In addition, the computation required to verify the output of these benchmarks becomes intractable as the number of qubits increases.įinally, capturing the general performance of a computational system within a single number can be very challenging and can lead to unintended consequences. Typical quantum applications do not generally take the form of random quantum circuits, so they are not necessarily representative of useful workloads. Similarly, synthetic benchmarks, while useful for providing insights into the theoretical computational power of a device, are also limited in their scalability and similarity to real-world applications. While understanding the properties of individual gate operations is a critical component of a quantum computing (QC) system, it does not represent performance on applications. Examples include (a) measurements of individual gate errors, qubit coherence times, or other low-level hardware properties, and (b) synthetic benchmarks that use random circuits to gauge hardware performance. Many attempts to describe quantum devices focus on metrics that are unrepresentative of holistic performance for real applications. You may have faced a challenge when deciding on which QPU to run your application because the quantum industry has yet to adopt a de facto benchmark. With the introduction of various quantum computing architectures, new benchmarks must be developed and tailored to these systems. The PARSEC benchmark suite was introduced in response to the proliferation of chip multiprocessors, and the explosion of machine learning applications led to the creation of MLPerf to benchmark performance between different models. Looking back, the growth of computing in the 1970s and 1980s led to the creation of LINPACK and SPEC for benchmarking supercomputers and workstations. Benchmarking: past and presentĬreating benchmarks is a foundational aspect of the computing industry, as the emergence of new architectures requires new ways to measure and define performance. SupermarQ uses Amazon Braket for device-agnostic access to gate-based quantum processing units (QPUs), so benchmarks can highlight the heterogeneity of quantum computers and their various strengths in the Noisy Intermediate-Scale Quantum (NISQ) era and beyond. In this post, we will discuss the current landscape of quantum benchmarking and introduce SupermarQ, Super.tech’s suite of application-based benchmarks designed to overcome the limitations of existing approaches.
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