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.Comment: 7 pages, 8 figure

Measurement optimization of variational quantum simulation by classical shadow and derandomization

Simulating large quantum systems is the ultimate goal of quantum computing. Variational quantum simulation (VQS) gives us a tool to achieve the goal in near-term devices by distributing the computation load to both classical and quantum computers. However, as the size of the quantum system becomes large, the execution of VQS becomes more and more challenging. One of the most severe challenges is the drastic increase in the number of measurements; for example, the number of measurements tends to increase by the fourth power of the number of qubits in a quantum simulation with a chemical Hamiltonian. This work aims to dramatically decrease the number of measurements in VQS by recently proposed shadow-based strategies such as classical shadow and derandomization. Even though previous literature shows that shadow-based strategies successfully optimize measurements in the variational quantum optimization (VQO), how to apply them to VQS was unclear due to the gap between VQO and VQS in measuring observables. In this paper, we bridge the gap by changing the way of measuring observables in VQS and propose an algorithm to optimize measurements in VQS by shadow-based strategies. Our theoretical analysis not only reveals the advantage of using our algorithm in VQS but theoretically supports using shadow-based strategies in VQO, whose advantage has only been given numerically. Additionally, our numerical experiment shows the validity of using our algorithm with a quantum chemical system

Expressive Quantum Supervised Machine Learning using Kerr-nonlinear Parametric Oscillators

Quantum machine learning with variational quantum algorithms (VQA) has been actively investigated as a practical algorithm in the noisy intermediate-scale quantum (NISQ) era. Recent researches reveal that the data reuploading, which repeatedly encode classical data into quantum circuit, is necessary for obtaining the expressive quantum machine learning model in the conventional quantum computing architecture. However, the data reuploding tends to require large amount of quantum resources, which motivates us to find an alternative strategy for realizing the expressive quantum machine learning efficiently. In this paper, we propose quantum machine learning with Kerr-nonlinear Parametric Oscillators (KPOs), as another promising quantum computing device. The key idea is that we use not only the ground state and first excited state but also use higher excited states, which allows us to use a large Hilbert space even if we have a single KPO. Our numerical simulations show that the expressibility of our method with only one mode of the KPO is much higher than that of the conventional method with six qubits. Our results pave the way towards resource efficient quantum machine learning, which is essential for the practical applications in the NISQ era.Comment: 13 pages, 8 figure

Inverse Problem of Cosmic-Ray Electron/Positron from Dark Matter

We discuss the possibility of solving the inverse problem of the cosmic-ray electron/positron from decaying/annihilating dark matter, and show simple analytic formulae to reconstruct the source spectrum of the electron/positron from the observed flux. We also illustrate our approach by applying the obtained formula to the just released Fermi data as well as the new HESS data.Comment: 16 pages, 6 figure

Identifying the Origin of Longevity of Metastable Stau at the LHC

In the framework of the supersymmetric standard model, the lighter stau often becomes long-lived. Such longevity of the stau is realized in three well-motivated scenarios: (A) the stau is the next-to-lightest supersymmetric particle (NLSP) and the gravitino is the lightest supersymmetric particle (LSP), (B) the stau is the LSP and R-parity is slightly violated, and (C) the stau is NLSP, the neutralino is the LSP, and the their masses are degenerate. We study the event topology and the decay of the stopping stau at the hadron calorimeter at the LHC, and show that it is possible to identify the reason why the stau becomes long-lived.Comment: 14 pages, 3 figures, 1 tabl

The generative quantum eigensolver (GQE) and its application for ground state search

We introduce the generative quantum eigensolver (GQE), a novel method for applying classical generative models for quantum simulation. The GQE algorithm optimizes a classical generative model to produce quantum circuits with desired properties. Here, we develop a transformer-based implementation, which we name the generative pre-trained transformer-based (GPT) quantum eigensolver (GPT-QE), leveraging both pre-training on existing datasets and training without any prior knowledge. We demonstrate the effectiveness of training and pre-training GPT-QE in the search for ground states of electronic structure Hamiltonians. GQE strategies can extend beyond the problem of Hamiltonian simulation into other application areas of quantum computing.Comment: 16 pages, 7 figure

Probing High Reheating Temperature Scenarios at the LHC with Long-Lived Staus

We investigate the possibility of probing high reheating temperature scenarios at the LHC, in supersymmetric models where the gravitino is the lightest supersymmetric particle, and the stau is the next-to-lightest supersymmetric particle. In such scenarios, the big-bang nucleosynthesis and the gravitino abundance give a severe upper bound on the gluino mass. We find that, if the reheating temperature is \sim 10^8 GeV or higher, the scenarios can be tested at the LHC with an integrated luminosity of O(1 fb^{-1}) at \sqrt{s}=7 TeV in most of the parameter space.Comment: 17 pages, 5 figures, minor modification

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

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 load only 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 data set 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