Optimization of circuits is an essential task for both quantum and classical computers to improve their efficiency. In contrast, classical logic optimization is known to be difficult, and a lot of heuristic approaches have been developed so far. In this study, we define and construct a quantum algorithmic primitive called quantum circuit unoptimization, which makes a given quantum circuit complex by introducing some redundancies while preserving circuit equivalence, i.e., the inverse operation of circuit optimization. Using quantum circuit unoptimization, we propose the quantum circuit equivalence test, a decision problem contained both in the NP and BQP classes but is not trivially included in the P class. Furthermore, as a practical application, we construct concrete unoptimization recipes to generate compiler benchmarks and evaluate circuit optimization performance using Qiskit and Pytket. Our numerical simulations demonstrate that quantum circuit unoptimizer systematically generates redundant circuits that are challenging for compilers to optimize, which can be used to compare the performance of different compilers and improve them. We also offer potential applications of quantum circuit unoptimization, such as generating quantum advantageous machine learning datasets and quantum computer fidelity benchmarks.
There are considerable obstacles against realizing practical quantum computing: a significant amount of computation errors and the restricted qubit count. As a unified method of noise-agnostic quantum error mitigation (QEM) methods, i.e., the quantum subspace expansion and virtual purification, a generalized quantum subspace expansion (GSE) has recently been proposed that is significantly robust against stochastic and coherent errors. However, GSE requires entangled measurements between copies of the quantum states, which is a significant drawback under the current situation of the restricted number of qubits and their connectivity. In this work, we propose a resource-efficient implementation of GSE, which we name "Dual-GSE", circumventing significant overheads of state copies by constructing an ansatz of error-mitigated quantum states via dual-state purification. Remarkably, Dual-GSE can further simulate larger quantum systems beyond the size of available quantum hardware with a suitable ansatz construction inspired by divide-and-conquer methods that forge entanglement classically. This also significantly reduces the measurement overhead because we only need to measure subsystems' Pauli operators. The proposed method is demonstrated by a numerical simulation of the eight-qubit transverse-field Ising model, showing that our method estimates the ground state energy with high precision under gate noise with low mitigation overhead and practical sampling cost.
Analog and digital quantum simulators can efficiently simulate quantum many-body systems that appear in natural phenomena. However, experimental limitations of near-term devices still make it challenging to perform the entire process of quantum simulation. The purification-based quantum simulation methods can alleviate the limitations in experiments such as the cooling temperature and noise from the environment, while this method has the drawback that it requires global entangled measurement with a prohibitively large number of measurements that scales exponentially with the system size. In this Letter, we propose that we can overcome these problems by restricting the entangled measurements to the vicinity of the local observables to be measured, when the locality of the system can be exploited. We provide theoretical guarantees that the global purification operation can be replaced with local operations under some conditions, in particular for the task of cooling and error mitigation. We furthermore give a numerical verification that the localized purification is valid even when conditions are not satisfied. Our method bridges the fundamental concept of locality with quantum simulators, and therefore expected to open a path to unexplored quantum many-body phenomena.
Tsubasa Ichikawa, Hideaki Hakoshima, Koji Inui, Kosuke Ito, Ryo Matsuda, Kosuke Mitarai, Koichi Miyamoto, Wataru Mizukami, Kaoru Mizuta, Toshio Mori, Yuichiro Nakano, Akimoto Nakayama, Ken N. Okada, Takanori Sugimoto, Souichi Takahira, Nayuta Takemori, Satoyuki Tsukano, Hiroshi Ueda, Ryo Watanabe, Yuichiro Yoshida, et al (1) Quantum computers (QCs), which work based on the law of quantum mechanics, are expected to be faster than classical computers in several computational tasks such as prime factoring and simulation of quantum many-body systems. In the last decade, research and development of QCs have rapidly advanced. Now hundreds of physical qubits are at our disposal, and one can find several remarkable experiments actually outperforming the classical computer in a specific computational task. On the other hand, it is unclear what the typical usages of the QCs are. Here we conduct an extensive survey on the papers that are posted in the quant-ph section in arXiv and claim to have used QCs in their abstracts. To understand the current situation of the research and development of the QCs, we evaluated the descriptive statistics about the papers, including the number of qubits employed, QPU vendors, application domains and so on. Our survey shows that the annual number of publications is increasing, and the typical number of qubits employed is about six to ten, growing along with the increase in the quantum volume (QV). Most of the preprints are devoted to applications such as quantum machine learning, condensed matter physics, and quantum chemistry, while quantum error correction and quantum noise mitigation use more qubits than the other topics. These imply that the increase in QV is fundamentally relevant, and more experiments for quantum error correction, and noise mitigation using shallow circuits with more qubits will take place.
Quantum computation is expected to accelerate certain computational task over classical counterpart. Its most primitive advantage is its ability to sample from classically intractable probability distributions. A promising approach to make use of this fact is the so-called quantum-enhanced Markov chain Monte Carlo (MCMC) [D. Layden, et al., arXiv:2203.12497 (2022)] which uses outputs from quantum circuits as the proposal distributions. In this work, we propose the use of Quantum Alternating Operator Ansatz (QAOA) for quantum-enhanced MCMC and provide a strategy to optimize its parameter to improve convergence speed while keeping its depth shallow. The proposed QAOA-type circuit is designed to satisfy the specific constraint which quantum-enhanced MCMC requires with arbitrary parameters. Through our extensive numerical analysis, we find a correlation in certain parameter range between an experimentally measurable value, acceptance rate of MCMC, and the spectral gap of the MCMC transition matrix, which determines the convergence speed. This allows us to optimize the parameter in the QAOA circuit and achieve quadratic speedup in convergence. Since MCMC is used in various areas such as statistical physics and machine learning makes, this work represents an important step toward realizing practical quantum advantage with currently available quantum computers through quantum-enhanced MCMC.
We propose a few-body quantum phenomenon, which manifests itself through stochastic state preparations and measurements followed by a conditioned post-processing procedure. We show two experimental protocols to implement these phenomena with existing quantum computers, and examine their feasibility by using simulations. Our simulation results suggest that the experimental demonstration is feasible if we repeat the state preparations and measurements about thirty thousand times to three-qubit systems.
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.
Quantum metrology with entangled resources aims to achieve sensitivity beyond the standard quantum limit by harnessing quantum effects even in the presence of environmental noise. So far, sensitivity has been mainly discussed from the viewpoint of reducing statistical errors under the assumption of perfect knowledge of a noise model. However, we cannot always obtain complete information about a noise model due to coherence time fluctuations, which are frequently observed in experiments. Such unknown fluctuating noise leads to systematic errors and nullifies the quantum advantages. Here, we propose an error-mitigated quantum metrology that can filter out unknown fluctuating noise with the aid of purification-based quantum error mitigation. We demonstrate that our protocol mitigates systematic errors and recovers superclassical scaling in a practical situation with time-inhomogeneous bias-inducing noise. Our results reveal the usefulness of purification-based error mitigation for unknown fluctuating noise, thus paving the way not only for practical quantum metrology but also for quantum computation affected by such noise.
The transverse-field Ising model is one of the fundamental models in quantum many-body systems, yet a full understanding of its dynamics remains elusive in higher than one dimension. Here, we show for the first time the breakdown of ergodicity in $d$-dimensional Ising models with a weak transverse field in a prethermal regime. We demonstrate that novel Hilbert-space fragmentation occurs in the effective non-integrable model with $d\geq2$ as a consequence of only one emergent global conservation law of the domain wall number. Our results indicate nontrivial initial-state dependence for non-equilibrium dynamics of the Ising models in a weak transverse field.
The Gibbs partition function is an important quantity in describing statistical properties of a system in thermodynamic equilibrium. There are several proposals to calculate the partition functions on near-team quantum computers. However, the existing schemes require many copies of the Gibbs states to perform an extrapolation for the calculation of the partition function, and these could be costly performed on the near-term quantum computers. Here, we propose an efficient scheme to calculate the Gibbs function with the imaginary time evolution. To calculate the Gibbs function of $N$ qubits, only $2N$ qubits are required in our scheme. After preparing Gibbs states with different temperatures by using the imaginary time evolution, we measure the overlap between them on a quantum circuit, and this allows us to calculate the Gibbs partition function.
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 $\rho^m$ 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.
Variational quantum algorithms (VQAs) have been considered to be useful applications of noisy intermediate-scale quantum (NISQ) devices. Typically, in the VQAs, a parametrized ansatz circuit is used to generate a trial wave function, and the parameters are optimized to minimize a cost function. On the other hand, blind quantum computing (BQC) has been studied in order to provide the quantum algorithm with security by using cloud networks. A client with a limited ability to perform quantum operations hopes to have access to a quantum computer of a server, and BQC allows the client to use the server's computer without leakage of the client's information (such as input, running quantum algorithms, and output) to the server. However, BQC is designed for fault-tolerant quantum computing, and this requires many ancillary qubits, which may not be suitable for NISQ devices. Here, we propose an efficient way to implement the NISQ computing with guaranteed security for the client. In our architecture, only N+ 1 qubits are required, under an assumption that the form of ansatzes is known to the server, where N denotes the necessary number of the qubits in the original NISQ algorithms. The client only performs single-qubit measurements on an ancillary qubit sent from the server, and the measurement angles can specify the parameters for the ansatzes of the NISQ algorithms. No-signaling principle guarantees that neither parameters chosen by the client nor the outputs of the algorithm are leaked to the server. This work paves the way for new applications of NISQ devices.
We propose a quantum-enhanced heat engine with entanglement. The key feature of our scheme is superabsorption, which facilitates enhanced energy absorption by entangled qubits. Whereas a conventional engine with $N$ separable qubits provides power with a scaling of $P = \Theta (N)$, our engine uses superabsorption to provide power with a quantum scaling of $P = \Theta(N^2)$. This quantum heat engine also exhibits a scaling advantage over classical ones composed of $N$-particle Langevin systems. Our work elucidates the quantum properties allowing for the enhancement of performance.
A lot of attention has been paid to a quantum-sensing network for detecting magnetic fields in different positions. Recently, cryptographic quantum metrology was investigated where the information of the magnetic fields is transmitted in a secure way. However, sometimes, the positions where non-zero magnetic fields are generated could carry important information. Here, we propose an anonymous quantum sensor where an information of positions having non-zero magnetic fields is hidden after measuring magnetic fields with a quantum-sensing network. Suppose that agents are located in different positions and they have quantum sensors. After the quantum sensors are entangled, the agents implement quantum sensing that provides a phase information if non-zero magnetic fields exist, and POVM measurement is performed on quantum sensors. Importantly, even if the outcomes of the POVM measurement is stolen by an eavesdropper, information of the positions with non-zero magnetic fields is still unknown for the eavesdropper in our protocol. In addition, we evaluate the sensitivity of our proposed quantum sensors by using Fisher information when there are at most two positions having non-zero magnetic fields. We show that the sensitivity is finite unless these two (non-zero) magnetic fields have exactly the same amplitude. Our results pave the way for new applications of quantum-sensing network.
Recently, there have been significant developments to detect nuclear spins with an nitrogen vacancy (NV) center in diamond. However, due to the nature of the short range dipole-dipole interaction, it takes a long time to detect distant nuclear spins with the NV centers. Here, we propose a rapid detection of nuclear spins with an entanglement between the NV centers. We show that the necessary time to detect the nuclear spins with the entanglement is several orders of magnitude shorter than that with separable NV centers. Our result pave the way for new applications in nanoscale nuclear magnetic resonance spectroscopy.
Quantum magnetic field sensing is an important technology for material science and biology. Although experimental imperfections affect the sensitivity, repetitions of the measurements decrease the estimation uncertainty by a square root of the total number of the measurements if there are only statistical errors. However, it is difficult to precisely characterize the coherence time of the system because it fluctuates in time in realistic conditions, which induces systematic errors. In this case, due to residual bias of the measured values, estimation uncertainty cannot be lowered than a finite value even in the limit of the infinite number of measurements. On the basis of the fact that the decoherence dynamics in the so-called Zeno regime are not significant compared to other regimes, we propose a novel but very simple protocol to use measurements in the Zeno regime for reducing systematic errors. Our scheme allows the estimation uncertainty $\delta ^2 \omega$ to scale as $L^{1/4}$ where $L$ denotes the number of the measurements even when we cannot precisely characterize the coherence time.
We investigate a total thermodynamic entropy production rate of an isolated quantum system. In particular, we consider a quantum model of coupled harmonic oscillators in a star configuration, where a central harmonic oscillator (system) is coupled to a finite number of surrounding harmonic oscillators (bath). In this model, when the initial state of the total system is given by the tensor product of the Gibbs states of the system and the bath, every harmonic oscillator is always in a Gibbs state with a time-dependent temperature. This enables us to define time-dependent thermodynamic entropy for each harmonic oscillator and total nonequilibrium thermodynamic entropy as the summation of them. We analytically confirm that the total thermodynamic entropy satisfies the third law of thermodynamics. Our numerical solutions show that, even when the dynamics of the system is well approximated by the Gorini-Kossakowski-Sudarshan-Lindblad (GKSL)-type Markovian master equation, the total thermodynamic entropy production rate can be negative, while the total thermodynamic entropy satisfies the second law of thermodynamics. This result is a counterexample to the common belief that the total entropy production rate is non-negative when the system is under the GKSL-type Markovian dynamics.
Quantum error mitigation (QEM) has been proposed as an alternative method of quantum error correction to compensate errors in quantum systems without qubit overhead. While Markovian gate errors on digital quantum computers have been mainly considered previously, it is indispensable to discuss a relationship between QEM and non-Markovian errors because non-Markovian noise effects inevitably exist in most of the solid-state systems. In this work, we investigate the QEM for non-Markovian noise, and show that there is a clear relationship between costs for QEM and non-Markovian measures. As examples, we show several non-Markovian noise models to bridge a gap between our theoretical framework and concrete physical systems. This discovery may help in designing better QEM strategies for realistic quantum devices with non-Markovian environments.
The quantum remote sensing (QRS) is a scheme to add security about the measurement results of a qubit-based sensor. A client delegates a measurement task to a remote server that has a quantum sensor, and eavesdropper (Eve) steals every classical information stored in the server side. By using quantum properties, the QRS provides an asymmetricity about the information gain where the client gets more information about the sensing results than Eve. However, quantum states are fragile against decoherence, and so it is not clear whether such a QRS is practically useful under the effect of realistic noise. Here, we investigate the performance of the QRS with dephasing during the interaction with the target fields. In the QRS, the client and server need to share a Bell pair, and an imperfection of the Bell pair leads to a state preparation error in a systematic way on the server side for the sensing. We consider the effect of both dephasing and state preparation error. The uncertainty of the client side decreases with the square root of the repetition number $M$ for small $M$, which is the same scaling as the standard quantum metrology. On the other hand, for large $M$, the state preparation error becomes as relevant as the dephasing, and the uncertainty decreases logarithmically with $M$. We compare the information gain between the client and Eve. This leads us to obtain the conditions for the asymmetric gain to be maintained even under the effect of dephasing.
Quantum chemistry is one of the important applications of quantum information technology. Especially, an estimation of the energy gap between a ground state and excited state of a target Hamiltonian corresponding to a molecule is essential. In the previous approach, an energy of the ground state and that of the excited state are estimated separately, and the energy gap can be calculated from the subtraction between them. Here, we propose a direct estimation of the energy gap between the ground state and excited state of the target Hamiltonian with quantum annealing. The key idea is to combine a Ramsey type measurement with the quantum annealing. This provides an oscillating signal with a frequency of the energy gap, and a Fourier transform of the signal let us know the energy gap. Based on typical parameters of superconducting qubits, we numerically investigate the performance of our scheme when we estimate an energy gap between the ground state and first excited state of the Hamiltonian. We show robustness against non-adiabatic transitions between the ground state and first-excited state. Our results pave a new way to estimate the energy gap of the Hamiltonian for quantum chemistry.
Boltzmann machine is a powerful tool for modeling probability distributions that govern the training data. A thermal equilibrium state is typically used for Boltzmann machine learning to obtain a suitable probability distribution. The Boltzmann machine learning consists of calculating the gradient of the loss function given in terms of the thermal average, which is the most time consuming procedure. Here, we propose a method to implement the Boltzmann machine learning by using Noisy Intermediate-Scale Quantum (NISQ) devices. We prepare an initial pure state that contains all possible computational basis states with the same amplitude, and apply a variational imaginary time simulation. Readout of the state after the evolution in the computational basis approximates the probability distribution of the thermal equilibrium state that is used for the Boltzmann machine learning. We actually perform the numerical simulations of our scheme and confirm that the Boltzmann machine learning works well by our scheme.
Simultaneous quantum estimation of multiple parameters has recently become essential in quantum metrology. Although the ultimate sensitivity of a multiparameter quantum estimation in noiseless environments can beat the standard quantum limit that every classical sensor is bounded by, it is unclear whether the quantum sensor has an advantage over the classical one under realistic noise. In this work, we present a framework of the simultaneous estimation of multiple parameters with quantum sensors in a certain noisy environment. Our multiple parameters to be estimated are three components of an external magnetic field, and we consider the noise that causes only dephasing. We show that there is an optimal sensing time in the noisy environment and the sensitivity can beat the standard quantum limit when the noisy environment is non-Markovian.
The efficient detection of a single spin is a significant goal of improving the sensitivity of quantum magnetic-field sensors. Recent results show that a specific type of entanglement such as Greenberger-Horne-Zeilinger (GHZ) states can be used as a resource to improve the performance of single spin detection. However, scalable generation of the GHZ states is experimentally difficult to realize. It is desirable to use a practical entangled state that can be easily generated. In this paper, we propose the efficient detection of a single spin with Dicke states. We show a way to prepare and measure Dicke states via a global control. Moreover, we investigate how dephasing due to unwanted coupling with the environment affects the performance of our proposal, and show that single spin detection with Dicke states with dephasing has a significant advantage over the classical strategy with separable states. Our results are important toward realizing entanglement enhanced single spin detection.
Quantum annealing (QA) provides us with a way to solve combinatorial optimization problems. In the previous demonstration of the QA, a superconducting flux qubit (FQ) was used. However, the flux qubits in these demonstrations have a short coherence time such as tens of nano seconds. For the purpose to utilize quantum properties, it is necessary to use another qubit with a better coherence time. Here, we propose to use a capacitive-shunted flux qubit (CSFQ) for the implementation of the QA. The CSFQ has a few order of magnitude better coherence time than the FQ used in the QA. We theoretically show that, although it is difficult to perform the conventional QA with the CSFQ due to the form and strength of the interaction between the CSFQs, a spin-lock based QA can be implemented with the CSFQ even with the current technology. Our results pave the way for the realization of the practical QA that exploits quantum advantage with long-lived qubits.
Single spin detection is one of the important tasks in the field of quantum metrology. Many experiments about the single spin detection has been performed. However, due to the weak magnetic fields from the single spin, a long measurement time is required to achieve a reasonably high signal-to-noise ratio. Here, we propose an alternative way to realize rapid and accurate single spin detection with entangled states. While it is known that entanglement can improve the sensitivity to measure globally applied magnetic fields, we investigate a strategy to use the entanglement for detecting spatially inhomogeneous magnetic fields from the target single spin. We show that the entanglement significantly increases the signal to noise ratio for the single spin detection even under the effect of realistic noise. Our results pave the way for practical single spin detection.
We investigate the size scaling of the entanglement entropy (EE) in nonequilibrium steady states (NESSs) of a one-dimensional open quantum system with a random potential. It models a mesoscopic conductor, composed of a long quantum wire (QWR) with impurities and two electron reservoirs at zero temperature. The EE at equilibrium obeys the logarithmic law. However, in NESSs far from equilibrium the EE grows anomalously fast, obeying the `quasi volume law,' although the conductor is driven by the zero-temperature reservoirs. This anomalous behavior arises from both the far from equilibrium condition and multiple scatterings due to impurities.
Braneshop, and
the US National Science Foundation.
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