arXiv subjects

Naoki Yamamoto

Publications and source records attributed to Naoki Yamamoto.

At least 91 records · Page 5Linked to original sources

arXiv2023

Quantum algorithm for position weight matrix matching

We propose two quantum algorithms for a problem in bioinformatics, position weight matrix (PWM) matching, which aims to find segments (sequence motifs) in a biological sequence such as DNA and protein that have high scores defined by the PWM and are thus of informational importance related to biological function. The two proposed algorithms, the naive iteration method and the Monte-Carlo-based method, output matched segments, given the oracular accesses to the entries in the biological sequence and the PWM. The former uses quantum amplitude amplification (QAA) for sequence motif search, resulting in the query complexity scaling on the sequence length $n$, the sequence motif length $m$ and the number of the PWMs $K$ as $\widetilde{O}\left(m\sqrt{Kn}\right)$, which means speedup over existing classical algorithms with respect to $n$ and $K$. The latter also uses QAA, and further, quantum Monte Carlo integration for segment score calculation, instead of iteratively operating quantum circuits for arithmetic in the naive iteration method; then it provides the additional speedup with respect to $m$ in some situation. As a drawback, these algorithms use quantum random access memories and their initialization takes $O(n)$ time. Nevertheless, our algorithms keep the advantage especially when we search matches in a sequence for many PWMs in parallel.

quant-ph↗

arXiv2023

Overfitting in quantum machine learning and entangling dropout

The ultimate goal in machine learning is to construct a model function that has a generalization capability for unseen dataset, based on given training dataset. If the model function has too much expressibility power, then it may overfit to the training data and as a result lose the generalization capability. To avoid such overfitting issue, several techniques have been developed in the classical machine learning regime, and the dropout is one such effective method. This paper proposes a straightforward analogue of this technique in the quantum machine learning regime, the entangling dropout, meaning that some entangling gates in a given parametrized quantum circuit are randomly removed during the training process to reduce the expressibility of the circuit. Some simple case studies are given to show that this technique actually suppresses the overfitting.

quant-ph↗

arXiv2022

Chiral effects in astrophysics and cosmology

The microscopic quantum nature of elementary particles, chirality, leads to macroscopic phenomena like the chiral anomaly, chiral magnetic effect, and chiral plasma instability. We review recent progress of the studies of these chiral effects in high-energy astrophysics, such as pulsar kicks, magnetars, and core-collapse supernovae, and early Universe cosmology, such as the primordial magnetic field, baryogenesis, and chiral gravitational waves. We also provide a pedagogical introduction to the chiral effects and low-energy effective theories to describe them in and out of equilibrium -- the chiral (magneto)hydrodynamics, chiral kinetic theory, and chiral radiation transport theory for neutrinos.

astro-ph.CO↗

arXiv2022

Jarzynski-like Equality of Nonequilibrium Information Production Based on Quantum Cross Entropy

The two-time measurement scheme is well studied in the context of quantum fluctuation theorem. However, it becomes infeasible when the random variable determined by a single measurement trajectory is associated with the von-Neumann entropy of the quantum states. We employ the one-time measurement scheme to derive a Jarzynski-like equality of nonequilibrium information production by proposing an information production distribution based on the quantum cross entropy. The derived equality further enables one to explore the roles of the quantum cross entropy in quantum communications, quantum machine learning and quantum thermodynamics.

quant-ph↗

arXiv2022

Unstable Nambu-Goldstone modes

Nambu-Goldstone (NG) modes for 0-form and higher-form symmetries can become unstable in the presence of background fields. Examples include the instability of a photon with a time-dependent axion background or with a chirality imbalance, known as the chiral plasma instability, and the instability of a dynamical axion with a background electric field. We show that all these phenomena can be universally described by a symmetry algebra for 0-form and higher-form symmetries. We prove a counting rule for the number of unstable NG modes in terms of correlation functions of broken symmetry generators. Based on our unified description, we further give a simple new example where one of the NG modes associated with the spontaneous 0-form symmetry breaking $U(1) \times U(1) \to \{1\}$ becomes unstable.

hep-th↗

arXiv2022

Quantum Fisher kernel for mitigating the vanishing similarity issue

Quantum kernel method is a machine learning model exploiting quantum computers to calculate the quantum kernels (QKs) that measure the similarity between data. Despite the potential quantum advantage of the method, the commonly used fidelity-based QK suffers from a detrimental issue, which we call the vanishing similarity issue; detecting the difference between data becomes hard with the increase of the number of qubits, due to the exponential decrease of the expectation and the variance of the QK. This implies the need to design QKs alternative to the fidelity-based one. In this work, we propose a new class of QKs called the quantum Fisher kernels (QFKs) that take into account the geometric structure of the data source. We analytically and numerically demonstrate that the QFK based on the anti-symmetric logarithmic derivatives (ALDQFK) can avoid the issue when the alternating layered ansatzs (ALAs) are used, while the fidelity-based QK cannot even with the ALAs. Moreover, the Fourier analysis numerically elucidates that the ALDQFK can have expressivity comparable to that of the fidelity-based QK. These results indicate that the QFK paves the way for practical applications of quantum machine learning with possible quantum advantages.

quant-ph↗

arXiv2022

Generative model for learning quantum ensemble via optimal transport loss

Generative modeling is an unsupervised machine learning framework, that exhibits strong performance in various machine learning tasks. Recently we find several quantum version of generative model, some of which are even proven to have quantum advantage. However, those methods are not directly applicable to construct a generative model for learning a set of quantum states, i.e., ensemble. In this paper, we propose a quantum generative model that can learn quantum ensemble, in an unsupervised machine learning framework. The key idea is to introduce a new loss function calculated based on optimal transport loss, which have been widely used in classical machine learning due to its several good properties; e.g., no need to ensure the common support of two ensembles. We then give in-depth analysis on this measure, such as the scaling property of the approximation error. We also demonstrate the generative modeling with the application to quantum anomaly detection problem, that cannot be handled via existing methods. The proposed model paves the way for a wide application such as the health check of quantum devices and efficient initialization of quantum computation.

quant-ph↗

arXiv2022

Chiral gravitational waves from thermalized neutrinos in the early Universe

We investigate polarized gravitational waves generated by chiral fermions in the early Universe. In particular, we focus on the contribution from left-handed neutrinos in thermal equilibrium with finite temperature and chemical potential in the radiation dominated era. We compute the correlation functions of gravitational fields pertinent to the Stokes parameter $V$ characterizing the circular polarization of gravitational waves in the Minkowski and expanding spacetime backgrounds. In the expanding universe, we find that the thermalized neutrinos induce a non-vanishing $V$ linear to the neutrino degeneracy parameter and wavenumber of gravitational waves in the long wavelength region. While the magnitude of the gravitational waves generated by thermal neutrinos is too small to be detectable by current and planned third generation gravitational wave detectors, their observations by future generation detectors for ultra-high frequency regimes could provide information on the neutrino degeneracy parameter in the early Universe.

astro-ph.CO↗

arXiv2022

Type checking data structures more complex than trees

Graphs are a generalized concept that encompasses more complex data structures than trees, such as difference lists, doubly-linked lists, skip lists, and leaf-linked trees. Normally, these structures are handled with destructive assignments to heaps, which is opposed to a purely functional programming style and makes verification difficult. We propose a new purely functional language, $λ_{GT}$, that handles graphs as immutable, first-class data structures with a pattern matching mechanism based on Graph Transformation and developed a new type system, $F_{GT}$, for the language. Our approach is in contrast with the analysis of pointer manipulation programs using separation logic, shape analysis, etc. in that (i) we do not consider destructive operations but pattern matchings over graphs provided by the new higher-level language that abstract pointers and heaps away and that (ii) we pursue what properties can be established automatically using a rather simple typing framework.

cs.PL↗

arXiv2022

Deterministic and random features for large-scale quantum kernel machine

Quantum machine learning (QML) is the spearhead of quantum computer applications. In particular, quantum neural networks (QNN) are actively studied as the method that works both in near-term quantum computers and fault-tolerant quantum computers. Recent studies have shown that supervised machine learning with QNN can be interpreted as the quantum kernel method (QKM), suggesting that enhancing the practicality of the QKM is the key to building near-term applications of QML. However, the QKM is also known to have two severe issues. One is that the QKM with the (inner-product based) quantum kernel defined in the original large Hilbert space does not generalize; namely, the model fails to find patterns of unseen data. The other one is that the classical computational cost of the QKM increases at least quadratically with the number of data, and therefore, QKM is not scalable with data size. This paper aims to provide algorithms free from both of these issues. That is, for a class of quantum kernels with generalization capability, we show that the QKM with those quantum kernels can be made scalable by using our proposed deterministic and random features. Our numerical experiment, using datasets including $O(1,000) \sim O(10,000)$ training data, supports the validity of our method.

quant-ph↗

arXiv2022

Numerical analysis of quantum circuits for state preparation and unitary operator synthesis

We perform optimal-control-theory calculations to determine the minimum number of two-qubit CNOT gates needed to perform quantum state preparation and unitary operator synthesis for few-qubit systems. By considering all possible gate configurations, we determine the maximum achievable fidelity as a function of quantum circuit size. This information allows us to identify the minimum circuit size needed for a specific target operation and enumerate the different gate configurations that allow a perfect implementation of the operation. We find that there are a large number of configurations that all produce the desired result, even at the minimum number of gates. We also show that the number of entangling gates can be reduced if we use multi-qubit entangling gates instead of two-qubit CNOT gates, as one might expect based on parameter counting calculations. In addition to treating the general case of arbitrary target states or unitary operators, we apply the numerical approach to the special case of synthesizing the multi-qubit Toffoli gate. This approach can be used to investigate any other specific few-qubit task and provides insight into the tightness of different bounds in the literature.

quant-ph↗

arXiv2022

Quantum Noise-Induced Reservoir Computing

Quantum computing has been moving from a theoretical phase to practical one, presenting daunting challenges in implementing physical qubits, which are subjected to noises from the surrounding environment. These quantum noises are ubiquitous in quantum devices and generate adverse effects in the quantum computational model, leading to extensive research on their correction and mitigation techniques. But do these quantum noises always provide disadvantages? We tackle this issue by proposing a framework called quantum noise-induced reservoir computing and show that some abstract quantum noise models can induce useful information processing capabilities for temporal input data. We demonstrate this ability in several typical benchmarks and investigate the information processing capacity to clarify the framework's processing mechanism and memory profile. We verified our perspective by implementing the framework in a number of IBM quantum processors and obtained similar characteristic memory profiles with model analyses. As a surprising result, information processing capacity increased with quantum devices' higher noise levels and error rates. Our study opens up a novel path for diverting useful information from quantum computer noises into a more sophisticated information processor.

quant-ph↗

arXiv2022

Development of an electron impact ion source with high ionization efficiency for future planetary missions

Ion sources using electron impact ionization (EI) methods have been widely accepted in mass spectrometry for planetary exploration missions because of their simplicity. Previous space-borne mass spectrometers were primarily designed with the EI method using rhenium tungsten alloy filaments, enabling up to 200 uA emission in typical cases. The emission level is desired to be enhanced because the sensitivity of mass spectrometers is a critical requirement for the future in situ mass spectrometry related to the measurement of trace components in planetary samples. In this study, we developed a new high-emission EI ion source using a Y2O3-coated iridium filament, which has a lower work function than rhenium tungsten alloy. The size of the ion source was 30 mm * 26 mm * 70 mm, and its weight was 70 g. We confirmed that when consuming 3.0 W power, the ion source emits more than 2 mA electrons, which is 10 times greater than the previous models electron emission level. Ionization efficiency of the EI ion source is proportional to the amount of electron emission, which implies our new model increased the ionization efficiency 10 times. We conducted performance tests on the prototype with the 3.0 W heating condition, confirming a high ionization efficiency (10^4 nA/Pa). In addition, we conducted endurance tests of the ion source and demonstrated the persistence of the ionization efficiency for 30 min * 100 cycles.

astro-ph.EP↗

arXiv2022

Chiral plasma instability and inverse cascade from nonequilibrium left-handed neutrinos in core-collapse supernovae

We show that the backreaction of left-handed neutrinos out of equilibrium on the matter sector induces an electric current proportional to a magnetic field even without a chiral imbalance for electrons in core-collapse supernovae. We derive the transport coefficient of this effect based on the recently formulated chiral radiation transport theory for neutrinos. This chiral electric current generates a strong magnetic field via the so-called chiral plasma instability, which could provide a new mechanism for the strong and stable magnetic field of magnetars. We also numerically study the physical origin of the inverse cascade of the magnetic energy in the magnetohydrodynamics including this current. Our results indicate that incorporating the chiral effects of neutrinos would drastically modify the hydrodynamic evolutions of supernovae, which may also be relevant to the explosion dynamics.

astro-ph.HE↗

arXiv2022

Approximate amplitude encoding in shallow parameterized quantum circuits and its application to financial market indicator

Efficient methods for loading given classical data into quantum circuits are essential for various quantum algorithms. In this paper, we propose an algorithm called Approximate Amplitude Encoding that can effectively load all the components of a given real-valued data vector into the amplitude of quantum state, while the previous proposal can only load the absolute values of those components. The key of our algorithm is to variationally train a shallow parameterized quantum circuit, using the results of two types of measurement; the standard computational-basis measurement plus the measurement in the Hadamard-transformed basis, introduced in order to handle the sign of the data components. The variational algorithm changes the circuit parameters so as to minimize the sum of two costs corresponding to those two measurement basis, both of which are given by the efficiently-computable maximum mean discrepancy. We also consider the problem of constructing the singular value decomposition entropy via the stock market dataset to give a financial market indicator; a quantum algorithm (the variational singular value decomposition algorithm) is known to produce a solution faster than classical, which yet requires the sign-dependent amplitude encoding. We demonstrate, with an in-depth numerical analysis, that our algorithm realizes loading of time-series of real stock prices on quantum state with small approximation error, and thereby it enables constructing an indicator of the financial market based on the stock prices.

quant-ph↗

arXiv2022

Photonic spin Hall effect from quantum kinetic theory in curved spacetime

Based on quantum field theory, we formulate the Wigner function and quantum kinetic theory for polarized photons in curved spacetimes which admit a covariantly constant timelike vector. From this framework, the photonic chiral/zilch vortical effects are reproduced in a rigidly rotating coordinate. In a spatially inhomogeneous coordinate, we derive the spin Hall effect for the photon helicity current and energy current in equilibrium. Our derivation reveals that such photonic Hall effect are related to the photonic vortical effect via the Lorentz invariance and their transport coefficients match each other.

hep-th↗

arXiv2022

Pulse-engineered Controlled-V gate and its applications on superconducting quantum device

In this paper, we demonstrate that, by employing OpenPulse design kit for IBM superconducting quantum devices, the controlled-V gate (CV gate) can be implemented in about half the gate time to the controlled-X (CX or CNOT gate) and consequently 65.5\% reduced gate time compared to the CX-based implementation of CV. Then, based on the theory of Cartan decomposition, we characterize the set of all two-qubit gates implemented with only two or three CV gates; using pulse-engineered CV gates enables us to implement these gates with shorter gate time and possibly better gate fidelity than the CX-based one, as actually demonstrated in two examples. Moreover, we showcase the improvement of linearly-coupled three-qubit Toffoli gate, by implementing it with the pulse-engineered CV gate, both in gate time and the averaged output-state fidelity. These results imply the importance of our CV gate implementation technique, which, as an additional option for the basis gate set design, may shorten the overall computation time and consequently improve the precision of several quantum algorithms executed on a real device.

quant-ph↗

arXiv2022

Speed limits for two-qubit gates with weakly anharmonic qubits

We consider the implementation of two-qubit gates when the physical systems used to realize the qubits possess additional quantum states in the accessible energy range. We use optimal control theory to determine the maximum achievable gate speed for two-qubit gates in the qubit subspace of the many-level Hilbert space, and we analyze the effect of the additional quantum states on the gate speed. We identify two competing mechanisms. On one hand, higher energy levels are generally more strongly coupled to each other. Under suitable conditions, this stronger coupling can be utilized to make two-qubit gates significantly faster than the reference value based on simple qubits. On the other hand, a weak anharmonicity constrains the speed at which the system can be adequately controlled: according to the intuitive picture, faster operations require stronger control fields, which are more likely to excite higher levels in a weakly anharmonic system, which in turn leads to faster decoherence and uncontrolled leakage outside the qubit space. In order to account for this constraint, we modify the pulse optimization algorithm to avoid pulses that lead to appreciable population of the higher levels. In this case we find that the presence of the higher levels can lead to a significant reduction in the maximum achievable gate speed. We also compare the optimal-control gate speeds with those obtained using the cross-resonance/selective-darkening gate protocol. We find that the latter, with some parameter optimization, can be used to achieve a relatively fast implementation of the CNOT gate. These results can help the search for optimized gate implementations in realistic quantum computing architectures, such as those based on superconducting circuits. They also provide guidelines for desirable conditions on anharmonicity that allow optimal utilization of the higher levels to achieve fast quantum gates.

quant-ph↗