arXiv subjects

Yasunari Suzuki

Publications and source records attributed to Yasunari Suzuki.

49 records · Page 3Linked to original sources

arXiv2023

Subspace Variational Quantum Simulator

Quantum simulation is one of the key applications of quantum computing, which accelerates research and development in the fields such as chemistry and material science. The recent development of noisy intermediate-scale quantum (NISQ) devices urges the exploration of applications without the necessity of quantum error correction. In this paper, we propose an efficient method to simulate quantum dynamics driven by a static Hamiltonian on NISQ devices, named subspace variational quantum simulator (SVQS). SVQS employs the subspace-search variational quantum eigensolver (SSVQE) to find a low-lying eigensubspace and extends it to simulate dynamics within the subspace with lower overhead compared to the existing schemes. We experimentally simulate the time-evolution operator in a low-lying eigensubspace of a hydrogen molecule. We also define the subspace process fidelity as a measure between two quantum processes in a subspace. The subspace time evolution mimicked by SVQS shows the subspace process fidelity of $0.88$-$0.98$.

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arXiv2022

Characterizing quantum pseudorandomness by machine learning

Random dynamics in isolated quantum systems is of practical use in quantum information and is of theoretical interest in fundamental physics. Despite a large number of theoretical studies, it has not been addressed how random dynamics can be verified from experimental data. In this paper, based on an information-theoretic formulation of random dynamics, i.e., unitary $t$-designs, we propose a method for verifying random dynamics from the data that is experimentally easy-to-access. More specifically, we use measurement probabilities estimated by a finite number of measurements of quantum states generated by a given random dynamics. Based on a supervised learning method, we construct classifiers of random dynamics and show that the classifiers succeed to characterize random dynamics. We then apply the classifiers to the data set generated by local random circuits (LRCs), which are canonical quantum circuits with growing circuit complexity, and show that the classifiers successfully characterize the growing features. We further apply the classifiers to noisy LRCs, showing the possibility of using them for verifying noisy quantum devices, and to monitored LRCs, indicating that the measurement-induced phase transition may possibly not be directly related to randomness.

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arXiv2022

NEO-QEC: Neural Network Enhanced Online Superconducting Decoder for Surface Codes

Quantum error correction (QEC) is essential for quantum computing to mitigate the effect of errors on qubits, and surface code (SC) is one of the most promising QEC methods. Decoding SCs is the most computational expensive task in the control device of quantum computers (QCs), and many works focus on accurate decoding algorithms for SCs, including ones with neural networks (NNs). Practical QCs also require low-latency decoding because slow decoding leads to the accumulation of errors on qubits, resulting in logical failures. For QCs with superconducting qubits, a practical decoder must be very power-efficient in addition to having high accuracy and low latency. In order to reduce the hardware complexity of QC, we are supposed to decode SCs in a cryogenic environment with a limited power budget, where superconducting qubits operate. In this paper, we propose an NN-based accurate, fast, and low-power decoder capable of decoding SCs and lattice surgery (LS) operations with measurement errors on ancillary qubits. To achieve both accuracy and hardware efficiency of the SC decoder, we apply a binarized NN. We design a neural processing unit (NPU) for the decoder with SFQ-based digital circuits and evaluate it with a SPICE-level simulation. We evaluate the decoder performance by a quantum error simulator for the single logical qubit protection and the minimum operation of LS with code distances up to 13, and it achieves 2.5% and 1.0% accuracy thresholds, respectively.

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arXiv2022

Generalized quantum subspace expansion

One of the major challenges for erroneous quantum computers is undoubtedly the control over the effect of noise. Considering the rapid growth of available quantum resources that are not fully fault-tolerant, it is crucial to develop practical hardware-friendly quantum error mitigation (QEM) techniques to suppress unwanted errors. Here, we propose a novel generalized quantum subspace expansion method which can handle stochastic, coherent, and algorithmic errors in quantum computers. By fully exploiting the substantially extended subspace, we can efficiently mitigate the noise present in the spectra of a given Hamiltonian, without relying on any information of noise. The performance of our method is discussed under two highly practical setups: the quantum subspaces are mainly spanned by powers of the noisy state $ρ^m$ and a set of error-boosted states, respectively. We numerically demonstrate in both situations that we can suppress errors by orders of magnitude, and show that out protocol inherits the advantages of previous error-agnostic QEM techniques as well as overcoming their drawbacks.

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arXiv2021

Quantum algorithm for calculation of transition amplitudes in hybrid tensor networks

The hybrid tensor network approach allows us to perform calculations on systems larger than the scale of a quantum computer. However, when calculating transition amplitudes, there is a problem that the number of terms to be measured increases exponentially with that of contracted operators. The problem is caused by the fact that the contracted operators are represented as non-Hermitian operators. In this study, we propose a method for the hybrid tensor network calculation that contracts non-Hermitian operators without the exponential increase of the number of terms. In the proposed method, calculations of transition amplitudes are performed by combining the singular value decomposition of the contracted non-Hermitian operators with the Hadamard test. The method significantly extends the applicability of the hybrid tensor network approach.

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arXiv2021

Quantum error mitigation as a universal error-minimization technique: applications from NISQ to FTQC eras

In the early years of fault-tolerant quantum computing (FTQC), it is expected that the available code distance and the number of magic states will be restricted due to the limited scalability of quantum devices and the insufficient computational power of classical decoding units. Here, we integrate quantum error correction and quantum error mitigation into an efficient FTQC architecture that effectively increases the code distance and $T$-gate count at the cost of constant sampling overheads in a wide range of quantum computing regimes. For example, while we need $10^4$ to $10^{10}$ logical operations for demonstrating quantum advantages from optimistic and pessimistic points of view, we show that we can reduce the required number of physical qubits by $80\%$ and $45\%$ in each regime. From another perspective, when the achievable code distance is up to about 11, our scheme allows executing $10^3$ times more logical operations. This scheme will dramatically alleviate the required computational overheads and hasten the arrival of the FTQC era.

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arXiv2021

Qulacs: a fast and versatile quantum circuit simulator for research purpose

To explore the possibilities of a near-term intermediate-scale quantum algorithm and long-term fault-tolerant quantum computing, a fast and versatile quantum circuit simulator is needed. Here, we introduce Qulacs, a fast simulator for quantum circuits intended for research purpose. We show the main concepts of Qulacs, explain how to use its features via examples, describe numerical techniques to speed-up simulation, and demonstrate its performance with numerical benchmarks.

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arXiv2021

Quantum circuits for exact unitary $t$-designs and applications to higher-order randomized benchmarking

A unitary $t$-design is a powerful tool in quantum information science and fundamental physics. Despite its usefulness, only approximate implementations were known for general $t$. In this paper, we provide for the first time quantum circuits that generate exact unitary $t$-designs for any $t$ on an arbitrary number of qubits. Our construction is inductive and is of practical use in small systems. We then introduce a $t$-th order generalization of randomized benchmarking ($t$-RB) as an application of exact $2t$-designs. We particularly study the $2$-RB in detail and show that it reveals self-adjointness of quantum noise, a new metric related to the feasibility of quantum error correction (QEC). We numerically demonstrate that the $2$-RB in one- and two-qubit systems is feasible, and experimentally characterize background noise of a superconducting qubit by the $2$-RB. It is shown from the experiment that interactions with adjacent qubits induce the noise that may result in an obstacle toward the realization of QEC.

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arXiv2021

QECOOL: On-Line Quantum Error Correction with a Superconducting Decoder for Surface Code

Due to the low error tolerance of a qubit, detecting and correcting errors on it is essential for fault-tolerant quantum computing. Surface code (SC) associated with its decoding algorithm is one of the most promising quantum error correction (QEC) methods. % One of the challenges of QEC is its high complexity and computational demand. QEC needs to be very power-efficient since the power budget is limited inside of a dilution refrigerator for superconducting qubits by which one of the most successful quantum computers (QCs) is built. In this paper, we propose an online-QEC algorithm and its hardware implementation with SFQ-based superconducting digital circuits. We design a key building block of the proposed hardware with an SFQ cell library and evaluate it by the SPICE-level simulation. Each logic element is composed of about 3000 Josephson junctions and power consumption is about 2.78 uW when operating with 2 GHz clock frequency which meets the required decoding speed. Our decoder is simulated on a quantum error simulator for code distances 5 to 13 and achieves a 1.0% accuracy threshold.

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arXiv2020

Quadratic Clifford expansion for efficient benchmarking and initialization of variational quantum algorithms

Variational quantum algorithms are considered to be appealing applications of near-term quantum computers. However, it has been unclear whether they can outperform classical algorithms or not. To reveal their limitations, we must seek a technique to benchmark them on large scale problems. Here, we propose a perturbative approach for efficient benchmarking of variational quantum algorithms. The proposed technique performs perturbative expansion of a circuit consisting of Clifford and Pauli rotation gates, which is enabled by exploiting the classical simulatability of Clifford circuits. Our method can be applied to a wide family of parameterized quantum circuits consisting of Clifford gates and single-qubit rotation gates. The approximate optimal parameter obtained by the method can also serve as an initial guess for further optimizations on a quantum device, which can potentially solve the so-called ``barren-plateau'' problem. As the first application of the method, we perform a benchmark of so-called hardware-efficient-type ansatzes when they are applied to the VQE of one-dimensional hydrogen chains up to $\mathrm{H}_{24}$, which corresponds to $48$-qubit system, using a standard workstation.

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arXiv2019

General framework for constructing fast and near-optimal machine-learning-based decoder of the topological stabilizer codes

Quantum error correction is an essential technique for constructing a scalable quantum computer. In order to implement quantum error correction with near-term quantum devices, a fast and near-optimal decoding method is demanded. A decoder based on machine learning is considered as one of the most viable solutions for this purpose, since its prediction is fast once training has been done, and it is applicable to any quantum error correcting code and any noise model. So far, various formulations of the decoding problem as the task of machine learning have been proposed. Here, we discuss general constructions of machine-learning-based decoders. We found several conditions to achieve near-optimal performance, and proposed a criterion which should be optimized when a size of training data set is limited. We also discuss preferable constructions of neural networks, and proposed a decoder using spatial structures of topological codes using a convolutional neural network. We numerically show that our method can improve the performance of machine-learning-based decoders in various topological codes and noise models.

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arXiv2018

Variational Quantum Gate Optimization

We propose a gate optimization method, which we call variational quantum gate optimization (VQGO). VQGO is a method to construct a target multi-qubit gate by optimizing a parametrized quantum circuit which consists of tunable single-qubit gates with high fidelities and fixed multi-qubit gates with limited controlabilities. As an example, we apply the proposed scheme to the models relevant to superconducting qubit systems. We show in numerical simulations that the high-fidelity CNOT gate can be constructed with VQGO using cross-resonance gates with finite crosstalk. We also demonstrate that fast and a high-fidelity four-qubit syndrome extraction can be implemented with simultaneous cross-resonance drives even in the presence of non-commutative crosstalk. VQGO gives a pathway for designing efficient gate operations for quantum computers.

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arXiv2017

Efficient simulation of quantum error correction under coherent error based on non-unitary free-fermionic formalism

In order to realize fault-tolerant quantum computation, tight evaluation of error threshold under practical noise models is essential. While non-Clifford noise is ubiquitous in experiments, the error threshold under non-Clifford noise cannot be efficiently treated with known approaches. We construct an efficient scheme for estimating the error threshold of one-dimensional quantum repetition code under non-Clifford noise. To this end, we employ non-unitary free-fermionic formalism for efficient simulation of the one-dimensional repetition code under coherent noise. This allows us to evaluate the effect of coherence in noise on the error threshold without any approximation. The result shows that the error threshold becomes one third when noise is fully coherent. Our scheme is also applicable to the surface code undergoing a specific coherent noise model. The dependence of the error threshold on noise coherence can be explained with a leading-order analysis with respect to coherence terms in the noise map. We expect that this analysis is also valid for the surface code since it is a two-dimensional extension of the one-dimensional repetition code. Moreover, since the obtained threshold is accurate, our results can be used as a benchmark for approximation or heuristic schemes for non-Clifford noise.

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