Scaling up the number of qubits and accelerating quantum algorithms are important steps towards reaching quantum advantage. This poses heavy demands, as pulses need to be distributed to an increasing number of control channels and variational algorithms require rapid interleaving of quantum and classical computation. Assessing the bottlenecks in the control stack is therefore key to prepare it for reaching quantum advantage. However, existing benchmark suites suffer from indirect access to control hardware, limiting accuracy. In this work, we present Q-Profile, a tool to profile quantum control stacks using a direct connection from the host CPU to the control stack, providing fine accuracy in measuring runtime and allowing to identify performance bottlenecks. We demonstrate our tool by benchmarking the Quantum Approximate Optimization Algorithm on a Qblox Cluster for a virtual 4 to 14-qubit transmon system and identify the major execution bottlenecks. We estimate a 1.40x total runtime speedup by using an active qubit reset, and demonstrate a further 1.37x speedup by parallel control module initialization. The presented method of profiling is included in the open-source Quantify quantum control software, making it applicable to other control-stack providers and to other benchmarks, while still providing detailed information. By extension, this tool will enable identifying and eliminating bottlenecks for future quantum acceleration.
Scaling up the number of qubits and accelerating quantum algorithms are important steps towards reaching quantum advantage. This poses heavy demands, as pulses need to be distributed to an increasing number of control channels and variational algorithms require rapid interleaving of quantum and classical computation. Assessing the bottlenecks in the control stack is therefore key to prepare it for reaching quantum advantage. However, existing benchmark suites suffer from indirect access to control hardware, limiting accuracy. In this work, we present Q-Profile, a tool to profile quantum control stacks using a direct connection from the host CPU to the control stack, providing fine accuracy in measuring runtime and allowing to identify performance bottlenecks. We demonstrate our tool by benchmarking the Quantum Approximate Optimization Algorithm on a Qblox Cluster for a virtual 4 to 14-qubit transmon system and identify the major execution bottlenecks. We estimate a 1.40x total runtime speedup by using an active qubit reset, and demonstrate a further 1.37x speedup by parallel control module initialization. The presented method of profiling is included in the open-source Quantify quantum control software, making it applicable to other control-stack providers and to other benchmarks, while still providing detailed information. By extension, this tool will enable identifying and eliminating bottlenecks for future quantum acceleration.
Abstract Effectively manipulating quantum computing (QC) hardware in the presence of imperfect devices and control systems is a central challenge in realizing useful quantum computers. Susceptibility to noise critically limits the performance and capabilities of today’s so-called noisy intermediate-scale quantum devices, as well as any future QC technologies. Fortunately, quantum control enables efficient execution of quantum logic operations and quantum algorithms with built-in robustness to errors, and without the need for complex logical encoding. In this manuscript we introduce software tools for the application and integration of quantum control in QC research, serving the needs of hardware R&D teams, algorithm developers, and end users. We provide an overview of a set of Python-based classical software tools for creating and deploying optimized quantum control solutions at various layers of the QC software stack. We describe a software architecture leveraging both high-performance distributed cloud computation and local custom integration into hardware systems, and explain how key functionality is integrable with other software packages and quantum programming languages. Our presentation includes a detailed mathematical overview of key features including a flexible optimization toolkit, engineering-inspired filter functions for analyzing noise susceptibility in high-dimensional Hilbert spaces, and new approaches to noise and hardware characterization. Pseudocode is presented in order to elucidate common programming workflows for these tasks, and performance benchmarking is reported for numerically intensive tasks, highlighting the benefits of the selected cloud-compute architecture. Finally, we present a series of case studies demonstrating the application of quantum control solutions derived from these tools in real experimental settings using both trapped-ion and superconducting quantum computer hardware.
We present Benchpress, a benchmarking suite for evaluating the performance and range of functionality of multiple quantum computing software development kits. This suite consists of a collection of over 1,000 tests measuring key performance metrics for a wide variety of operations on quantum circuits composed of up to 930 qubits and O(106) two-qubit gates, as well as an execution framework for running the tests over multiple quantum software packages in a unified manner. Here we give a detailed overview of the benchmark suite and its methodology and generate representative results over seven different quantum software packages. The flexibility of the Benchpress framework enables benchmarking that not only keeps pace with quantum hardware improvements but also can preemptively gauge the quantum circuit processing costs of future device architectures. In this Resource, the authors present an open-source extensible benchmark tool, Benchpress, for evaluating the performance of mainstream quantum computing software. Benchpress was demonstrated to perform over 1,000 tests with up to 930 qubits to compare the performance of quantum software, providing insight into how to best use current programming stacks.
In this paper, we present QPack, a universal benchmark for Noisy Intermediate-Scale Quantum (NISQ) computers based on Quantum Approximate Optimization Algorithms (QAOA). Unlike other evaluation metrics in the field, this benchmark evaluates not only one, but multiple important aspects of quantum computing hardware: the maximum problem size a quantum computer can solve, the required runtime, as well as the achieved accuracy. The applications MaxCut, dominating set and traveling salesman are included to provide variation in resource requirements. This will allow for a diverse benchmark that promotes optimal design considerations, avoiding hardware implementations for specific applications. We also discuss the design aspects that are taken in consideration for the QPack benchmark, with critical quantum benchmark requirements in mind. An implementation is presented, providing practical metrics. QPack is presented as a hardware agnostic benchmark by making use of the XACC library. We demonstrate the application of the benchmark on various IBM machines, as well as a range of simulators.
While quantum computers promise to solve some scientifically and commercially valuable problems thought intractable for classical machines, delivering on this promise will require a large-scale quantum machine. Understanding the impact of architecture design choices for a scaled quantum stack for specific applications, prior to full realization of the quantum system, is an important open challenge. To this end, we develop a framework for quantum resource estimation, abstracting the layers of the stack, to estimate resources required across these layers for large-scale quantum applications. Using a tool that implements this framework, we assess three scaled quantum applications and find that hundreds of thousands to millions of physical qubits are needed to achieve practical quantum advantage. We identify three qubit parameters, namely size, speed, and controllability, that are critical at scale to rendering these applications practical. A goal of our work is to accelerate progress towards practical quantum advantage by enabling the broader community to explore design choices across the stack, from algorithms to qubits.
Current technological advancements of quantum computers highlight the need for application-driven, practical and well-defined methods of benchmarking their performance. As the existing NISQ device's quality of two-qubit gate errors rate is even around one percent and the number of qubits is still limited to a few or several dozen, naturally, we need to propose rather small algorithms instances taken from key promising application areas, such as quantum chemistry, combinatorial optimisation or machine learning. While many techniques for assessing the performance of logical components, such as gate fidelity and qubit coherence exist, it is still challenging to extrapolate those values onto the performance of different quantum algorithms and subroutines. This work aims to introduce a series of initial quantum application benchmarks together with a methodology of execution for measuring the performance and fidelity of the results. The proposed suite refers to several variational algorithms widely used on available NISQ devices but also includes examples of quantum circuits designed for a fault-tolerant quantum computer.
Technological advances in neutral-atom platforms have opened a new path toward designing efficient protocols for fault-tolerant quantum computing (FTQC). However, each physical operation is still orders of magnitude slower than on other platforms, such as superconducting qubits. Therefore, architects must identify fast and efficient FTQC architectures, which require reliable modeling tools to explore various design choices across the software, classical hardware, and quantum device stacks of neutral-atom platforms.
In this paper, we propose NAQsim, an open-source simulation framework for neutral-atom FTQC architectures based on transversal gates. As its key feature, NAQsim enables detailed full-stack architecture evaluation that opens opportunities to explore previously overlooked performance bottlenecks. As a first use case for NAQsim, we identify one such bottleneck, patch rotations, perform full-stack co-optimization, and finally derive a near-rotation-free architecture (D3-ROT). For practical FTQC benchmark workloads, D3-ROT achieves a 2.27x speedup from this single bottleneck alone, with minimal footprint overhead. These results point to a much broader space of full-stack optimizations that NAQsim makes accessible for transversal surface-code architectures and beyond.