We present t|ket , a quantum software development platform produced by Cambridge Quantum Computing Ltd. The heart of t|ket is a language-agnostic optimising compiler designed to generate code for a variety of NISQ devices, which has several features designed to minimise the influence of device error. The compiler has been extensively benchmarked and outperforms most competitors in terms of circuit optimisation and qubit routing. User Runtime GPU Classical HPC Logging Task Manager Scheduler Real-Time Controller Control Device Control Device Readout Device … Figure 2: Idealised system architecture for a NISQ Computerprogrammable devices which drive the evolution of the qubits and read out their states. An example of this kind of device is an arbitrary waveform generator, as found in many superconducting architectures. The microwave pulse sequences output by these devices are generated by simple low-level programs optimised for speed of execution. These devices, and the real-time controller which synchronises them, operate in a hard real-time environment where the computation takes place on the time-scale of the coherence time of the qubits. These components combine to execute a single instance of a quantum circuit, possibly with some classical control. By analogy with GPU computing, we refer to this layer as a kernel. One level higher, the scheduler is responsible for dispatching circuits to be run and packaging the results for the higher layers. It is also likely to be heavily involved in the device calibration process. (Calibration data are an important input to the compiler.) This layer and those below may be thought of as the low-level system software of the quantum computer, and must normally be physically close to the device. In the layer above we find service-oriented middleware, principally the task manager, which may distribute jobs to different quantum devices or simulators, GPUs, and perhaps conventional HPC resources, to perform the various subroutines of the quantum algorithm. This layer may also allocate access to the quantum system among multiple users. Finally, at the highest level is the user runtime, which defines the overall algorithm and integrates the results of the subcomputations to produce the final answer.

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Quantum computing systems need to be benchmarked in terms of practical tasks they would be expected to do. Here, we propose 3 "application-motivated" circuit classes for benchmarking: deep (relevant for state preparation in the variational quantum eigensolver algorithm), shallow (inspired by IQP-type circuits that might be useful for near-term quantum machine learning), and square (inspired by the quantum volume benchmark). We quantify the performance of a quantum computing system in running circuits from these classes using several figures of merit, all of which require exponential classical computing resources and a polynomial number of classical samples (bitstrings) from the system. We study how performance varies with the compilation strategy used and the device on which the circuit is run. Using systems made available by IBM Quantum, we examine their performance, showing that noise-aware compilation strategies may be beneficial, and that device connectivity and noise levels play a crucial role in the performance of the system according to our benchmarks.

The detrimental effect of noise accumulates as quantum computers grow in size. In the case where devices are too small or noisy to perform error correction, error mitigation may be used. Error mitigation does not increase the fidelity of quantum states, but instead aims to reduce the approximation error in quantities of concern, such as expectation values of observables. However, it is as yet unclear which circuit types, and devices of which characteristics, benefit most from the use of error mitigation. Here we develop a methodology to assess the performance of quantum error mitigation techniques. Our benchmarks are volumetric in design, and are performed on different superconducting hardware devices. Extensive classical simulations are also used for comparison. We use these benchmarks to identify disconnects between the predicted and practical performance of error mitigation protocols, and to identify the situations in which their use is beneficial. To perform these experiments, and for the benefit of the wider community, we introduce Qermit -an open source python package for quantum error mitigation. Qermit supports a wide range of error mitigation methods, is easily extensible and has a modular graph-based software design that facilitates composition of error mitigation protocols and subroutines.