Variational quantum eigensolver (VQE) is a promising algorithm for near-term quantum machines. It can be used to estimate the ground state energy of a molecule by performing separate measurements of O(N ⁴ ) terms. This quartic scaling appears to be a significant obstacle to practical applications. However, we note that it empirically reduces to O(N ³ ) when we partition the terms into linear-sized commuting families that can be measured simultaneously. We confirm these empirical observations by studying the MIN-COMMUTING-PARTITION problem at the level of the fermionic Hamiltonian and its encoding into qubits. Moreover, we provide a fast, precomputable procedure for creating linearly sized commuting partitions by solving a round-robin scheduling problem via flow networks. In addition, we demonstrate how to construct the quantum circuits necessary for simultaneous measurement, and we discuss the statistical implication of simultaneous measurement. Our results are experimentally validated by a ground state estimation of deuteron with low shot budget on a 20-qubit IBM machine.

Building a quantum computer that surpasses the computational power of its classical counterpart is a great engineering challenge. Quantum software optimizations can provide an accelerated pathway to the first generation of quantum computing (QC) applications that might save years of engineering effort. Current quantum software stacks follow a layered approach similar to the stack of classical computers, which was designed to manage the complexity. In this review, we point out that greater efficiency of QC systems can be achieved by breaking the abstractions between these layers. We review several works along this line, including two hardware-aware compilation optimizations that break the quantum instruction set architecture (ISA) abstraction and two error-correction/information-processing schemes that break the qubit abstraction. Last, we discuss several possible future directions.

Variational quantum algorithms (VQAs) have demonstrated great potentials in the noisy intermediate scale quantum (NISQ) era. In the workflow of VQA, the parameters of ansatz are iteratively updated to approximate the desired quantum states. We have seen various efforts to draft better ansatz with less gates. Some works consider the physical meaning of the underlying circuits, while others adopt the ideas of neural architecture search (NAS) for ansatz generator. However, these designs do not exploit the full advantages of VQAs. Because most techniques target gate ansatz, and the parameters are usually rotation angles of the gates. In quantum computers, the gate ansatz will eventually be transformed into control signals such as microwave pulses on superconducting qubits. These control pulses need elaborate calibrations to minimize the errors, such as over-rotation and under-rotation. In the case of VQAs, this procedure will introduce redundancy, but the variational properties of VQAs can naturally handle problems of over-rotation and under-rotation by updating the amplitude and frequency parameters. Therefore, we propose NAPA , a native-pulse ansatz generator framework for VQAs. We generate native-pulse ansatz with trainable parameters for amplitudes and frequencies. In our proposed NAPA , we are tuning parametric pulses, which are natively supported on NISQ computers. Given the limited availability of gradient-based optimizers for pulse-level quantum programs, we choose to deploy nongradient optimizers in our framework. To constrain the number of parameters sent to the optimizer, we adopt a progressive way to generate our native-pulse ansatz. Experiments are conducted on both simulators and quantum devices for variational quantum eigensolver (VQE) tasks to envaluate our methods. When adopted on NISQ machines, NAPA obtained improved the performance with decreased latency by an average of 86%. NAPA is able to achieve 96.482% and 99.336% accuracy for VQE tasks on H2 and HeH+, respectively. An average accuracy of 97.27% is achieved for medium-size quantum chemistry tasks on CO2, H2O, and NaH. NAPA also demonstrates advantages on quantum optimization tasks even with considerable noises in NISQ machines.

This paper introduces quantum computing as a necessary and viable tool in addressing the needs of a modernized power grid. The application of quantum computing in enhancing physical security of the grid — an increasingly difficult problem to solve— is investigated. A comparative study based on mathematically proven computing performance measures shows the merits of the proposed method and further unveils the potential benefits of quantum computing in improving grid performance.

We advocate for a fundamentally different way to perform quantum computation by using three-level qutrits instead of qubits. In particular, we substantially reduce the resource requirements of quantum computations by exploiting a third state for temporary variables (ancilla) in quantum circuits. Past work with qutrits has demonstrated only constant factor improvements, owing to the log2(3) binary-to-ternary compression factor. We present a novel technique using qutrits to achieve a logarithmic depth (runtime) decomposition of the Generalized Toffoli gate using no ancilla—an exponential improvement over linear depth for the best qubit-only equivalent. Our approach also features a 70x improvement in total two-qudit gate count over the qubit-only decomposition. This results in improvements for important algorithms like for arithmetic and QRAM. Our simulator accounts for the cost of operating qutrits with realistic near-term noise models. Simulation results for these noise models indicate over 90% mean reliability (fidelity) for our circuit construction, versus under 30% for the qubit-only baseline. These results suggest that qutrits offer a promising path towards extending the frontier of quantum computers.

Contemporary quantum computers encode and process quantum information in binary qubits $(d=2)$. However, many architectures include higher energy levels that are left as unused computational resources. We demonstrate a superconducting ququart $(d=4)$ processor and combine quantum optimal control with efficient gate decompositions to implement high-fidelity ququart gates. We distinguish between viewing the ququart as a generalized four-level qubit and an encoded pair of qubits, and characterize the resulting gates in each case. In randomized benchmarking experiments we observe gate fidelities $\ge 95\%$ and identify coherence as the primary limiting factor. Our results validate ququarts as a viable tool for quantum information processing.