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arXiv:2506.14257

One Polynomial Strategy for Computing Local Projections on Square-Lattice Cluster States

Nyau Fisn, Hung‐Lin Fu

Published Jun 17, 2025• 0 citations
arXivHTMLPDF

Abstract

Quantum computing has attracted a lot of attention in recent years. It is one of the promising candidates for the next-generation computing paradigms. Basically, there are two technical lines to realize quantum computing. One is composing the unitary operators of a few qubits to achieve general unitary operators on an arbitrary number of qubits, known as the approach of quantum circuits. The other one focuses on preparing quantum cluster states and performing the computation by measuring the states with a particular basis, known as measurement-based quantum computing or one-way quantum computing. The two strategies have been proven to be equivalent to each other. This note aims to discuss the strategies for computing the local projections on square-lattice cluster states. Seemingly, one strategy for the computation could require both polynomial steps and memories. In particular, if the number of qubits in a square-lattice is denoted by $N$, the step number for computing an arbitrary local projection on the state could be proportional to a polynomial of $N$ under a bounded cost of memory. Consider that the square-lattice cluster states are one kind of universal computing resource, the results might be helpful for understanding the computational advantages of quantum algorithms, as well as the limits of the numerical analysis on other relevant quantum models. Although the results in the note are not peer reviewed, we would like to make them public because they are quite easy to check.

DOI
10.48550/arxiv.2506.14257

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Alternative quantum computing models to the standard unitary-based model have been independently introduced and developed. One of them, introduced by Nielsen [11] is based on projective measurements only. Another, the one-way quantum computation introduced by Briegel and Raussendorf [13] consists in applying local measurements on an offline-prepared entangled state, called cluster state. The model via measurements only has been successively improved [12], decreasing the resources (in terms of number of different projective measurements) used for a universal quantum computation. The last improvement is based on state transfer [15]. The resources used by the one-way quantum computation are not limited to local measurements, since the preparation of the initial cluster state has to be taken into account. It turns out that the resources used by the one-way quantum computation if the cluster state is prepared by means of projective measurements [9] are similar to the resources used by the quantum computation via measurements only based on state transfer. Moreover, the measurement-based preparation of the initial cluster state leads to a natural unification of these models. If, instead of a measurement-based preparation, a unitary-based preparation of the cluster state is considered, a natural unification between the one-way quantum computation and an hybrid model allowing both unitary transformation and projective measurements is obtained [3].
We examine cluster states transformed by stochastic local operations and classical communication, as a resource for deterministic universal computation driven strictly by projective measurements. We identify circumstances under which such states in one dimension constitute resources for random-length single-qubit rotations, in one case quasideterministically ($N\ensuremath{-}U\ensuremath{-}N$ states) and in another probabilistically ($B\ensuremath{-}U\ensuremath{-}B$ states). In contrast to the cluster states, the $N\ensuremath{-}U\ensuremath{-}N$ states exhibit spin correlation functions that decay exponentially with distance, while the $B\ensuremath{-}U\ensuremath{-}B$ states can be arbitrarily locally pure. A two-dimensional square $N\ensuremath{-}U\ensuremath{-}N$ lattice is a universal resource for quasideterministic measurement-based quantum computation. Measurements on cubic $B\ensuremath{-}U\ensuremath{-}B$ states yield two-dimensional cluster states with bond defects, whose connectivity exceeds the percolation threshold for a critical value of the local purity.
The last two decades have witnessed the rapid growth of quantum information processing as an active field of multidisciplinary science and technology. Tremendous theoretical and experimental progress has been made in quantum communication and quantum computation, and it seems clear that some of this progress will soon result in practical applications outside the laboratory. Quantum computation aims to build a quantum computer and to explore the origins and limits of quantum computing power. The original circuit model of a quantum computer, due to Deutsch, has a strong formal similarity with the circuit model of a classical computer. Both models depict a computation as processing via the wiring of a number of fiducial gates, each of which dynamically couples a small number of input (qu)bits in a controlled manner. In other words, unitary evolution is the basic mechanism of information processing in the circuit model of quantum computers. Starting with a given fixed state of many qubits, measurement based quantum computation (MBQC) processes information by applying a sequence of measurements to designated qubits in designated bases. This is remarkable, given the probabilistic nature of quantum measurement and the murky physical status of the dynamics of 'wavefunction collapse'. The final result of the computation is determined from the classical data of all the measurement outcomes. There exist two principle models: the teleportation based model and the 'one-way quantum computer' of 'cluster state computation'. The formalism of cluster states has proven to be a powerful way of describing the essential entanglement resources needed to perform quantum information processing. Unlike the circuit model, the MBQC models have no natural classical counterpart. There are a number of physical architectures for which measurements on the relevant systems are more easily performed than controlled unitary evolution. Moreover, it appears the MBQC models can offer novel computational benefits, especially regarding issues of fault-tolerance and parallelizability of algorithms. As a result, the theory of MBQC is now shaping the latest experimental proposals across the full spectrum of quantum information processing technologies. Naturally, understanding the scope of measurement based quantum computation is also yielding many insights into what is necessary (as opposed to merely sufficient) for us to harness the power of quantum computers. Quantum communication aims to offer efficient and secure ways of information exchange, directly based on quantum mechanics. Photons serve as natural information carriers, owing to their robustness against decoherence. A major problem for photonic communication systems is due to photon loss. It turns out that this difficulty can be overcome by the quantum repeater, a measurement-based technique. The basic idea is to divide the transmission channel into many segments and perform some quantum measurement on each segment. Additional measurement-based protocols and technologies have also been demonstrated to improve the quality of quantum communication, such as entanglement swapping and purification. The articles in this invited Focus Issue cover topics both in measurement-based quantum communication and computation, but the emphasis is given to quantum computation. A significant number of articles deal with the preparation of cluster states, the experimental realization of a one-way quantum computer, and the associated efficiency and benefits. We hope that you will find that some of these new results are of interest to you, and further hope that the issue will increase your interest in this interdisciplinary research field if you are a non-expert. Focus on Measurement-Based Quantum Information Processing Contents A proof-of-principle experiment of eliminating photon-loss errors in cluster states Wei-Bo Gao, Xiao-Qi Zhou, Jin Zhang, Tao Yang and Jian-Wei Pan Continuous variable multipartite entanglement and optical implementations of quantum communication networks Yimin Lian, Changde Xie and Kunchi Peng Generalized flow and determinism in measurement-based quantum computation Daniel E Browne, Elham Kashefi, Mehdi Mhalla and Simon Perdrix The entanglement of the four-photon cluster state C Schmid, N Kiesel, W Wieczorek and H Weinfurter Optical zeno gate: bounds for fault tolerant operation Patrick M Leung and Timothy C Ralph Towards experimental entanglement connection with atomic ensembles in the single excitation regime Julien Laurat, Chin-wen Chou, Hui Deng, Kyung Soo Choi, Daniel Felinto, Hugues de Riedmatten and H J Kimble Towards minimal resources of measurement-based quantum computation Simon Perdrix Experimental realization of a quantum game on a one-way quantum computer Robert Prevedel, Andre Stefanov, Philip Walther and Anton Zeilinger Fundamentals of universality in one-way quantum computation M Van den Nest, W Dur, A Miyake and H J Briegel Loss tolerant linear optical quantum memory by measurement-based quantum computing Michael Varnava, Daniel E Browne and Terry Rudolph Graph state generation with noisy mirror-inverting spin chains Stephen R Clark, Alexander Klein, Martin Bruderer and Dieter Jaksch One-way quantum computing in adecoherence-free subspace M S Tame, M Paternostro and M S Kim Cluster state preparation using gates operating at arbitrary success probabilities K Kieling, D Gross and J Eisert Topological fault-tolerance in cluster state quantum computation R Raussendorf, J Harrington and K Goyal Strategies for the preparation of large cluster states using non-deterministic gates Peter P Rohde and Sean D Barrett A repeat-until-success quantum computing scheme A Beige, Y L Lim and L C Kwek Efficient growth of complex graph states via imperfect path erasure Earl T Campbell, Joseph Fitzsimons, Simon C Benjamin and Pieter Kok Investigation of a single-photon source based on quantum interference T B Pittman, J D Franson and B C Jacobs Entanglement and local information access for graph states Damian Markham, Akimasa Miyake and Shashank Virmani The efficiencies of generating cluster states with weak nonlinearities Sebastien G R Louis, Kae Nemoto, W J Munro and T P Spiller A direct approach to fault-tolerance in measurement-based quantum computation via teleportation Marcus Silva, Vincent Danos, Elham Kashefi and Harold Ollivier Experimental ancilla-assisted qubit transmission against correlated noise T Yamamoto, R Nagase, J Shimamura, S K Ozdemir, M Koashi and N Imoto Terry Rudolph, Imperial College, London, UK Jian-Wei Pan, University of Science and Technology of China, Hefei, People's Republic of China and Universitat Heidelberg, Germany
Quantum state preparation is a central primitive in many quantum algorithms, yet it is generally resource intensive, with efficient constructions known only for structured families of states. This work introduces a method for preparing quantum states whose amplitudes are given by a degree−d-d−d polynomial, using circuits with logarithmic depth in the number nnn of qubits and only O(n)\mathcal O(n)O(n) ancilla qubits, improving previous approaches that required linear-depth circuits. The construction first relies on a block-encoding of an affine diagonal operator based on its Pauli-basis decomposition, which involves only nnn terms. A modified linear-combination-of-unitaries (LCU) technique is introduced to implement this decomposition in logarithmic depth, together with a novel circuit for the EXACT-one oracle that flags basis states in which exactly one qubit is in the state ∣1⟩|1\rangle∣1⟩. It then uses a generalized quantum eigenvalue transformation (GQET) to promote this affine operator to an arbitrary degree polynomial. Theoretical analysis and numerical simulations are reported along with a proof-of-principle implementation on a trapped-ion quantum processor using 141414 qubits and more than 500500500 primitive quantum gates. Because polynomial approximations are ubiquitous in scientific computing, this construction provides a scalable and resource-efficient approach to quantum state preparation, further improving the potential of quantum algorithms in fields such as chemistry, physics, engineering, and finance.
We propose a method for the implementation of one-way quantum computing in superconducting circuits. Measurement-based quantum computing is a universal quantum computation paradigm in which an initial cluster state provides the quantum resource, while the iteration of sequential measurements and local rotations encodes the quantum algorithm. Up to now, technical constraints have limited a scalable approach to this quantum computing alternative. The initial cluster state can be generated with available controlled-phase gates, while the quantum algorithm makes use of high-fidelity readout and coherent feedforward. With current technology, we estimate that quantum algorithms with above 20 qubits may be implemented in the path toward quantum supremacy. Moreover, we propose an alternative initial state with properties of maximal persistence and maximal connectedness, reducing the required resources of one-way quantum computing protocols.
Recently, quantum convolutional neural networks (QCNNs) are proposed, harnessing the power of quantum computing for faster training compared to the classical counterparts. However, this framework for deep learning also relies on multiple processing layers to capture the representation of data, which necessitates precise dynamical control. Given the current stage of quantum computing, achieving this level of control at a large scale remains challenging. Here, we propose an alternate approach to implementing QCNNs by utilizing cluster states. The training process of the method involves tuning the projection basis of each qubit in cluster states, rather than adjusting the parameters of layers of operators in deep quantum circuits. Hence, the whole system is easier to stabilize by avoiding the complex controls. Leveraging techniques in measurement-based quantum computing, we present an exact cluster state solution to general QCNNs. Followingly, we provide numerical evidence that both quantum and classical data can be learned by measuring cluster states, and a faster convergence of the method is observed. The cluster states we consider in our learning examples are merely square-lattice cluster states, whose implementation at large scale have been reported recently. It indicates that our method has the potential for realizing the advance of quantum deep learning for practical uses.