Variational quantum eigensolver for closed-shell molecules with non-bosonic corrections.

The realization of quantum advantage with noisy-intermediate-scale quantum (NISQ) machines has become one of the major challenges in computational sciences. Maintaining coherence of a physical system with more than ten qubits is a critical challenge that motivates research on compact system representations to reduce algorithm complexity. Toward this end, the variational quantum eigensolver (VQE) used to perform quantum simulations is considered to be one of the most promising algorithms for quantum chemistry in the NISQ era. We investigate reduced mapping of one spatial orbital to a single qubit to analyze the ground state energy in a way that the Pauli operators of qubits are mapped to the creation/annihilation of singlet pairs of electrons. To include the effect of non-bosonic (or non-paired) excitations, we introduce a simple correction scheme in the electron correlation model approximated by the geometrical mean of the bosonic (or paired) terms. Employing it in a VQE algorithm, we assess ground state energies of H2O, N2, and Li2O in good agreement with full configuration interaction (FCI) models respectively, using only 6, 8, and 12 qubits with quantum gate depths proportional to the squares of the qubit counts. With the adopted seniority-zero approximation that uses only one half of the qubit counts of a conventional VQE algorithm, we find that our non-bosonic correction method reaches reliable quantum chemistry simulations at least for the tested systems.

Fragment molecular orbital-based variational quantum eigensolver for quantum chemistry in the age of quantum computing.

Quantum computers offer significant potential for complex system analysis, yet their application in large systems is hindered by limitations such as qubit availability and quantum hardware noise. While the variational quantum eigensolver (VQE) was proposed to address these issues, its scalability remains limited. Many efforts, including new ansätze and Hamiltonian modifications, have been made to overcome these challenges. In this work, we introduced the novel Fragment Molecular Orbital/Variational Quantum Eigensolver (FMO/VQE) algorithm. This method combines the fragment molecular orbital (FMO) approach with VQE and efficiently utilizes qubits for quantum chemistry simulations. Employing the UCCSD ansatz, the FMO/VQE achieved an absolute error of just 0.053 mHa with 8 qubits in a [Formula: see text] system using the STO-3G basis set, and an error of 1.376 mHa with 16 qubits in a [Formula: see text] system with the 6-31G basis set. These results indicated a significant advancement in scalability over conventional VQE, maintaining accuracy with fewer qubits. Therefore, our FMO/VQE method exemplifies how integrating fragment-based quantum chemistry with quantum algorithms can enhance scalability, facilitating more complex molecular simulations and aligning with quantum computing advancements.

Continuous-variable quantum key distribution with time-division dual-quadrature homodyne detection.

We propose a novel heterodyne detection scheme for continuous-variable quantum key distribution (CVQKD), which measures both quadrature components of a quantum signal encoded in optical phase space. The proposed method uses time division to achieve identical performance to conventional heterodyne detection with only a single homodyne detection system. Our method also uses a Faraday-Michelson interferometer to make it independent of polarization drift and eliminate the need for dynamic polarization control. Our method is experimentally demonstrated using the Gaussian-modulated coherent-states (GMCS) protocol over a 20.06 km optical fiber channel, achieving an expected secret key rate of up to 0.187 Mbps.

Quantum Graph Neural Network Models for Materials Search.

Inspired by classical graph neural networks, we discuss a novel quantum graph neural network (QGNN) model to predict the chemical and physical properties of molecules and materials. QGNNs were investigated to predict the energy gap between the highest occupied and lowest unoccupied molecular orbitals of small organic molecules. The models utilize the equivariantly diagonalizable unitary quantum graph circuit (EDU-QGC) framework to allow discrete link features and minimize quantum circuit embedding. The results show QGNNs can achieve lower test loss compared to classical models if a similar number of trainable variables are used, and converge faster in training. This paper also provides a review of classical graph neural network models for materials research and various QGNNs.

Variational quantum approximate support vector machine with inference transfer.

A kernel-based quantum classifier is the most practical and influential quantum machine learning technique for the hyper-linear classification of complex data. We propose a Variational Quantum Approximate Support Vector Machine (VQASVM) algorithm that demonstrates empirical sub-quadratic run-time complexity with quantum operations feasible even in NISQ computers. We experimented our algorithm with toy example dataset on cloud-based NISQ machines as a proof of concept. We also numerically investigated its performance on the standard Iris flower and MNIST datasets to confirm the practicality and scalability.

Photon-counting statistics-based support vector machine with multi-mode photon illumination for quantum imaging.

We propose a photon-counting-statistics-based imaging process for quantum imaging where background photon noise can be distinguished and eliminated by photon mode estimation from the multi-mode Bose-Einstein distribution. Photon-counting statistics show multi-mode behavior in a practical, low-cost single-photon-level quantum imaging system with a short coherence time and a long measurement time interval. Different mode numbers in photon-counting probability distributions from single-photon illumination and background photon noise can be classified by a machine learning technique such as a support vector machine (SVM). The proposed photon-counting statistics-based support vector machine (PSSVM) learns the difference in the photon-counting distribution of each pixel to distinguish between photons from the source and the background photon noise to improve the image quality. We demonstrated quantum imaging of a binary-image object with photon illumination from a spontaneous parametric down-conversion (SPDC) source. The experiment results show that the PSSVM applied quantum image improves a peak signal-to-noise ratio (PSNR) gain of 2.89dB and a structural similarity index measure (SSIM) gain of 27.7% compared to the conventional direct single-photon imaging.

Quantum computational study of chloride attack on chloromethane for chemical accuracy and quantum noise effects with UCCSD and k-UpCCGSD ansatzes.

Quantum computing is expected to play an important role in solving the problem of huge computational costs in various applications by utilizing the collective properties of quantum states, including superposition, interference, and entanglement, to perform computations. Quantum mechanical (QM) methods are candidates for various applications and can provide accurate absolute energy calculations in structure-based methods. QM methods are powerful tools for describing reaction pathways and their potential energy surfaces (PES). In this study, we applied quantum computing to describe the PES of the bimolecular nucleophilic substitution (SN2) reaction between chloromethane and chloride ions. We performed noiseless and noise simulations using quantum algorithms and compared the accuracy and noise effects of the ansatzes. In noiseless simulations, the results from UCCSD and k-UpCCGSD are similar to those of full configurational interaction (FCI) with the same active space, which indicates that quantum algorithms can describe the PES of the SN2 reaction. In noise simulations, UCCSD is more susceptible to quantum noise than k-UpCCGSD. Therefore, k-UpCCGSD can serve as an alternative to UCCSD to reduce quantum noisy effects in the noisy intermediate-scale quantum era, and k-UpCCGSD is sufficient to describe the PES of the SN2 reaction in this work. The results showed the applicability of quantum computing to the SN2 reaction pathway and provided valuable information for structure-based molecular simulations with quantum computing.

Circuit-Based Quantum Random Access Memory for Classical Data.

A prerequisite for many quantum information processing tasks to truly surpass classical approaches is an efficient procedure to encode classical data in quantum superposition states. In this work, we present a circuit-based flip-flop quantum random access memory to construct a quantum database of classical information in a systematic and flexible way. For registering or updating classical data consisting of M entries, each represented by n bits, the method requires O(n) qubits and O(Mn) steps. With post-selection at an additional cost, our method can also store continuous data as probability amplitudes. As an example, we present a procedure to convert classical training data for a quantum supervised learning algorithm to a quantum state. Further improvements can be achieved by reducing the number of state preparation queries with the introduction of quantum forking.

Zadoff-Chu sequence-based upstream ranging in OFDMA-PON mobile radio access networks.

Orthogonal frequency division multiple access (OFDMA) uplink in a passive optical network (PON) requires the delay alignment for OFDMA symbols from remotely distributed optical network units (ONUs). In this paper, we experimentally demonstrate and analyze the performance of a Zadoff-Chu (ZC) sequence-based upstream ranging scheme in an intensity modulation/direct detection (IM/DD)-based OFDMA-PON. The experimental results show that the proposed scheme can achieve upstream synchronization with only marginal inter-carrier interference (ICI) and requires no additional bandwidths in a typical OFDMA transmission with cyclic prefix (CP).

Security analysis of quantum key distribution on passive optical networks.

Needs for providing security to end users have brought installation of quantum key distribution (QKD) in one-to-many access networks such as passive optical networks. In the networks, a presence of optical power splitters makes issues for secure key rate more important. However, researches for QKD in access networks have mainly focused on implementation issues rather than protocol development for key rate enhancement. Since secure key rate is theoretically limited by a protocol, researches without protocol development cannot overcome the limit of secure key rate given by a protocol. This brings need of researches for protocol development. In this paper, we provide a new approach which provides secure key rate enhancement over the conventional protocol. Specifically, we propose the secure key rate formula in a passive optical network by extending the secure key rate formula based on the decoy-state BB84 protocol. For a passive optical network, we provide a way that incorporates cooperation across end users. Then, we show that the way can mitigate a photon number splitting (PNS) attack which is crucial in an well known decoy BB84 protocol. Especially, the proposed scheme enables multi-photon states to serve as secure keys unlike the conventional decoy BB84 protocol. Numerical simulations demonstrate that our proposed scheme outperforms the decoy BB84 protocol in secure key rate.

Scalable software-defined optical networking with high-performance routing and wavelength assignment algorithms.

The feasibility of software-defined optical networking (SDON) for a practical application critically depends on scalability of centralized control performance. The paper, highly scalable routing and wavelength assignment (RWA) algorithms are investigated on an OpenFlow-based SDON testbed for proof-of-concept demonstration. Efficient RWA algorithms are proposed to achieve high performance in achieving network capacity with reduced computation cost, which is a significant attribute in a scalable centralized-control SDON. The proposed heuristic RWA algorithms differ in the orders of request processes and in the procedures of routing table updates. Combined in a shortest-path-based routing algorithm, a hottest-request-first processing policy that considers demand intensity and end-to-end distance information offers both the highest throughput of networks and acceptable computation scalability. We further investigate trade-off relationship between network throughput and computation complexity in routing table update procedure by a simulation study.

Theories and applications of chromatic dispersion penalty mitigation in all optical OFDM transmission system.

Fiber chromatic dispersion (CD) in optical OFDM transmission degrades carrier orthogonality, resulting in a system penalty. Such penalty can be mitigated by per-carrier delay precompensation and spectrum filtering. We present a theoretical model to investigate the CD impairment in all-optical OFDM system, and demonstrate experimentally that both methods restore performance without overhead or guard interval.

System tolerance of all-optical sampling OFDM using AWG discrete Fourier transform.

The fundamental-mode arrayed waveguide grating (AWG) for all-optical discrete Fourier transformer (DFT) shows significant feasibility in the system tolerance of all-optical sampling orthogonal frequency division multiplexing (AOS-OFDM) systems. We discuss the system tolerance of AWG-based DFT designs for 100/160Gbps OFDM transmission system in comparison with coupler-based DFT designs.

Add-drop Benes network for scalable optical packet networks.

For the scalable optical packet transport networks, an innovative design of add-drop Benes network (ADBN) is presented where the cost and energy consumption can be considerably reduced by element savings in the architecture. In a WDM optical packet transport switching node, the ADBNs are interconnected to achieve buffer sharing among multiple ADBNs. A corresponding switch configuration algorithm and architecture rules for the single ADBN and shared ADBN are proposed to mitigate the limited connection capability of the proposed ADBN designs. Switch scalability is verified in consideration of a crosstalk noise performance and element counts.

All optical discrete Fourier transform processor for 100 Gbps OFDM transmission.

Optical orthogonal frequency division multiplex (OFDM) symbol generation by all-optical discrete Fourier transform (DFT) is proposed and investigated for 100-Gbps transmission performance. We discuss a design example for a 4x25Gbps OFDM transmission system and its performance comparison with that for a 100-Gbps single-channel return-to-zero data transmission in an optically amplified system.

Optical burst add-drop multiplexing technique for sub-wavelength granularity in wavelength multiplexed ring networks.

We demonstrate optical burst add-drop multiplexing as a practical application of the optical burst switching technology in a wavelength-division-multiplexed ring network. To control optical bursts in the network, a burst identifier (BI) for delivering control information, and a BI processor for handling the BI, were designed. Optical bursts of 10- to 100-mus in length were optically multiplexed or demultiplexed in an intermediate node of the ring network. The demonstration shows that the optical burst add-drop multiplexing technique provides sub-wavelength granularity to a ring network.