No Arabic abstract
Quantum annealing machines based on superconducting qubits, which have the potential to solve optimization problems faster than digital computers, are of great interest not only to researchers but also to the general public. Here, we propose a quantum annealing machine based on a semiconductor floating gate (FG) array. We use the same device structure as that of the commercial FG NAND flash memory except for small differences such as thinner tunneling barrier. We theoretically derive an Ising Hamiltonian from the FG system in its single-electron region. Recent high-density NAND flash memories are subject to several problems that originate from their small FG cells. In order to store information reliably, the number of electrons in each FG cell should be sufficiently large. However, the number of electrons stored in each FG cell becomes smaller and can be countable. So we utilize the countable electron region to operate single-electron effects of FG cells. Second, in the conventional NAND flash memory, the high density of FG cells induces the problem of cell-to-cell interference through their mutual capacitive couplings. This interference problem is usually solved by various methods using a software of error-correcting codes. We derive the Ising interaction from this natural capacitive coupling. Considering the size of the cell, 10 nm, the operation temperature is expected to be approximately that of a liquid nitrogen. If a commercial 64 Gbit NAND flash memory is used, ideally we expect it to be possible to construct 2 megabytes (MB) entangled qubits by using the conventional fabrication processes in the same factory as is used for manufacture of NAND flash memory. A qubit system of highest density will be obtained as a natural extension of the miniaturization of commonly used memories in this society.
We propose a prime factorizer operated in a framework of quantum annealing (QA). The idea is inverse operation of a multiplier implemented with QA-based Boolean logic circuits. We designed the QA machine on an application-specific-annealing-computing architecture which efficiently increases available hardware budgets at the cost of restricted functionality. The invertible operation of QA logic gates consisting of superconducting flux qubits was confirmed by circuit simulation with classical noise sources. The circuits were implemented and fabricated by using superconducting integrated circuit technologies with Nb/AlOx/Nb Josephson junctions. We also propose a 2.5Dimensional packaging scheme of a qubit-chip/interpose /package-substrate structure for realizing practically large-scale QA systems.
We present a real-world application that uses a quantum computer. Specifically, we train a RBM using QA for cybersecurity applications. The D-Wave 2000Q has been used to implement QA. RBMs are trained on the ISCX data, which is a benchmark dataset for cybersecurity. For comparison, RBMs are also trained using CD. CD is a commonly used method for RBM training. Our analysis of the ISCX data shows that the dataset is imbalanced. We present two different schemes to balance the training dataset before feeding it to a classifier. The first scheme is based on the undersampling of benign instances. The imbalanced training dataset is divided into five sub-datasets that are trained separately. A majority voting is then performed to get the result. Our results show the majority vote increases the classification accuracy up from 90.24% to 95.68%, in the case of CD. For the case of QA, the classification accuracy increases from 74.14% to 80.04%. In the second scheme, a RBM is used to generate synthetic data to balance the training dataset. We show that both QA and CD-trained RBM can be used to generate useful synthetic data. Balanced training data is used to evaluate several classifiers. Among the classifiers investigated, K-Nearest Neighbor (KNN) and Neural Network (NN) perform better than other classifiers. They both show an accuracy of 93%. Our results show a proof-of-concept that a QA-based RBM can be trained on a 64-bit binary dataset. The illustrative example suggests the possibility to migrate many practical classification problems to QA-based techniques. Further, we show that synthetic data generated from a RBM can be used to balance the original dataset.
Quantum annealing is an optimization technique which potentially leverages quantum tunneling to enhance computational performance. Existing quantum annealers use superconducting flux qubits with short coherence times, limited primarily by the use of large persistent currents $I_mathrm{p}$. Here, we examine an alternative approach, using qubits with smaller $I_mathrm{p}$ and longer coherence times. We demonstrate tunable coupling, a basic building block for quantum annealing, between two flux qubits with small ($sim 50~mathrm{nA}$) persistent currents. Furthermore, we characterize qubit coherence as a function of coupler setting and investigate the effect of flux noise in the coupler loop on qubit coherence. Our results provide insight into the available design space for next-generation quantum annealers with improved coherence.
Piezoelectric surface acoustic waves (SAWs) are powerful for investigating and controlling elementary and collective excitations in condensed matter. In semiconductor two-dimensional electron systems SAWs have been used to reveal the spatial and temporal structure of electronic states, produce quantized charge pumping, and transfer quantum information. In contrast to semiconductors, electrons trapped above the surface of superfluid helium form an ultra-high mobility, two-dimensional electron system home to strongly-interacting Coulomb liquid and solid states, which exhibit non-trivial spatial structure and temporal dynamics prime for SAW-based experiments. Here we report on the coupling of electrons on helium to an evanescent piezoelectric SAW. We demonstrate precision acoustoelectric transport of as little as ~0.01% of the electrons, opening the door to future quantized charge pumping experiments. We also show SAWs are a route to investigating the high-frequency dynamical response, and relaxational processes, of collective excitations of the electronic liquid and solid phases of electrons on helium.
Electron spins in silicon quantum dots provide a promising route towards realising the large number of coupled qubits required for a useful quantum processor. At present, the requisite single-shot spin qubit measurements are performed using on-chip charge sensors, capacitively coupled to the quantum dots. However, as the number of qubits is increased, this approach becomes impractical due to the footprint and complexity of the charge sensors, combined with the required proximity to the quantum dots. Alternatively, the spin state can be measured directly by detecting the complex impedance of spin-dependent electron tunnelling between quantum dots. This can be achieved using radio-frequency reflectometry on a single gate electrode defining the quantum dot itself, significantly reducing gate count and architectural complexity, but thus far it has not been possible to achieve single-shot spin readout using this technique. Here, we detect single electron tunnelling in a double quantum dot and demonstrate that gate-based sensing can be used to read out the electron spin state in a single shot, with an average readout fidelity of 73%. The result demonstrates a key step towards the readout of many spin qubits in parallel, using a compact gate design that will be needed for a large-scale semiconductor quantum processor.