Fast classical processing is essential for most quantum fault-tolerance architectures. We introduce a sliding-window decoding scheme that provides fast classical processing for the surface code through parallelism. Our scheme divides the syndromes in space-time into overlapping windows along the time direction, which can be decoded in parallel with any inner decoder. With this parallelism, our scheme can solve the backlog problem as the code scales up, even if the inner decoder is slow. When using minimum-weight perfect matching and union find as the inner decoders, we observe circuit-level thresholds of $0.68\mathrm{\%}$ and $0.55\mathrm{\%}$, respectively, which are almost identical to those for the batch decoding.

We develop an algorithmic framework for contracting tensor networks and demonstrate its power by classically simulating quantum computation of sizes previously deemed out of reach. Our main contribution, index slicing, is a method that efficiently parallelizes the contraction by breaking it down into much smaller and identically structured subtasks, which can then be executed in parallel without dependencies. We benchmark our algorithm on a class of random quantum circuits, achieving greater than 10⁵ times acceleration over the original estimate of the simulation cost. We then demonstrate applications of the simulation framework for aiding the development of quantum algorithms and quantum error correction. As tensor networks are widely used in computational science, our simulation framework may find further applications.

In the effort to develop useful quantum computers, simulating quantum machines with conventional classical computing resources is a key capability. Such simulations will always face limits, preventing the emulation of quantum computers at substantial scale; however, by pushing the envelope through optimal choices of algorithms and hardware, the value of simulator tools can be maximized. This work reviews state-of-the-art numerical simulation methods, i.e., classical algorithms that emulate quantum computer evolution under specific operations. We focus on the mainstream state-vector and tensor-network paradigms, while briefly mentioning alternative methods. Moreover, we review the diverse applications of simulation across different facets of quantum computer development, including understanding the fundamental differences between quantum and classical computations, exploring algorithmic design for quantum advantage, predicting quantum processor performance at the design stage, and efficiently characterizing fabricated devices for rapid iterations. This review complements recent surveys of current tools and implementations; here, we aim to provide readers with an essential understanding of the theoretical basis of classical simulation methods, a detailed discussion of their advantages and limitations, and an overview of the demands and challenges arising from practical use cases.

A quantum instruction set is where quantum hardware and software meet. We develop characterization and compilation techniques for non-Clifford gates to accurately evaluate its designs. Applying these techniques to our fluxonium processor, we show that replacing the iSWAP gate by its square root SQiSW leads to a significant performance boost at almost no cost. More precisely, on SQiSW we measure a gate fidelity of up to 99.72% and averaging at 99.31%, and realize Haar random two-qubit gates with an average fidelity of 96.38%. This is an average error reduction of 41% for the former and a 50% reduction for the latter compared to using iSWAP on the same processor.

The set of quantum logic gates that can be easily implemented is fundamental to the performance of quantum computers, as it governs the accuracy of basic quantum operations and dictates the complexity of implementing quantum algorithms. Traditional approaches to extending gate sets often require operating devices outside the ideal parameter regimes used to realize qubits, leading to increased control complexity while offering only a limited set of gates. Here we experimentally demonstrate a unified and versatile gate scheme capable of generating arbitrary two-qubit gates using only an exchange interaction and qubit driving on a superconducting quantum processor. We achieve high fidelities averaging 99.38% across a wide range of commonly used two-qubit unitaries, enabling precise multipartite entangled state preparation. Furthermore, we successfully produce a B gate, which efficiently synthesizes the entire family of two-qubit gates. Our results establish that fully exploiting the capabilities of the exchange interaction can yield a comprehensive and highly accurate gate set. With maximum expressivity, optimal gate time, demonstrated high fidelity and easy adaption to other quantum platforms, our unified control scheme offers the prospect of improved performance in quantum hardware and algorithm development.