution, you can still estimate its resources using the estimator module, which intelligently maps PennyLane operators to the analogous resource operators.

PennyLane's resource estimator provides resource estimates using state-of-the-art decompositions from the latest literature. The decompositions being used to describe the resources of particular operators are detailed in our documentation, which also includes references to the relevant literature. Resource operators are also straightforward to define, so you can easily implement and analyze your own algorithmic techniques.

To learn the basics of using PennyLane's estimator module, check out our demo on:

• How to estimate the resource requirements of quantum algorithms

We're also showcasing our estimator in a series of demos where you can see it in action, estimating the resources of various algorithms with promising applications:

• Qubit and gate trade-offs in qubitized Quantum Phase Estimation
• Resource estimation for Hamiltonian simulation with GQSP
• How to estimate the resource cost of QSVT

With PennyLane by your side, I encourage you to be intentional in your development of quantum algorithms. See how your quantum algorithms compare with the quantum hardware available today and expected in the coming years, so you know exactly where improvements are needed to bring about an era of indisputably useful quantum computers.