A recent advancement in quantum computing shows a quantum advantage of certified randomness on the racetrack processor. This work investigates the execution efficiency of this architecture for general-purpose programs. We first explore the impact of increasing zones on runtime efficiency. Counterintuitively, our evaluations using variational programs reveal that expanding zones may degrade runtime performance under the existing scheduling policy. This degradation may be attributed to the increase in track length, which increases ion circulation overhead, offsetting the benefits of enhanced parallelism. To mitigate this, the proposed Plutarch exploits 3 strategies: (i) unitary decomposition and translation to maximize zone utilization, (ii) prioritizing the execution of nearby gates over ion circulation, and (iii) implementing shortcuts to provide the alternative path.

A zoned neutral atom architecture achieves exceptional fidelity by segregating the execution spaces of 1- and 2-qubit gates, being a promising candidate for high-accuracy quantum systems. Unfortunately, na'ively applying programs designed for static qubit topologies to zoned architectures may result in most execution time being consumed by intra-zone travels of atoms. To address this, we introduce Mantra (Minimizing trAp movemeNts for aTom aRray Architectures), which rewrites quantum programs to reduce the interleaving of single- and two-qubit gates. Mantra incorporates three strategies: (i) a fountain-shaped controlled-Z (CZ) chain, (ii) ZZ-interaction protocol without a 1-qubit gate, and (iii) preemptive gate scheduling. Mantra reduces inter-zone movements by 68%, physical gate counts by 35%, and improves circuit fidelities by 17% compared to the standard executions.

This paper introduces Genesis, the first compiler designed to support Hamiltonian Simulation on hybrid continuous-variable (CV) and discrete-variable (DV) quantum computing systems.Genesis is a two-level compilation system.At the first level, it decomposes an input Hamiltonian into basis gates using the native instruction set of the target hybrid CV-DV quantum computer.At the second level, it tackles the mapping and routing of qumodes/qubits to implement long-range interactions for the gates decomposed from the first level.Rather than a typical implementation that relies on SWAP primitives similar to qubit-based (or DV-only) systems, we propose an integrated design of connectivity-aware gate synthesis and beamsplitter SWAP insertion tailored for hybrid CV-DV systems.We also introduce an OpenQASM-like domain-specific language (DSL) named CVDV-QASM to represent Hamiltonian in terms of Pauli-exponentials and basic gate sequences from the hybrid CV-DV gate set.Genesis has successfully compiled several important Hamiltonians, including the Bose-Hubbard model, Z 2 -Higgs model, Hubbard-Holstein model, Heisenberg model and Electron-vibration coupling Hamiltonians, which are critical in domains like quantum field theory, condensed matter physics, and quantum chemistry.Our implementation is available at Genesis-CVDV-Compiler https://

Quantum computing has shown promise in solving complex problems by leveraging the principles of superposition and entanglement. Variational quantum algorithms (VQA) are a class of algorithms suited for near-term quantum computers due to their modest requirements of qubits and depths of computation. This paper introduces Tetris – a compilation framework for VQA applications on near-term quantum devices. Tetris focuses on reducing two-qubit gates in the compilation process since a two-qubit gate has an order of magnitude more significant error and execution time than a single-qubit gate. Tetris exploits unique opportunities in the circuit synthesis stage often overlooked by the state-of-the-art VQA compilers for reducing the number of two-qubit gates. Tetris comes with a refined IR of Pauli string to express such a two-qubit gate optimization opportunity. Moreover, Tetris is equipped with a fast bridging approach that mitigates the hardware mapping cost. Overall, Tetris demonstrates a reduction of up to $41.3 \%$ in CNOT gate counts, $37.9 \%$ in circuit depth, and $\mathbf{4 2. 6 \%}$ in circuit duration for various molecules of different sizes and structures compared with the state-of-the-art approaches. Tetris is open-sourced at this link.

Due to the unreliability and limited capacity of existing quantum computer prototypes, quantum circuit simulation continues to be a vital tool for validating next generation quantum computers and for studying variational quantum algorithms, which are among the leading candidates for useful quantum computation. Existing quantum circuit simulators do not address the common traits of variational algorithms, namely: 1) their ability to work with noisy qubits and operations, 2) their repeated execution of the same circuits but with different parameters, and 3) the fact that they sample from circuit final wavefunctions to drive a classical optimization routine. We present a quantum circuit simulation toolchain based on logical abstractions targeted for simulating variational algorithms. Our proposed toolchain encodes quantum amplitudes and noise probabilities in a probabilistic graphical model, and it compiles the circuits to logical formulas that support efficient repeated simulation of and sampling from quantum circuits for different parameters. Compared to state-of-the-art state vector and density matrix quantum circuit simulators, our simulation approach offers greater performance when sampling from noisy circuits with at least eight to 20 qubits and with around 12 operations on each qubit, making the approach ideal for simulating near-term variational quantum algorithms. And for simulating noise-free shallow quantum circuits with 32 qubits, our simulation approach offers a 66× reduction in sampling cost versus quantum circuit simulation techniques based on tensor network contraction.

In support of the growing interest in quantum computing experimentation, programmers need new tools to write quantum algorithms as program code. Compared to debugging classical programs, debugging quantum programs is difficult because programmers have limited ability to probe the internal states of quantum programs; those states are difficult to interpret even when observations exist; and programmers do not yet have guidelines for what to check for when building quantum programs. In this work, we present quantum program assertions based on statistical tests on classical observations. These allow programmers to decide if a quantum program state matches its expected value in one of classical, superposition, or entangled types of states. We extend an existing quantum programming language with the ability to specify quantum assertions, which our tool then checks in a quantum program simulator. We use these assertions to debug three benchmark quantum programs in factoring, search, and chemistry. We share what types of bugs are possible, and lay out a strategy for using quantum programming patterns to place assertions and prevent bugs.

We tackle the important problem class of solving nonlinear partial differential equations. While nonlinear PDEs are typically solved in high-performance supercomputers, they are increasingly used in graphics and embedded systems, where efficiency is important.

We present RoboBench, a novel platform for sharing robot full-system simulations for benchmarking. The creation of this platform and benchmark suite is motivated by a need for reproducible research. A challenge in creating a full-system benchmarks are incompatibilities in software created by different groups and the difficulty of reproducing software environments. We solve this problem by using software containers, an emerging virtualization technology. RoboBench enables sharing robot software in a runnable state, capturing the software behavior of robots carrying out missions. These simulations make clear the performance impact and resource usage of programs and algorithms relative to other software involved in the mission. These containers are integrated with the CITK platform for reproducible research, which automates generation and publishing of the containers. We present an overview of the system, a description of our prototype set of benchmark missions, along with a validation study comparing the computational load profile of a mission performed on a real and simulated robot. Additionally, we present preliminary results of an overall analysis of the benchmarks in the RoboBench suite, showing where computational work is expended in robotics common robotics tasks. RoboBench is extensible, and is the first step toward a robust, quantitative approach to engineering computationally-efficient robots.

Due to the end of supply voltage scaling and the increasing percentage of dark silicon in modern integrated circuits, researchers are looking for new scalable ways to get useful computation from existing silicon technology. In this paper we present a reconfigurable analog accelerator for solving systems of linear equations. Commonly perceived downsides of analog computing, such as low precision and accuracy, limited problem sizes, and difficulty in programming are all compensated for using methods we discuss. Based on a prototyped analog accelerator chip we compare the performance and energy consumption of the analog solver against an efficient digital algorithm running on a CPU, and find that the analog accelerator approach may be an order of magnitude faster and provide one third energy savings, depending on the accelerator design. Due to the speed and efficiency of linear algebra algorithms running on digital computers, an analog accelerator that matches digital performance needs a large silicon footprint. Finally, we conclude that problem classes outside of systems of linear equations may hold more promise for analog acceleration.