In quantum computing, state preparation is the problem of synthesizing circuits that initialize quantum systems to specific states. It has been shown that there are states that require circuits of exponential size to be prepared (when not using measurements), and consequently, despite extensive research on this problem, the existing computer-aided design (CAD) methods produce circuits of exponential size. In this paper, we show how CAD based state preparation can be made scalable by using techniques that are unique to quantum computing: measurements, and the resulting state collapses. With this approach, we are able to produce wide classes of states in polynomial time, resulting in an exponential improvement over existing CAD methods.

Data movements between different levels of the memory hierarchy (I/O-transitions, or simply I/O s) are a critical performance bottleneck in modern computing. Therefore it is a problem of high practical relevance to find algorithms that use a minimal number of I/O s. We present a cache-oblivious sparse matrix-sparse matrix multiplication algorithm that uses a worst-case number of I/O s that matches a previously established lower bound for this problem (0 (N2/B.M) read-I/Os and 0 (N2/B) write-I/Os, where$N$is the size of the problem instance,$M$is the size of the fast memory and$B$is the size of the cache lines). When the output does not need to be stored, also the number of write-I/Os can be reduced to 0 (N2/B.M). This improves the worst-case I/O-complexity of the previously best known algorithm for this problem (which is cache-aware) by a logarithmic multiplicative factor. Compared to other cache-oblivious algorithms our algorithm improves the worst-case number of I/Os by a multiplicative factor of Θ(M. N). We show how the algorithm can be applied to produce the first I/O-efficient solution for the sparse 2- vs 3-diameter problem on sparse directed graphs.

Important graph mining problems such as Clustering are computationally demanding. To significantly accelerate these problems, we propose ProbGraph: a graph representation that enables simple and fast approximate parallel graph mining with strong theoretical guarantees on work, depth, and result accuracy. The key idea is to represent sets of vertices using probabilistic set representations such as Bloom filters. These representations are much faster to process than the original vertex sets thanks to vectorizability and small size. We use these representations as building blocks in important parallel graph mining algorithms such as Clique Counting or Clustering. When enhanced with ProbGraph, these algorithms significantly outperform tuned parallel exact baselines (up to nearly 50 x on 32 cores) while ensuring accuracy of more than 90% for many input graph datasets. Our novel bounds and algorithms based on probabilistic set representations with desirable statistical properties are of separate interest for the data analytics community. Proofs of theorems & more results: http://arxiv.org/abs/2208.11469

Generating quantum circuits that prepare specific states is an essential part of quantum compilation. Algorithms that solve this problem for general states generate circuits at grow exponentially in the number of qubits. However, in contrast to general states, many practically relevant states are sparse in the standard basis. In this paper we show how sparsity can be used for efficient state preparation. We present a polynomial-time algorithm that generates polynomial-size quantum circuits (linear in the number of nonzero coefficients times number of qubits) that prepare given states, making computer-aided design of sparse state preparation scalable.

In order to compute a non-invertible function on a reversible circuit, one needs to "embed" the function into a larger function which has some garbage bits, corresponding to additional lines. The problem of determining the minimal number of garbage bits that are needed to embed a given function has attracted extensive research, largely motivated by quantum computing, where the number of lines equals the number of qubits. However, all approaches that are known have either no theoretical quality guarantees (bounds on approximation factors) or require exponential runtime. We present an efficient probabilistic approximation algorithm with theoretical bounds.