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We present a novel automated technique for parallelizing quantum circuits via forward and backward translation to measurement-based quantum computing patterns and analyze the trade off in terms of depth and space complexity. As a result we…

Quantum Physics · Physics 2012-02-22 Anne Broadbent , Elham Kashefi

Decoherence -- in the current physical implementations of quantum computers -- makes depth reduction a vital task in quantum-circuit design. Moore and Nilsson (SIAM Journal of Computing, 2001) demonstrated that additional qubits -- known as…

Quantum Physics · Physics 2022-09-26 Jiaqing Jiang , Xiaoming Sun , Shang-Hua Teng , Bujiao Wu , Kewen Wu , Jialin Zhang

Despite the promise that fault-tolerant quantum computers can efficiently solve classically intractable problems, it remains a major challenge to find quantum algorithms that may reach computational advantage in the present era of noisy,…

Quantum Physics · Physics 2024-11-13 Miguel Murça , Duarte Magano , Yasser Omar

We give a comprehensive characterization of the computational power of shallow quantum circuits combined with classical computation. Specifically, for classes of search problems, we show that the following statements hold, relative to a…

We propose a definition of QNC, the quantum analog of the efficient parallel class NC. We exhibit several useful gadgets and prove that various classes of circuits can be parallelized to logarithmic depth, including circuits for encoding…

Quantum Physics · Physics 2009-09-25 Cristopher Moore , Martin Nilsson

Parallel computation enables multiple processors to execute different parts of a task simultaneously, improving processing speed and efficiency. In quantum computing, parallel gate implementation involves executing gates independently in…

Quantum Physics · Physics 2024-11-20 Boris Arseniev

We exhibit some simple gadgets useful in designing shallow parallel circuits for quantum algorithms. We prove that any quantum circuit composed entirely of controlled-not gates or of diagonal gates can be parallelized to logarithmic depth,…

Quantum Physics · Physics 2009-09-25 Cristopher Moore , Martin Nilsson

Massively parallel hardware (GPUs) and long sequence data have made parallel algorithms essential for machine learning at scale. Yet dynamical systems, like recurrent neural networks and Markov chain Monte Carlo, were thought to suffer from…

Numerical Analysis · Mathematics 2026-03-18 Xavier Gonzalez

A defining feature in the field of quantum computing is the potential of a quantum device to outperform its classical counterpart for a specific computational task. By now, several proposals exist showing that certain sampling problems can…

Quantum Physics · Physics 2020-09-23 Rawad Mezher , Joe Ghalbouni , Joseph Dgheim , Damian Markham

We study how parallelism can speed up quantum simulation. A parallel quantum algorithm is proposed for simulating the dynamics of a large class of Hamiltonians with good sparse structures, called uniform-structured Hamiltonians, including…

Quantum Physics · Physics 2024-01-17 Zhicheng Zhang , Qisheng Wang , Mingsheng Ying

We present a novel parallelisation scheme that simplifies the adaptation of learning algorithms to growing amounts of data as well as growing needs for accurate and confident predictions in critical applications. In contrast to other…

Machine Learning · Computer Science 2018-10-09 Michael Kamp , Mario Boley , Olana Missura , Thomas Gärtner

The rapid evolution of quantum devices fuels concerted efforts to experimentally establish quantum advantage over classical computing. Many demonstrations of quantum advantage, however, rely on computational assumptions and face…

The Linear Combination of Unitaries (LCU) method is a powerful scheme for the block encoding of operators but suffers from high overheads. In this work, we discuss the parallelisation of LCU and in particular the SELECT subroutine of LCU…

Quantum Physics · Physics 2024-08-22 Gregory Boyd

We investigate the use of possibly the simplest scheme for the parallelisation of the standard particle filter, that consists in splitting the computational budget into $M$ fully independent particle filters with $N$ particles each, and…

Computation · Statistics 2015-10-12 Dan Crisan , Joaquin Miguez , Gonzalo Rios

Robust quantum computation with d-level quantum systems (qudits) poses two requirements: fast, parallel quantum gates and high fidelity two-qudit gates. We first describe how to implement parallel single qudit operations. It is by now well…

Quantum Physics · Physics 2009-11-13 Dianne P. O'Leary , Gavin K. Brennen , Stephen S. Bullock

Optimizing quantum circuits by reducing circuit depth is essential for improving the efficiency and scalability of quantum algorithms, particularly as quantum hardware continues to evolve. This can be achieved by restructuring quantum…

Quantum Physics · Physics 2026-05-07 Folkert de Ronde , Stephan Wong , Sebastian Feld

The MapReduce framework has firmly established itself as one of the most widely used parallel computing platforms for processing big data on tera- and peta-byte scale. Approaching it from a theoretical standpoint has proved to be…

Computational Complexity · Computer Science 2019-12-30 Fabian Frei , Koichi Wada

Motivated by the recent experimental demonstrations of quantum supremacy, proving the hardness of the output of random quantum circuits is an imperative near term goal. We prove under the complexity theoretical assumption of the…

Quantum Physics · Physics 2021-12-13 Yasuhiro Kondo , Ryuhei Mori , Ramis Movassagh

Quantum computations are typically compiled into a circuit of basic quantum gates. Just like for classical circuits, a quantum compiler should optimize the quantum circuit, e.g. by minimizing the number of required gates. Optimizing quantum…

Quantum Physics · Physics 2023-03-31 Raban Iten , Romain Moyard , Tony Metger , David Sutter , Stefan Woerner

Classes of set functions along with a choice of ground set are a bedrock to determine and develop corresponding variants of greedy algorithms to obtain efficient solutions for combinatorial optimization problems. The class of approximate…

Optimization and Control · Mathematics 2021-08-20 Praneeth Vepakomma , Yulia Kempner , Ramesh Raskar
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